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Semantic Web/The Vision

The Vision of the Semantic Web
"The Semantic Web is an extension of the current web in which information is given well-defined meaning, better enabling computers and people to work in cooperation." - Tim Berners-Lee, James Hendler, Ora Lassila (2001)

According to the World Wide Web Consortium (W3C), the Web can reach its full potential only if it becomes a place where data can be shared, processed, and understood by automated tools as well as by people. For the Web to scale, tomorrow's programs must be able to share, process, and understand data even when these programs have been designed independently from one another.

Still in its definition stage, the term Semantic Web is perhaps new to many people, even to those within IT circles. But the problems it aims to address are ones we have been struggling to solve for decades – issues such as information overload, stovepipe systems, and poor content aggregation (Daconta, Orbst, and Smith, 2003). The fundamental roots of these problems are the lack of semantic definitions in individual systems, the lack of semantic integration among data sets, and the lack of semantic interoperability across disparate systems. The Semantic Web extends beyond the capabilities of the current Web and existing information technologies, enabling more effective collaborations and smarter decision-making. It is an aggregation of intelligent websites and data stores accessible by an array of semantic technologies, conceptual frameworks, and well-understood contracts of interaction to allow machines to do more of the work to respond to service requests – whether that be taking on rote search processes, providing better information relevance and confidence, or performing intelligent reasoning or brokering.

The steps to reach this state, however, are not likely to be accomplished in a few short years. Certainly, rapid progress will be made on some ends, just as numerous websites appeared soon after the introduction of low-cost/no-cost web servers and free graphical browsers. But the progression in the development of websites moved relatively chaotically over the course of a half-dozen years – starting from an ad hoc set of scripting languages, low-end tools, and custom-built server components, and steadily progressing to a relatively unified set of core languages, application servers, content management systems, e-commerce engines, web services, and other enterprise-worthy components and offerings. The growth of the Semantic Web is likely to go through a similar progression in market dynamics. Although the business models of a connected world are better understood and the level of awareness of emerging web technologies more greatly heightened, there will nevertheless be a significant time lag until many of the pieces of the vision are assembled.

What the Semantic Web Is and Is Not
1.	The Semantic Web is not a new and distinct set of websites.

The Semantic Web is an extension of the current World Wide Web, not a separate set of new and distinct websites. It builds on the current World Wide Web constructs and topology, but adds further capabilities by defining machine-processable data and relationship standards along with richer semantic associations. Existing sites may use these constructs to describe information within web pages in ways more readily accessible by outside processes such as search engines, spider searching technology, and parsing scripts. Additionally, new data stores, including many databases, can be exposed and made available to machine processing that can do the heavy lifting to federate queries and consolidate results across multiple forms of syntax, structure, and semantics. The protocols underlying the Semantic Web are meant to be transparent to existing technologies that support the current World Wide Web.

2.	The Semantic Web is not being constructed with just human accessibility in mind.

The current Web relies mainly on text markup and data link protocols for structuring and interconnecting information at a very coarse level. The protocols are used primarily to describe and link documents in the forms presentable for human consumption (but that have useful hooks for first-order machine searching and aggregation). Semantic Web protocols define and connect information at a much more refined level. Meanings are expressed in formats understood and processed more easily by machines in ways that can bridge structural and semantic differences within data stores. This abstraction and increased accessibility means that current web capabilities can be augmented and extended – and new, powerful ones introduced.

3.	The Semantic Web is not built upon radical untested information theories.

The emergence of the Semantic Web is a natural progression in accredited information theories, borrowing concepts from the knowledge representation and knowledge management worlds as well as from revised thinking within the World Wide Web community. The newly approved protocols have lineages that go back many years and embody the ideas of a great number of skilled practitioners in computer languages, information theory, database management, model-based design approaches, and logics. These concepts have been proven within a number of real-world situations although the unifying set of standards from the W3C promises to accelerate and broaden adoption within the enterprise and on the Web. With respect to issues about knowledge representation and its yet-to-be-fulfilled promise, a look at history shows numerous examples of a unifying standard providing critical momentum for acceptance of a concept. HTML was derived from SGML, an only mildly popular text markup language, and yet HTML went on to cause a sea change in the use of information technology. Many in the field point to the long acceptance timeframes for both object-oriented programming and conceptual-to-physical programming models. According to Ralph Hodgson, “knowledge representation is a fundamental discipline that now has an infrastructure and a set of supporting standards to move it out of the labs and into real-world use.”

4. The Semantic Web is not a drastic departure from current data modeling concepts.

According to Tim Berners-Lee, the Semantic Web data model is analogous to the relational database model. “A relational database consists of tables, which consist of rows, or records. Each record consists of a set of fields. The record is nothing but the content of its fields, just as an RDF node is nothing but the connections: the property values. The mapping is very direct – a record is an RDF node; the field (column) name is RDF propertyType; and the record field (table cell) is a value. Indeed, one of the main driving forces for the Semantic Web has always been the expression, on the Web, of the vast amount of relational database information in a way that can be processed by machines." (Berners-Lees, 1998) That said, the Semantic Web is a much more expressive, comprehensive, and powerful form of data modeling. It builds on traditional data modeling techniques – be they entity-relation modeling or another form – and transforms them into much more powerful ways for expressing rich relationships in a more thoroughly understandable manner.

5.	The Semantic Web is not some magical piece of artificial intelligence

The concept of machine-understandable documents does not imply some form of magical artificial intelligence that allows machines to comprehend human mumblings. It only indicates a machine's ability to solve a well-defined problem by performing well-defined operations on existing well-defined data (Berners-Lee, Handler, and Lassila, 2001). Current search engines perform capabilities that would have been magical 20 years ago, but that we recognize now as being the result of IP protocols, HTML, the concept of websites, web pages, links, graphical browsers, innovative search and ranking algorithms, and, of course, a large number of incredibly fast servers and equally large and fast disk storage arrays. Semantic Web capabilities will likewise be the result of a logical series of interconnected progressions in information technology and knowledge representation formed around a common base of standards and approaches.

6.	The Semantic Web is not an existing entity, ready for users to make use of it.

The Semantic Web currently exists as a vision, albeit a promising and captivating one. Similar to the current Web, the Semantic Web will be formed through a combination of open standard and proprietary protocols, frameworks, technologies, and services. The W3C-approved standards – XML, RDF, and OWL – form the base protocols. New data schemas and contract mechanisms, built using these new protocols, will arise around communities of interest, industry, and intent; some will be designed carefully by experienced data architects and formally recognized by established standards bodies; others will appear from out of nowhere and gain widespread acceptance overnight. A host of new technologies and services will appear such as semantically-aware content publishing tools; context modeling tools; mediation, inference, and reputing engines; data-cleansing and thesaurus services; and new authentication and verification components. Although various elements of the vision already exist, rollout of these technologies, coordination amidst competitive forces, and fulfillment of the vision will take many years.

Benefits
While the full vision of the Semantic Web may be a bit distant, there are, on the near horizon, capabilities that many think will make enterprise software more connectable, interoperable, and adaptable as well as significantly cheaper to maintain. The use of semantic approaches in combination with the existing and emerging semantics-based schemas and tools can bring immediate and/or near-term benefits to many corporate enterprise and government agency IT initiatives.

Semantic interoperability, for example, represents a more limited or constrained subset of the vision of the Semantic Web. Significant returns, however, can still be gained by using semantic-based tools to arbitrate and mediate the structures, meanings, and contexts within relatively confined and well-understood domains for specific goals related to information sharing and information interoperability. In other words, semantic interoperability addresses a more discrete problem set with more clearly defined endpoints (Pollock and Hodgson, 2004). Semantic technologies can also provide a loosely connected overlay on top of existing Web service and XML frameworks, which in turn can offer greater adaptive capabilities than those currently available. They can also make immediate inroads in helping with service discovery and reconciliation, as well as negotiation of requests and responses across different vocabularies. Considering the depth and difficulty of issues the federal, state, and local agencies have in these regards, semantic technologies may provide the first flexible, open, and comprehensive means to solve them.

Semantic computing is an emerging discipline being formed and shaped as this is written. As such, there are many definitions and interpretations, and even a few low-intensity philosophical wars being waged among thought-leaders and practitioners. That said, the release of RDF and OWL as W3C Recommendations earlier in the year has created a greater commonality in expression.

Because semantic computing makes use of various forms of abstraction and logical expression, it can be difficult to see how the languages provide many of the powerful capabilities expressed in earlier sections. But just as the Internet and World Wide Web are built upon layers of protocols and technologies, so too is the Semantic Web. Understanding several key concepts and becoming familiar with the core building blocks of the Semantic Web will form a basis for visualizing how higher order tools, components, and technologies can deliver on the promise of richer and more flexible machine-processable data. Understanding some of the foundational concepts will also allow readers to better understand the state of the technologies and the areas that still need to be refined in order to reach the full vision of the Semantic Web.

Semantic technologies differ from database schemas, data dictionaries, and controlled vocabularies in an important way: They have been designed with the connectivity in mind allowing different conceptual domains to work together as a network.

Languages
1. XML (eXtensible Markup Language)

XML stands for eXtensible Markup Language and is a standard way of describing, transporting, and exchanging data that was pioneered by the W3C in the late 1990s. XML serves as a mechanism for marking up data through the use of customized "tags" in such a way as to describe the data. XML is not necessarily related to HTML and, in fact, the two were designed for entirely different purposes. Despite this fact, the two can complement each other in various ways, depending on a user's needs.

The tags are typically the labels for the data such as “FirstName” or “StreetAddress.” When trying to use XML to define a standard interchange format, it is important to have agreement on the tags. For example, two book suppliers might wish to formalize a partnership involving data exchange. Specifying at the outset that Supplier A’s definition of “Author” is identical to Supplier B’s definition of “Writer” and codifying that in the XML structure would be an essential part of formulating proper data agreement. Additional terms that overlap and have the same meaning would also need to be formally identified, usually in something called a DTD or XML Schema. (XML Schema is a mechanism for defining XML documents in a formal way, thereby ensuring the accurate exchange of information.)

Examples of XML Schemas in working use can be found in many government and industry registries. According to the U.S. CIO Council XML Working Group, “The full benefits of XML will be achieved only if organizations use the same data element definitions and those definitions are available for partners to discover and retrieve. A registry/repository is a means to discover and retrieve documents, templates, and software (i.e., objects and resources) over the Internet. The registry is used to discover the object. It provides information about the object, including its location. A repository is where the object resides for retrieval by users.”

In the context of semantics and the Semantic Web effort, XML is a set of syntax rules for creating semantically rich markup languages in particular domains. XML allows users to add arbitrary structure to their documents but says nothing about what the structures mean (Berners-Lee, Hendler, and Lassila, 2001). In other words, whereas IT systems, databases, and content management systems have become good at describing things, they have not done so well at describing associations. More concrete and faithful descriptions that provide better senses of words, terms, and domains are needed.

2. RDF (Resource Description Framework)

RDF stands for Resource Description Framework and has been specifically designed to provide this associative information. RDF offers ways to make data richer and more flexible, and therefore able to exist in environments outside those explicitly defined by system programmers and data modelers. RDF encodes information in sets of triples, each triple being rather like the subject, verb, and object of an elementary sentence. (This same model can also represent resource, property, and value constructs.) RDF provides an infrastructure for linking distributed metadata and also serves in conjunction with OWL as a core language for describing and representing ontologies.

One of the primary benefits of using RDF to describe data associations is the scalability and flexibility it provides. Explicit database tables can be created that do much the same thing but the unique nature of RDF provides a flexible mechanism that allows far greater associative capabilities, thereby increasing the ability to query and make inferences on topic matters not explicitly hard-wired into tables. The benefits only increase when trying to integrate new data sources, especially when they have different structures or semantics or, more importantly, when they cross conceptual domains (as in the case of environmental and public health data or, alternatively, law enforcement and intelligence data). RDF triples are serialized in XML, providing a way to describe relationships between data elements using XML tags or other syntax in a format that can be easily processed by machines. In an effort to support a loosely coupled and/or virtual architecture, a Universal Resource Identifier (URI) is used to identify each of the triple elements. The purpose of a URI is to uniquely identify a concept in the form of subject, verb, or object by linking to the origin where the concept is defined.

RDF Schema (sometimes written as RDFS or RDF-S) offers a way of semantically describing and extending RDF. It provides mechanisms for describing groups of related resources and the relationships among these resources. RDF Schema does the same thing for RDF that DTD and XML Schema do for XML. A number of query languages for RDF have been developed within academic and industry circles. In October 2004, the W3C RDF Data Access Working Group released a draft specification for SPARQL (pronounced "sparkle"), a query language for RDF that seeks to unify the way developers and end users write, and to consume RDF search results across a wide range of information.

3. OWL (Web Ontology Language)

OWL stands for Web Ontology Language. (The acronym is purposely transposed from the actual name – OWL instead of WOL – as a conscious link to the name of the owl in the book Winnie the Pooh.) Whereas RDF's primary value can be seen in enabling association and integration of distributed data, OWL's main value is in enabling reasoning over distributed data.

OWL is a highly expressive modeling language that is compatible with existing data stores and modeling constructs including XML, Rational, and object-oriented approaches. OWL also provides loosely-coupled “views” of data which makes federated knowledge bases easy to build and evolve. Most importantly, OWL has machine-actionable semantics. Run-time and design-time software tools can do “things” with models, data, metadata, rules, and logic without human assistance or highly specific application code. (Pollock, 2004)

OWL is derived from a number of efforts to develop a set of flexible and computational logic constructs, many of which go back many years. It is the next generation of the ontology language called DAML+OIL, which in turn integrated two efforts, DAML, the DARPA Markup Language, an effort that was based in the United States, and OIL, the Ontology Inference Layer (or Language), an effort that was based in Europe. It also has roots in SHOE (Simple HTML Ontology Extensions), an effort led by James Hendler at the University of Maryland, created specifically for incorporating machine-readable knowledge into web documents thereby facilitating intelligent agent capabilities. There are three levels of OWL defined (OWL Lite, OWL DL, and OWL Full) each having progressively more expressiveness and inferencing power. These levels were created to make it easier for tool vendors to support a specified level of OWL.RDF and OWL can operate together or separately. In some cases, supporting the distributed nature of data may be the primary objective, in which case only RDF may be used. In other cases, distribution plus reasoning capabilities may be desired, and so both RDF and OWL may be featured. In other instances, just reasoning capabilities are desired, and so OWL may suffice.

4. Other Language Development Efforts

Other languages are currently being developed to address additional layers within the Semantic Web vision. For example, a Rule language will provide the capability to express certain logical relationships in a form suitable for machine processing. This language will allow the expression of business rules and will provide greater reasoning and inference capabilities. RuleML was initially proffered as a rule language, although efforts to formalize the Semantic Web Rules Language (SWRL) are currently underway at W3C. A Logic language will conceivably provide a universal mechanism for expressing monotonic logic and validating proofs. A long-term hope is to eventually make use of assertions from around the Web to derive new knowledge. (The problem here is that deduction systems are not terribly interoperable. Rather than designing a single overarching reasoning system, current activities are focused on specifying a universal language for representing proofs. Systems can then digitally sign and export these proofs for other systems to use and incorporate.)

Likewise, constructs, schemas, and architectures for inferring reputation and trust are also being developed, both within the W3C and by the larger web community. These approaches are being looked at not just to infer reputation and trust by and among individuals, but also of groups of people (such as companies, media sources, non-government organizations, and political movements), inanimate objects (such as books, movies, music, academic papers, and consumer products), and even ideas (such as belief systems, political ideas, and policy proposals). (Masum and Zhang, 2004) One challenge faced by practitioners in the field is to create frameworks and languages with sufficient expressiveness to capture the knowledge that can be described in ambiguous human languages. At issue is how to create languages, tools, and systems that will support the easy expression of simple things, while making it possible to express complex things. Another challenge is to maintain compatibility with existing syntax standards such as HTML, XML, and RDF while dealing with issues pertaining to the readability and human accessibility of the syntax. Ultimately, better tools will be developed that will minimize these issues but in the meantime complexities within some of the higher order languages may make it more difficult to develop fully compliant implementations using current editing and modeling tools.

Semantic Tools and Components
'"Semantic Web tools are getting better every day. New companies are starting to form. Big companies are starting to move."'

-James Hendler

Several models exist that describe the lifecycle or stages of maturity that technologies go through. Typically these have four stages: entry (or definition), growth (or validation), maturity (or refinement), and decline (or consolidation). By most measures, the Semantic Web, as experienced in a publicly available format, is still in the entry/definition phase. Many of the semantic technologies, however, are well into the growth/validation phase. (The shift into maturity is often elusive; the tipping point being visible only after the fact, and at times passing through a period of hype and unmet expectations.)

Leaders in technology applications across government and private industry have been forging new paths and obtaining successful results from their semantic implementation projects. There are semantic research projects in a number of federal agencies. Semantic products are available from large and established companies such as Adobe, Hewlett Packard, and IBM, as well as from many small pioneering companies such as Unicorn, Network Inference, and Semagix. In addition, there are a number of open source and publicly available tools created by public and private research institutions and organizations.

What follows is a brief survey of commercial and open source tools that can be used to create applications powered by semantic technologies. One way to understand how these tools work together is to view them as either design-time tools or run-time tools. Design-time tools are used by document authors, system designers, and others as part of the creation, design, or authoring process. Examples include tools to create metadata or to create or populate ontologies. Other software components are used as run-time components to process queries, transform data, or otherwise produce operational results. Examples include mediation servers and inference engines. Many of the tools are used as a set within an implementation process – for example, modeling and mapping tools during design-time in partnership with query facilities and mediation servers at run-time.

Metadata Publishing and Management Tools

The process for creating metadata about a document or data item can occur when that item is created or authored, when it is imported into a content management system or a website, or when it is viewed or read by users. It can also be added by some other explicit or implicit action at any point over the course of the existence of that data item. In other words, metadata creation is not just a one-time occurrence. Metadata can continue to accumulate and can be modified at any time by conceivably any number of people.

At content creation, authors typically connect information such as the subject, creator, location, language, and copyright status with a particular document. This information makes the document much more searchable. RSS consists essentially of this type of information, providing newsreading applications with significantly expanded capabilities for searching and filtering information. Moveable Type from a company called SixApart is one of the more popular tools in the blogging community for creating RSS-compliant documents. The increasing popularity and simplicity of RSS is causing its use to extend outside of the blogging community into the general media and even into the enterprise. Other vendors of desktop and web-authoring tools are also moving quickly to provide RSS publishing capabilities.

The creation of metadata is only one step in the process. Metadata management tools are needed in order to maintain metadata vocabularies, perform metadata-driven queries, and provide visualization tools for monitoring changes in areas of interest. An example of a website that uses metadata as a key aspect of creating a collaborative and shared system of data is Flickr, a site for people to easily upload and share digital photos. What sets it apart from other digital photos services is that it provides photo-tagging capabilities as well as an innovative interface for viewing the categories of photos. (The tags are contained in a map and vary in size depending on the frequency of the tag within the data store.) What distinguishes it from earlier metadata implementations is that the feedback loop is extremely tight, meaning that the assignment of tags is bound closely to their use. As soon as photos and sets of photos are tagged, users see clusters of items carrying the same tag. Users can easily change tags to refine the clusters. In terms of tools for querying metadata, the components are not much different than current search engines, although the inclusion of metadata makes for richer data and therefore more precise and relevant searches. Query scripts and languages will likely adapt to allow users more precision although the balance between simplicity and features is constantly in flux, especially in more publicly available search engines. As with the Flickr example above, however, new visualization tools are being developed to help users navigate through complex fields of related data.

Modeling Tools (Ontology creation and modification)

Modeling tools are used to create and modify ontologies. Knowledge modelers used them to create and edit class structures and model domains. The tools often have an interface that is similar to a file system directory structure or bookmark folder interface. They also tend to offer the ability to import, transform, and re-purpose, in whole or in part, existing ontological structures that are often in the form of database schemas, product catalogues, and yellow pages listings. Other prominent feature includes advanced mechanisms for organizing, matching, and associating similar terms and concepts.

Also, because it is a common practice for modelers to create smaller interconnected ontologies instead of a single large monolithic model – primarily for better reusability and ease of use – support for splitting, merging, and connecting models can be an important capability in the ontology editor. Some editors even support collaborative work methods and rich visualization and graphical interaction modes. Protégé-2000 is a free ontology editor from Stanford University with a large and active user community. It features an open architecture that allows independent developers to write plug-ins that can significantly extend Protégé capabilities. Commercial modeling tools are available from a number of vendors including Network Inference, Language and Computing, and Intelligent Views. IBM’s Ontology Management System (also known as SNOBASE, for Semantic Network Ontology Base) is a framework for loading ontologies from files and via the Internet and for locally creating, modifying, querying, and storing ontologies. Internally, SNOBASE uses an inference engine, an ontology persistent store, an ontology directory, and ontology source connectors. Applications can query against the created ontology models and the inference engine deduces the answers and returns results sets similar to JDBC (Java Data Base Connectivity) result sets. As of the time of publication of this paper, SNOBASE is not, however, compatible with OWL. The Sigma ontology development and reasoning system is also a fully formed design and run-time ontology management system. It can be freely licensed although it, like SNOBASE, is not compliant with OWL.

Ontologies

Arriving at the right ontology is often a critical element of successful implementation of semantics-based projects. Even more so than database design, ontology creation is a highly specialized field. Not only are there not as yet a sizeable number of skilled practitioners, it can take considerable time to arrive at an ontology that successfully captures a conceptual domain. As a result, it is important to look at existing bodies of work that can be used (and reused) in lieu of having to create something from scratch. Likely sources of existing ontologies can typically be located in close association with ontology modeling tools, several of which are named above. Use of proprietary ontologies may be contingent upon licensing of the modeling tools, a practice which is not unreasonable considering the efforts expended to develop the ontologies. Other ontologies, however, may be open and free for use for commercial and non-commercial purposes, much in the vein of Linux, JBoss, Wikipedia, Musicbrainz, and other open source software and data repositories.

Current ontology development efforts vary in scope and size. Some ontologies have been developed specifically in answer to localized implementations such as reconciling charts of accounts or health care records, areas where the emphasis is primarily on information interoperability – arbitrating between syntaxes, structures, and semantics – and less on logic programming. Other ontology development efforts take a more top-down approach under the assumption that a shared view of a wide knowledge domain is critical to widespread proliferation of adaptive computing and intelligent reasoning capabilities. There is significant advocacy in these latter circles on the establishment of an enterprise-wide common upper ontology under the belief that it will provide the foundation for any number of domain ontologies. New domain ontologies could be extensions of, and fully compliant with, this upper ontology. Existing ontologies and legacy data models could be mapped to this upper ontology, which theoretically would constitute a significant number of the steps toward achieving greater semantic interoperability across domains. (It should be noted, however, that additional development and engineering is still needed to demonstrate the feasibility and scalability of this approach.)

Several candidate upper ontologies now exist, including DOLCE (Gangemi, et al., 2002), Upper Cyc (Lenat, 1995), and SUMO (Niles and Pease, 2001), but none of these as yet has gained significant market adoption. Proponents of this upper ontology approach believe that were the U.S. Department of Defense and/or the Federal Government to adopt one of these candidates, there is a good chance industry would follow, after which the US could then propose it as a standard to the International Standards Organization.

Even where domain-specific ontologies do not exist, it is possible to jumpstart development by making use of existing taxonomies, XML standards, or other lower order data models. At the federal level, the Knowledge Management working group (http://km.gov) has made significant progress in sharing information about taxonomy projects across agencies. XML.Gov (http://xml.gov) has a mission to facilitate efficient and effective use of XML across agencies in order that seamless sharing of documents and data can be achieved. Many government agencies have existing taxonomies, or have begun to develop taxonomies for their information domains. JusticeXML, for example, is an impressive body of work that could be extended and enhanced by RDF and OWL to provide a more flexible data model, an effort that could pave the way for far easier access to federal, state, and local law enforcement information by other agencies.

Mapping Tools (Ontology population)

Once an ontology model is created, it needs to be populated with data (referred to as class instances in “ontology speak”). This process is usually accomplished by linking various data sources to the concepts in an ontology using a mapping tool. Once “maps” have been created, a query in one data source could be transformed by its map to the ontology and then from the ontology to the other data sources using their maps. The corresponding data could then be returned in the same manner without any of the data stores knowing or caring about the others. In other words, each data source may have a unique “map” to an overarching ontology that acts as a pivot table among the various sources and targets. Providing this abstraction layer requires some effort on the part of creating the ontology and then creating the data maps, but once this has been done each data source can interoperate with other data sources strictly within run-time processes. Bringing new data sources onboard will, in most cases, have little or no effect on existing data sources.

This process drastically reduces the amount of data value mapping and semantic conflict resolution that typically takes place using current enterprise application approaches – approaches that up to now typically require n-squared mappings (mapping from and to each and every data source) or alternatively, exporting to hard-coded, inflexible, and explicit standards. The modeling and mapping process makes the process far less political and far more flexible and adaptable. Anomalies specific to a single data source, for example, can be handled almost transparently, whereas addressing such anomalies within the typical standards process would entail expending significant time and energy. Most of the tools used to handle structured data forms have features that automate the process of mapping database fields to ontologies. Network Inference and Unicorn are two vendors with tools of this type. Tools that aggregate, normalize, and map unstructured data forms to ontologies typically work with a variety of unstructured data forms including Word, RTF, text files, and HTML. Semagix is a leading vendor for unstructured data.

Data Stores

Ontologies and other RDF data models can be stored in native RDF data stores or in relational databases that have been customized to support associative data techniques. Native RDF-data stores are inherently designed to support the concept of triples and can offer an efficient out-of-the-box approach to storing ontologies. RDF native databases are available from companies such as Tucana Technologies and Intellidimensions. Several high-quality open source RDF data stores also exist, including Kowari, Redland, Sesame, and 3Store. To use a relational database, the database must be designed in a somewhat non-traditional way. Instead of having a table that describes each major concept, the database design typically mimics the concept of triples by using a single table containing four columns. Three of the columns store the triple while the forth column is used to store its identification tag. (A report entitled “Mapping Semantic Web Data with RDBMSes” is an excellent resource for finding out more about implementing triple stores in relational databases.)

Issues related to representing, storing, and querying using triples (i.e., RDF) versus traditional relational approaches, as well as the use and/or co-existence of the two types of data stores within implementations, are still working themselves out within industry and the marketplace. Each store-and-query facility provides unique capabilities that the other, at present, does not. RDF is great for situations when it is difficult to anticipate the types of queries that will be performed in the future. It is also terrific for handling metadata and for making queries that require inferences across imprecise or disparate data. For example, a query along the lines of, “How many energy producers qualify for ‘green’ status this year?” is much easier to perform using an RDF query language than in SQL (once the models have been created to tie together various data stores). At the same time, queries that are trivial in SQL, such as, “Which energy producers reduced their CO2 output the most this year?” can be quite complicated using an RDF query language.

It is important to note that RDF query languages are still evolving, which may to some extent explain this limitation. Other limitations of RDF relate to performance issues. Because queries can be broadened, for example, to include concepts instead of just terms, the search space can be dramatically increased. Because RDF data stores are relatively new and the number of implementations relatively small, system developers need to iterate over their designs, paying particular attention to queries and functions that could have negative effects on performance. In terms of industry growth, it is difficult to predict how RDF will affect the database industry. RDF data stores may remain a distinct data storage category in their own right or their capabilities may be subsumed into relational databases in a manner similar to what happened with object-oriented databases.

Mediation Engines

Mediation engines are automated tools that can dynamically transform data among different syntaxes, structures, and semantics using models instead of hard-wired transformation code. They are critical components of any interoperability architecture. Using data maps, ontologies, and other forms of conceptual models, mediation engines are run-time processes that provide an abstraction layer between heterogeneous data sets, allowing organizations to essentially agree to disagree about how data and information should be represented. Mediation engines typically work with highly structured data. Unstructured and semi-structured data must first be bound to a schema prior to creating the mediation maps (Pollock, 2004).

Inference Engines

Inference engines (sometimes referred to as reasoners) are software tools that derive new facts or associations from existing information. It is often said that an inference engine emulates the human capability to arrive at a conclusion by reasoning. In reality, inferencing is not some mythical artificial intelligence capability but, rather, a quite common approach in data processing. One can think of a complex data mining exercise as a form of inferencing. By creating a model of the information and relationships, we enable reasoners to draw logical conclusions based on the model. A common example of an inference is to use models of people and their connections to other people to gain new knowledge. Exploration of these network graphs can enable inferences about relationships that may not have been explicitly defined. Note that with just RDF and OWL, inferences are limited to the associations represented in the models, which primarily means inferring transitive relationships. With the addition of rule and logic languages, however, greater leaps in conceptual understandings, learning, and adaptation can take place, although implementations with these types of capabilities are, as yet, few and far between. Both free and commercial versions of inference engines are available. For example, Jena, an open source Java framework for writing Semantic Web applications developed by HP Labs, has a reasoner subsystem. Jena reasoner includes a generic rule based inference engine together with configured rule sets for RDFS and for the OWL-Lite subset of OWL Full. JESS is a popular OWL inference engine from Carnegie Melon University. Network Inference offers a commercial reasoner based on description logic (OWL-DL).

Other Components

Ordinary web pages are a good source of instance information; many tools for populating ontologies are based on annotation of web pages. W3C Annotea project offers free annotation tools. Commercial vendors include Ontoprise and Lockheed-Martin. Several software vendors, including Semagix, Siderian Software and Entopia, offer products that use ontologies to categorize information and to provide improved search and navigation.

Applications of Semantic Technologies

Semantic technologies can solve problems that, using current technologies, are unsolvable at any price.

-Don Hall

There are a wide variety of applications where semantic technologies can provide key benefits. At their core, semantic approaches are an infrastructure capability that, when combined with other key technologies, represent the next wave of computing. When taken with a multi-year view, there is great promise that these technologies will help the IT industry reach the ever-elusive goal of truly adaptive computing. In some respects, though, the future is already happening. Commercial enterprises and government agencies are implementing production-level programs using existing semantic data stores, ontologies, toolsets, and applications. A few of these near-term project areas include Semantic Web services, semantic interoperability, and intelligent search.

Semantic Web Services
A Web service is a software system designed to support interoperable machine-to-machine interaction over a network. A Web service has an interface described in a machine-processable format using Web Services Description Language (WSDL). The combination of WSDL, UDDI, and SOAP  form a triad of technologies that will shift the entire market toward service-oriented architectures (SOA). Together, these technologies provide directory, component lookup, and exchange protocol services on top of an HTTP or SMTP network protocol. Microsoft, IBM, and most other large software vendors have embraced the concepts and languages that underlie the Web services model, and an increasing number of books and industry articles point to the benefits of adopting a service oriented architecture. Web services, however, are not without shortcomings. Security issues have long been a concern but several solutions that address these issues have been introduced over the last several years. Perhaps the most significant improvement opportunities for Web services that remain are in the areas of

(a) flexible look-up and discovery and

(b) information management and schema transformation.

Fundamentally, Web service technologies handle messages in a loosely coupled manner but they do not currently bridge differences in description terminologies nor do they inherently enable the recipient to understand a message that has been sent. With Web services, these parts of the exchange rely on custom-coded solutions and/or widespread community agreement upon some kind of document exchange standard (the latter is rarely achieved).

This difficulty in ensuring flexible discovery and service initiation, as well as seamless operational use of information exchanged with Web services, has led to W3C’s efforts to incorporate semantic technologies as part of its Semantic Web Services initiative. Semantic Web Services are a Web Service implementation that leverages the Web Ontology Language Service specification (OWL-S) to provide a flexible framework for describing and initiating web services. OWL-S supplies Web service providers with a core set of markup language constructs for describing the properties and capabilities of their Web Services in unambiguous, computer-interpretable form. OWL-S markup of Web services will facilitate the automation of Web service tasks, including automated Web service discovery, execution, interoperation, composition, and execution monitoring. Following the layered approach to markup language development, the current version of OWL-S builds upon W3C’s standard OWL.

Semantic Interoperability
Formally put, the use of semantic technologies makes it possible to describe the logical nature and context of the information being exchanged, while allowing for maximum independence among communicating parties. The results are greater transparency and more dynamic communication among information domains irrespective of business logic, processes, and workflows (Pollock and Hodgson, 2004).

The technical vision is one where flexible information models, not inflexible programs or code, are used to drive dynamic, self-healing, and emergent infrastructures for the sharing of mission critical data in massively scaleable environments. Recent advances in taxonomy and thesaurus technology, context modeling approaches, inferencing technology, and ontology-driven interoperability can be applied in a cohesive framework that dramatically changes the way information is managed in disperse, decentralized communities of knowledge (Pollock and Hodgson, 2004).

NASA views semantic interoperability as an extremely promising way to make information available to all stakeholders without having to standardize on a particular format or vocabulary or re-key databases to conform to a uniform model. One example where NASA is using these concepts is to address serious and ongoing maintenance problems related to the aging wiring systems within the Space Shuttle fleet. The existing set of wiring system databases contains information about part specifications, bills of materials, drawings, change orders, standard practices, test procedures, test reports, inspection reports, failure tracking and reporting information, work orders, and repair disposition documentation. Tens of diverse databases and systems – each supporting different but related aspects of engineering and design work – are in use within NASA with related data dispersed among several contracting companies that support the Space Shuttle program. Troubleshooting wiring problems requires timely access to many cross-organizational systems, databases, and knowledge repositories, the breadth of which is enormous. The situation becomes especially critical for diagnosing and troubleshooting in-flight anomalies whereby a timely resolution is mission-critical as well as life-critical. The work to make these sets of data richer and more accessible to the numerous parties who need access to them is still in its early stages, but as highlighted in the quote at the beginning of this chapter, semantic technologies represent one of the more promising ways to address what is largely an unsolvable problem using current data integration approaches.

A highly distilled version of how such a project works is as follows. Design-time tools are used to develop RDF and OWL models that encompass a particular domain. These models could be based on existing XML standards or defined via other means. Other design-time tools can be used to flexibly map specific data representations to these models, thus eliminating the need to explicitly convert applications to adopt a certain data standard. Run-time processes can then use these models and maps as pivot tables to transform data from source to target or to perform federated queries from a single query statement. Semantic interoperability frameworks of this type can provide a solid basis for better resolving differences in syntax, structure, and semantics – ushering in a future where organizations can agree to disagree, yet still share data and interoperate without having to change their current methods of operation.

One of the key advantages of using semantic interoperability approaches is that they do not necessarily require the replacement of existing integration technologies, databases, or software applications. A semantic framework made up of various semantics-based components and application program interfaces (APIs) can be deployed with web services or traditional middleware APIs to leverage existing infrastructure investments, and yet still provide massive benefits by virtually centralizing the query, transformation, and business rules metadata that flows through the network infrastructure’s pipes. As such, the software will fit into the customer’s existing IT ecosystem with low overhead for installation, minimal coding, and maximum reusability (Pollock and Hodgson, 2004).

Intelligent Search
Related in some regards to semantic interoperability is the area of intelligent search. As mentioned above, semantic interoperability techniques can allow queries native to one system to be federated to other non-native systems. This eliminates the need to convert systems to a universal query language and enables systems to continue maintaining the information they have in their current formats. By overlaying a virtual layer on top of the data sources, queries can be defined in a universal manner, thereby enabling access to all mapped assets. Federated searching can also be made smarter by making searches more semantically precise. In other words, searches can be broadened to include concepts, or narrowed to include only specific key words. The depth – or granularity – of such searches enables the specification of the search that the individual desires. Another aspect of intelligent search is the ability to make searches more relevant to the person searching by making use of identity and relationship information. Relationships among people and information about them can be key links to greater relevance and confidence. Despite investments in knowledge management systems, many people still rely on their personal network of friends, neighbors, co-workers, and others to locate experts or find trusted information. Personal relationships are also useful in sales situations and in many organizational interactions. Social networking schemas and software are making broad use of this.

An example of how this information can be used on a larger scale is the case of a telephone company exploring technologies for providing more intelligent phone number look-up. Instead of providing a generic list of matched names, the telecommunications company is looking at combining information about the person searching and the list of possible names, in order to provide a more intelligent match. For example, inferring relationships between social networks could provide information on whether a person is known, or could be expected to be known, to the other person (by employing friend-of-a-friend forms of calculations). Other information such as locations or schools attended or past or current jobs could be used to infer matches. To be sure, there are significant privacy issues involved; many believe, however, that techniques such as hashing and encryption of personally identifiable information and progressive disclosure will likely resolve many privacy concerns. Semantic approaches for enabling intelligent search are beginning to find their ways into knowledge management systems. Whereas current knowledge management systems tend to exist within their own silos and have difficultly crossing organization boundaries, intelligent search techniques can be added as overlays to existing information infrastructures, thereby bridging physical data formats, knowledge domains, and organizational structures.

title: Semantic Web/The Vision


text: User:Snow

license: Creative Commons - Attribution Share-Alike - https://creativecommons.org/licenses/by-sa/4.0/

title: User:Snow


text:
Talk:Macroeconomics/Savings and Investment

dont get what you're saying in the second half of this article! --Mark Lewis 19:37, 17 Mar 2005 (UTC)

Saving = investment?
O=Aggregate output of a country. Y= National Income of a country. E= Aggregate expenditures of a country. We can say. O=Y= E.             ➡ eq(1). eq= equation. O= Y.  eq➡ (2). O=E. eq➡ (3). Y= E.    ➡ eq (4). Y= C+ S  ➡ eq(5). Y= C + I ➡   eq(6). S= I.         ➡ eq(7). The expenditures can not be without concunptions so we can say C= E.  ➡eq (8). C= C+ S ➡ (9). because Y=E ➡ eq(4). And Y= C+ S➡ eq(5). Equation no 9 is only possible if S=0(zero). S= I       see equation. If S is zero than the investment is also zero.If there is no investment than there will be no output and no output means no expenses and no national income how is this possible? Ali Kamran 786 (discuss • contribs) 18:13, 20 July 2016 (UTC)


text:
User:Sfinx~enwikibooks

Cercatemi sul mio profilo principale

 * 1) Profilo principale []
 * 2) Le mie discussioni []

Find me on my main page

 * 1) Main profile page []
 * 2) My talk page []

title: User:Sfinx~enwikibooks


text:
User talk:JoeEdelman~enwikibooks

Hi. Welcome.

JoeEdelman 00:47, 18 Mar 2005 (UTC)

Your account will be renamed
Hello,

The developer team at Wikimedia is making some changes to how accounts work, as part of our on-going efforts to provide new and better tools for our users like cross-wiki notifications. These changes will mean you have the same account name everywhere. This will let us give you new features that will help you edit and discuss better, and allow more flexible user permissions for tools. One of the side-effects of this is that user accounts will now have to be unique across all 900 Wikimedia wikis. See the announcement for more information.

Unfortunately, your account clashes with another account also called JoeEdelman. To make sure that both of you can use all Wikimedia projects in future, we have reserved the name JoeEdelman~enwikibooks that only you will have. If you like it, you don't have to do anything. If you do not like it, you can pick out a different name.

Your account will still work as before, and you will be credited for all your edits made so far, but you will have to use the new account name when you log in.

Sorry for the inconvenience.

Yours, Keegan Peterzell Community Liaison, Wikimedia Foundation 23:17, 17 March 2015 (UTC)

Renamed
 This account has been renamed as part of single-user login finalisation. If you own this account you can |log in using your previous username and password for more information. If you do not like this account's new name, you can choose your own using this form after logging in: . -- Keegan (WMF) (talk) 05:05, 19 April 2015 (UTC)

title: User talk:JoeEdelman~enwikibooks


text:
User talk:66.168.122.226

Thanks for experimenting with Wikibooks. Your test worked, and has now been removed. Please use Sandbox for any other tests you want to do, since testing material in modules will normally be removed quickly. Please see the welcome page if you would like to learn more about contributing to our project. Thanks. Guanaco 02:53, 18 Mar 2005 (UTC)

title: User talk:66.168.122.226


text:
Unsolved Problems in Biology/Magnetite

It has been suggested by those outside of mainstream science that magnetite gives organisms a sort of '6th sense', with which they can predict events in the near future (for example, earthquakes and other difficult-to-predict phenomena). Clearly this is not possible if existing scientific theories of time are accepted.

A more plausible idea is that magnetite's weak magnetic field provides some information that can be processed by an organism's central nervous system. The degree to which this information could be gathered, processed, and perceived is not established. More conservative researchers believe the magnetite acts as a tiny compass which might explain, for example, how migratory birds seem to know the difference between north and south, as exhibited by their migratory patterns.

title: Unsolved Problems in Biology/Magnetite


text:
User talk:Ojmorales0002

The Golden Bough and hello!

 * Welcome to Wikibooks. I hope you enjoy being around here and doing many interesting things.
 * It was a good idea of yours to put The Golden Bough on Wikibooks, and I would be interested to see what interactive and graphical stuff you and others might be able to put on it.
 * However, if it is just a textbook (I mean, only text) it would belong better on Wikisource. If we could interact with it and put our own views, thoughts and exercises, as well as bring more modern myths to it. I am aware that there is a lot of the developing world that has not seen this text, and may well have different opinions about the material.

-EuropracBHIT 03:58, 18 Mar 2005 (UTC)

 * I will put the text on wikisource as well. The more developed e-book will be at wikibooks
 * Thanks for the advice.

Ojmorales0002 12:20, 18 Mar 2005 (UTC)

Organization of the Golden Bough
Hi! I had a look at your book The Golden Bough: A Study in Magic and Religion. There is some discussion about how to organize books at Hierachy naming scheme, and I would like to give you the following suggestion: I know you invest a lot of time into your book. Please consider make it a lasting effort. Thanks, and good luck in the future. If you have questions or need help, just let me know. --Andreas 15:25, 18 Mar 2005 (UTC)
 * tgb is not a good way to separate your book from others.
 * I recommend you to use The Golden Bough for the book and The Golden Bough/Preface,The Golden Bough/The King of the Wood, etc., that is create subpages to your book using the "/" slash command. In this way you will automatically get a link back to your main book, if it has the same name throughout (have a look at the book Hamster Care how it is done there.)
 * For successful and not so successful alternatives have a look at Hierachy naming scheme.

 * Thanks Andreas, that makes sense. I will read up on how to change / move to that scheme. I have only added two chapters at the moment good thing you told me now. DO you have a quick link on how to rename the initial book or move the already created chapters. Ojmorales0002 15:34, 18 Mar 2005 (UTC)

 * If you are logged in, then you should have the "move" command on top of the page, with which you can rename a page. This is the best way to do things. If you don't have this command, then I can also move the pages for you, if you want. --Andreas 15:45, 18 Mar 2005 (UTC)

 * I did it for the main page The Golden Bough, worked fine, will do the other pages before continuing and Ill check any pages refrencing them as well. again, thanks a lot Ojmorales0002 16:02, 18 Mar 2005 (UTC)

 * I redid all the links, its actually easier with the naming as you suggested, like directories in DOS. Thanks again, Ojmorales0002 18:35, 18 Mar 2005 (UTC)

 * Great! Well done. Another thing: Most of your links are displayed as ==headings== . It seems natural to do so, because you are putting headings there, but the content is somewhere else. On other Wikibooks, people struggle with other users clicking on the "edit" button next to those sections to edit things, some don't notice that they should click on the heading itself.
 * I therefore recommend you, to put links not into ==headings==, but use ordinary "*" lists for it. Also if you put all chapters between "==..==", then you get a table of contents at the top of the page, which is pointless, since the same table appears below. Therefore, the cleanest thing is not to put links between "==..==", but into a list. --Andreas 18:48, 18 Mar 2005 (UTC)

 * I startigng noticing that, The idea is a left over from when I wanted the whole book on one page. I do like the horizontal bar that shows up after text when they are within "=..=" though, is there a way of having that or of not having a TOC? Ojmorales0002 19:03, 18 Mar 2005 (UTC)

 * Yes, there is a way for each of these things, but I don't think you need the line under each link on a page like The Golden Bough/Part 1. Maybe you already got used to it, but with so many chapters having each of them so big and underlined looks a little bit clumsy.. --Andreas 19:25, 18 Mar 2005 (UTC)

 * In fact I am doing away with the whole part sections and adding the chapter links to the The GOlden Bough page, I will keep the toc there so that people can jump to the old "Part ..." heading, to save would be readers a bit of time. It also will save space on the servers and further editing. Ojmorales0002 19:19, 18 Mar 2005 (UTC)

 * Very good. This looks much better now. --Andreas 19:25, 18 Mar 2005 (UTC)

 * I have requested that The Golden Bough/Part1, The Golden Bough/Part 1 The Golden Bough/Part 2 and The Golden Bough/Part 3 be deleted, I have moed the links to the main page. I am way happier with how it looks now. Ojmorales0002 20:05, 18 Mar 2005 (UTC)

 * I am like an idiot, I can take out the bottom lonks as well, since they are on top due to the hierarchy. Ojmorales0002

 * :-) If you want even more convenience, you could use numbered lists, using the "#" symbol instead of "*", like here:

 * 1) The King of the Wood
 * 2) Priestly Kings
 * 3) Sympathetic Magic
 * Could also save some time, for numbering all chapters.. and later inserting a new one.. --Andreas 21:18, 18 Mar 2005 (UTC)

Where my version of The Golden Bough Belongs

 * If I need to, is there a way of moving this book to wikisource. Although i thought wikisource was for 'source texts' not formatted or edited text. Ojmorales0002 19:08, 18 Mar 2005 (UTC)

 * Yes, the way you started organizing it, it seems it is better a book for Wikisource (which contains original work by a certain author). Wikibooks is more meant for books, where each page can be edited by anyone, so I don't think this is what you want. There could be a Wikibook that explains the main ideas of the original work (like a study guide). Unfortunately there is no easy way to transfer a Wikibook to wikisource other than copying and pasting every page.. but I'd recommend you to do this sooner than later (the sooner you do it, the less you have to copy). Just ask yourself: Is it ok, if other users change the content of each of the chapters in the book (modern language, add or remove content, rewrite parts of the book, etc.), then it could be a wikibook. Or do you want to present the book as it was originally written, then it should be Wikisource, where users mainly correct spelling mistakes and the like, but not the original content. (Both are wikis, and in both wikis the book can be presented nicely). --Andreas 19:25, 18 Mar 2005 (UTC)

 * Gotcha, then I think is best keep it here. I will still upload the actual text to wikisource for people to be able to use. But I want to be able to change certain things, which I, out of respect, would not want to do to the original. Ojmorales0002 19:29, 18 Mar 2005 (UTC)

 * Ok, if you really want this to be a wikibook with no compromises, then I have yet another suggestion (I hope it is not getting too many... :-) ): Assume in a year somebody comes, and wants to insert a new chapter between chapter 4 and chapter 5. The person would have to move all chapters in order to renumber them, to insert a chapter. That is why the prefered way of organizing Wikibooks is, to have the title of a chapter as the link name: Use The Golden Bough/The King of the Wood, The Golden Bough/Priestly Kings, etc. instead of The Golden Bough/Chapter 1, The Golden Bough/Chapter 2, and so on . If you want, you can still write the Chapter number on the main page, but this is then much easier to correct, if somebody wants to add or delete or move a chapter. --Andreas 20:11, 18 Mar 2005 (UTC)

 * I can see a benefit in that. People can add remove, expand or compile other versions of the book (the ones in PD anyway) Thanks. Ojmorales0002

Love affair
By the way, I am loving the wiki world.... and I just noticed that this book will take a few, lol. thanks all, Ojmorales0002 15:42, 18 Mar 2005 (UTC)

title: User talk:Ojmorales0002


text:
Wikibooks talk:Book of the month/April 2005 voting

Graphic
The graphic still says March. We're voting for April. How can this be fixed?--Hyperlink 07:09, 18 Mar 2005 (UTC)
 * Just edit the page as you would normally: The first line is inserting the image, with the explicit caption of the image, so anyone can change it there. I did the change already. --Andreas 09:44, 18 Mar 2005 (UTC)
 * I understand now, thanks. Somehow I didn't see it when I went to edit it... It must have just slipped by me.--Hyperlink 09:50, 18 Mar 2005 (UTC)

Eyu100

 * Unfortunately, on the Mandarin Chinese page, one of the users hasn't been logged in.
 * They haven't made any contributions. This is a little suspicious, because if they did they would have a contributions page.
 * So, how do we 'take back' that vote?

-EuropracBHIT 04:24, 18 Mar 2005 (UTC).
 * If you talk about Eyu100, he/she made 2 contributions so far: Eyu contributions, the first being the vote. Nothing wrong with that, maybe the user decided to create an account in order to vote, so that's perfectly fine (as long as it is not 10 new users suspiciously voting for the same book...) --Andreas 09:47, 18 Mar 2005 (UTC)

Voting closed - congraulations to US History - happy voting for May 2005

 * Yesterday, (as of midnight UTC) the voting closed.
 * US History was 'clearly' in the lead with five votes, however there were 3 books on four votes out of the 12 nominated this month.
 * I would like to congraulate the editors of US History for having brought to fruition the first Humanities Wikibook of the Month. I am sure you will agree when I say that history is very important to know ourselves and our future. US History is a very good Wikibook for this purpose.
 * We have had some very close, interesting and competitive votes here and many well-deserving books.
 * We now have the five books with more than 3 votes for May, which means US history cannot be featured for a long year.
 * Who is prepared to design a picture and change the main page accordingly?
 * Once again, well done and congraulations!

--EuropracBHIT 05:56, 1 Apr 2005 (UTC).


text:
Using KDE/History of KDE

Brief History
KDE was founded in 1996 by Matthias Ettrich, who was then a student at the University of Tübingen. He found a number of things wrong with the UNIX desktop at that time. Among his qualms, outlined in a now-famous newsgroup post, were that none of the applications looked, felt, or worked alike to each other. He proposed the formation of not only a set of applications, but rather a desktop environment, in which users could expect things to look, feel, and work consistently. He also wanted to make this desktop  easy to use. One of his complaints with desktop applications of the time was that his girlfriend could not use them. That post spurred a lot of interest, and the KDE project was born.

Matthias chose to use the Qt toolkit as the toolkit of choice of the KDE project. Other programmers quickly started developing KDE/Qt applications, and by early 1997, large and complex applications were being released. In mid-1997, the GNU project had concerns about the licensing of Qt, leading to their founding the GNOME Desktop project and Harmony, a now-abandoned project to clone Qt. Qt was later relicensed to provide the GNU General Public License as an option, which has mitigated these concerns. There is still considerable disagreement over the use of the full GPL for a library like Qt, and the restrictions this imposes on code linking to it, such as the KDE framework and any applications written for it. Both KDE and GNOME now participate in Freedesktop.org, an effort to standardize Unix desktop interoperability, although there is still some friendly competition between them.

title: Using KDE/History of KDE


text:
Using KDE/Applications

KDE Applications
KDE has following applications under different categories.

Development

 * Cervisia - CVS Frontend
 * KAppTemplate - KDE Template Generator
 * KCachegrind - Profiler Frontend
 * KDevelop - Integrated Development Environment
 * KDiff3 - Diff/Patch Frontend
 * KImageMapEditor - HTML Image Map Editor
 * KLinkStatus - Link Checker
 * Kommander - Dynamic Dialog Editor
 * Kompare - Diff/Patch Frontend
 * KUIViewer - Qt Designer UI File Viewer
 * Lokalize - Computer-Aided Translation System
 * Umbrello - UML Modeller

Education

 * Artikulate - Pronunciation Trainer
 * Blinken - Memory Enhancement Game
 * Cantor - KDE Frontend to Mathematical Software
 * GCompris - Educational suite
 * KAlgebra - Graph Calculator
 * Kalzium - Periodic Table of Elements
 * Kanagram - Letter Order Game
 * KBruch - Exercise Fractions
 * KGeography - Geography Trainer
 * KHangMan - Hangman Game
 * Kig - Interactive Geometry
 * Kiten - Japanese Reference/Study Tool
 * KLettres - Learn The Alphabet
 * KmPlot - Mathematical Function Plotter
 * Kst - Data Viewer
 * KStars - Desktop Planetarium
 * KTouch - Touch Typing Tutor
 * KTurtle - Educational Programming Environment
 * KWordQuiz - Flash Card Trainer
 * LabPlot - Scientific Data Plotter
 * Marble - Virtual Globe
 * Minuet - Music Education Software
 * Parley - Vocabulary Trainer
 * Rocs - Rocs Graph Theory
 * Step - Interactive Physical Simulator

Games

 * Bomber - Arcade Bombing Game
 * Bovo - Five-in-a-row Board Game
 * Granatier - Bomberman clone
 * Kajongg - Mah Jongg
 * Kapman - Pac-Man Clone
 * KAtomic - Sokoban-like Logic Game
 * KBlackbox - Blackbox Logic Game
 * KBlocks - Falling Blocks Game
 * KBounce - Ball Bouncing Game
 * KBreakout - Breakout-like Game
 * KDiamond - Three-in-a-row game
 * KFourInLine - Four-in-a-row Board Game
 * KGoldrunner - Hunt Gold, Dodge Enemies and Solve Puzzles
 * Kigo - Go Board Game
 * Killbots
 * Kiriki - Yahtzee-like Dice Game
 * KJumpingCube - Territory Capture Game
 * KLines - Tactical Game
 * KMahjongg - Mahjongg Solitaire
 * KMines - Minesweeper-like Game
 * KNetWalk - Network Construction Game
 * KNights - Chess Game
 * Kolf - Miniature Golf
 * Kollision - A simple ball dodging game
 * Konquest - Galactic Strategy Game
 * KPatience - Patience Card Game
 * KReversi - Reversi Board Game
 * KSame - Board Game
 * KShisen - Shisen-Sho Mahjongg-like Tile Game
 * KsirK - World Domination Strategy Game
 * KSnakeDuel - Tron-like Game
 * KSpaceDuel - Space Arcade Game
 * KSquares - Connect the dots to create squares
 * KSudoku - Sudoku Game
 * KTeaTime - Tea Cooker
 * KTuberling - Picture Game for Children
 * Kubrick - 3-D Game based on Rubik's Cube
 * LsKat - Card Game
 * Naval Battle - Naval Battle Battleship Game
 * Palapeli - Jigsaw puzzle game

Graphics

 * digiKam - Photo Management Program
 * Gwenview - Image Viewer
 * Karbon - Scalable Graphics
 * KColorChooser - Color Chooser
 * KGraphViewer - A Graphviz dot graph viewer
 * KolourPaint - Paint Program
 * KPhotoAlbum - Photo Album
 * Krita - Digital Painting and Drawing Application
 * KRuler - Screen Ruler
 * KSnapshot - Screen Capture Program
 * KXStitch - Cross Stitch Pattern Editor
 * Okular - Document Viewer
 * Skanlite - Image Scanning Application
 * Spectacle - Screenshot Capture Utility
 * SymbolEditor - Cross Stitch Symbol Editor

Internet

 * Akregator - Feed Reader
 * Blogilo - A KDE Blogging Client
 * Choqok - KDE Micro-blogging Client
 * KFTPgrabber - FTP Client
 * KGet - Download Manager
 * KMail - Mail Client
 * KMLDonkey - MLDonkey Client
 * KNemo - Network Monitor
 * KNode - News Reader
 * Konqueror - KDE File Manager & Web Browser
 * Konversation - IRC Client
 * Kopete - Instant Messenger
 * KPPP - Internet Dial-Up Tool
 * KRDC - Remote Desktop Client
 * KTorrent - BitTorrent Client
 * Kwlan - Wireless Lan Manager

Multimedia

 * Amarok - Audio Player
 * Dragon Player - Video Player
 * JuK - Music Player
 * K3b - Disk Burning
 * Kaffeine - Media Player
 * KAudioCreator - CD Ripper
 * Kdenlive - Video Editor
 * KMid - A KDE4 MIDI/Karaoke Player
 * KMix - Sound Mixer
 * KMPlayer - Media Player
 * KWave - Sound Editor
 * KsCD - CD Player

Office

 * Flow - Flowchart & Diagram Editing
 * KAddressBook - Contact Manager
 * KEuroCalc - A currency converter and calculator
 * Kexi - Database Creator
 * Kile - LaTeX Frontend
 * KMyMoney - Personal Finance Manager
 * Kontact - Personal Information Manager
 * KOrganizer - Personal Organizer
 * Plan - Project Management
 * Sheets - Spreadsheet
 * Skrooge - Manage your money
 * Stage - Presentation
 * Tellico - Collection Manager
 * Words - Word Processor

System

 * Apper - Software Management
 * Dolphin - File Manager
 * KAppfinder - Menu Updating Tool
 * KDE Partition Manager - Partition Editor
 * KDiskFree - View Disk Usage
 * KInfoCenter - Info Center
 * KHelpCenter - Help Center
 * Kiosk Admin Tool - Kiosk Framework Administration
 * Konsole - Terminal
 * Krfb - Desktop Sharing
 * KSysGuard - System Monitor
 * KSystemLog - System Log Viewer
 * KWalletManager - Wallet Management Tool
 * Yakuake - Drop-down Terminal

Utilities

 * Ark - Archiving Tool
 * Jovie - KDE Text To Speech Daemon
 * KAlarm - Personal Alarm Scheduler
 * Kate - Advanced Text Editor
 * KCalc - Scientific Calculator
 * KCharSelect - Character Selector
 * KFileReplace - Search & Replace Tool
 * KFind - Find Files/Folders
 * KFloppy - Floppy Formatter
 * KGpg - Encryption Tool
 * Kleopatra - Certificate Manager and Unified Crypto GUI
 * KMag - Screen Magnifier
 * KMouseTool - Automatic Mouse Click
 * KMouth - Speech Synthesizer Frontend
 * KNotes - Popup Notes
 * KPager - Desktop Pager
 * KRecipes - Cooking Book
 * KRemoteControl - Remote Controls
 * Kronometer - Stopwatch
 * Krusader - File Manager
 * KSig - Signature Editor
 * KTimer - Countdown Launcher
 * KWrite - Text Editor
 * Okteta - Hex Editor
 * RSIBreak - Helps you fight RSI
 * Smb4K - Advanced Network Neighborhood Browser
 * Sweeper - System Cleaner
 * Zanshin - TODO Management Application

Taking a screenshot
Screenshots can be captured using the KSnapshot utility. Hitting the PrtScn key activates KSnapshot. You can refine the screenshot by changing the capture mode, and clicking Take a New Screenshot (or hitting Alt-N).

 * Full Screen - Captures everything. (This is the default.)
 * Window Under Cursor - Grabs the clicked-on window, even if another window is in front of it.
 * Rectangular Region - Click and drag to select the area, and then hit Enter to return to KSnapshot and save it, or Esc to quit.
 * Freehand Region - Same as Rectangular Region, except that you aren't limited to a rectangle: the area you select can be any shape.
 * Section of Window - The area of the window that will be captured is outlined in red. Double-click on it.

Besides PNG and JPEG, you can also save the screenshot as a BMP, EPS, JPEG-2000 (.jp2), MNG, PBM, PCX, PIC, PGM, PPM, RGB, TGA, TIFF, XBM, or XPM. You can also click the Copy button to copy it to the clipboard. The Send To... menu lets you import the image directly into another program.

title: Using KDE/Applications


text:
Using KDE/What is KDE

KDE (K Desktop Environment) is a free desktop environment and development platform built with Trolltech's Qt toolkit. It runs on most Unix and Unix-like systems, such as Linux, BSD and Solaris. There are also ports to Mac OS X using its X11 layer and Microsoft Windows using Cygwin. A port to native Microsoft Windows using a new-from-scratch Qt toolkit is being discussed. It is developed in conjunction with KDevelop, a software development suite, and KOffice, a suite of office software.

The "K" originally stood for "Kool" ("C" as in "cool" was already given away to the Common Desktop Environment), but was changed soon after to stand simply for "K", which is "The first letter before 'L' (which stands for Linux) in the Latin alphabet."

=Desktop Environment: What?= Computers are made by humans to make it easier to deal with information. Think of spreadsheets in accounting and word processors in writing lovely letters. While computers can easily store gigantic amounts of information and search and analyse it extremely fast, it is difficult to represent such information in a human usable method.

Here the desktop metaphor enters. One should think of your monitor as one's office desk. It has a telephone to call other people, paper stack with your correspondence, desk drawers with your files and a garbage can for your junk. You interact with each of these objects on your desk with physical means. You pick up a piece of paper, call the number of the top, crumple it up and drop it in the garbage or file it in the file cabinet.

To interact with your computer you usually have a monitor, a mouse and a keyboard. This is very different from the real desk where you just use your hands to pick up a document. On a computer you can’t work in this way. Here you have to move the mouse so that the corresponding mouse cursor on your monitor is hovering above a little picture that represents your document. Now you need to push the buttons on the mouse without moving its screen cursor to interact with your document.

Desktop environments are the interfaces that make such interaction of your hands through a mouse/keyboard as logical and easy as possible and display the results of your actions on the screen in a manner so that you quickly and easily understand the consequences of your actions. KDE is a desktop environment that aims to be the best graphical interface between humans and the information they have stored in computer systems. It aims to not only provide the framework but also the applications. The framework draws on the screen, accepts user input, the applications apply such input to modify the information, and passes the results back to the framework where they are displayed. Difficulties that are common in this method is that it takes time for a computer to do its work, so it might take some time before your actions reveal this time, called latency. Latency is the time between input and its corresponding output. The longer this time, the less pleasing the user experience is for the user.

A second oft-sited concern of users is that buttons with the same functions are located in varying locations in the interface. KDE attempts to limit this by providing standards for the makers of the applications so that consistent interfaces are presented to the user. Consistency is nice but consistent mysteries don’t help you! KDE tries to be clear in what the results will be of possible actions. It does so by providing pictures on buttons (icons) that represent the results of activating such buttons. However, some people don’t quickly understand icons, so straightforward text is sometimes better. KDE allows both and gives people the option of customizing their interface to their information so that they can work as effectively as possible.

And that is the goal of KDE: giving humans the most effective interface for humans to work with their information on computers.

title: Using KDE/What is KDE


text:
User:Hyperlink

Open Source stuff is great. I'm going to be switching from Winders to FreeBSD or Debian soon.

I am a native Wikibookian. I'm around most of the other "foundation" Wikis, but I spend the majority of my time here. I try to crawl around and "restructure" things as much as possible. Organization and coherency is very important.

Private Shelves
I am currently involved with Green items. I intend to soon be involved with Yellow items. I intend to deal with Red items "sometime in the future". If you add an item, please code it Gray. I will change it when I decide how I will be dealing with it. How To Build A Computer - A Neutral Look at Operating Systems - Note taking - Meeting_Basics - Learn_Accounting_with_Dick_and_Jane

Chess - Algebra - Applied Math Basics - AI - School of Computer Science - School of Philosophy - High_school_extensions - Physics Study Guide - PHP - Study_Skills

 * Think that there is something I need to work on or read? Feel free to stick anything suitable on either shelf! You can drop a comment about it somewhere too if you like.--Hyperlink 07:05, 25 Mar 2005 (UTC)

How To Build A Computer

 * Revamped the Hardware organization.
 * Brought the "one page book" to the level of sub-modules.

A Neutral Look at Operating Systems

 * Founded
 * Currently working on the "basic stuff"

Misc

 * I do a lot of random crawling and editing.
 * I've been trying to "clean up" the language books. Perhaps with a little bit more structure they will be more likely to grow...

Notes to self

 * Issue Guide conversation with TUF-KAT

title: User:Hyperlink


text:
5 in a Row

This module is aimed at explaining the rules of games such as gomoku, renju, and pente. The aim of these games is generally to get five pieces in a row. This module also aims to provide some general theory to aid in gameplay.

Games

 * /Gomoku/
 * /Pente/
 * /Renju/
 * /Wuziqi/

Terms & Definitions
Here are some terms used in this book:

Piece: a stone (generally either black or white) belonging to one of the two players. this can be a glass stone, a plastic stone, a symbol representing a stone, etc.

Row: a line of adjacent pieces which are all of the same colour. gaps in this line are allowed.

Unbroken row: a row without gaps.

n in a Row: n pieces of the same colour arranged in a horizontal, vertical, or diagonal row. this row is unbroken. creating 5 in a row generally implies a win.

Four: a row of 4 (not necessarily 4 in a row), in which placing one more piece results in a 5 in a row.

Straight four: 4 in a row.

Three: a row of 3 (not necessarily 3 in a row), in which placing one more piece results in a straight four.

Straight three: 3 in a row.

Overline: n in an unbroken row. (n > 5)

Double three: two threes intersecting each other

Double four: two fours intersecting each other

Capture: to execute a move which results in the removal of some opponent pieces from the board

title: 5 in a Row


text:
5 in a Row/Gomoku

Description: Gomoku is a relatively easy game for two players. It consists of a 15 x 15 board and two sets of player pieces (one set for each player). Generally there is a set of black pieces and a set of white pieces; the 'black player' makes the first move.

Aim: to get five of one's pieces in a row (horizontal, vertical, or diagonal)

Rules: players alternate turns, players may not make more than one move in one turn, players may not skip their move during their turn.

Gameplay: (optional) after black makes his/her second move, white may choose to play as black. (note: this is the only time in the game where players may swap the colour of their pieces)

title: 5 in a Row/Gomoku


text:
User talk:Reub2000

Reub - How To Build A Computer has been nominated for April 2005 book of the month. We're two off leading it - so if you could vote, it would be great. You can do so here --Noogz 11:03, 18 Mar 2005 (UTC)

Note: My talk page is not for disscussing articles. Please disscuss an article on it's talk page. If you want me to look at a talk page for an article, you may post a message here. Any disscusion of an article or module will be moves to the appropriate talk page.Reub2000 21:11, 14 Apr 2005 (UTC)

Congrats
Reub - just a quick note of congratulations on having the project you started achieve Book of The Month - That is a truly fantastic achievement. I'm really glad to have worked on it with you. --Noogz 02:05, 1 May 2005 (UTC)

 * Yeah, this is great. I can't belive that my messy computer case is on the front page, lol. Reub2000 04:28, 1 May 2005 (UTC)

Slackersbible
This book contains part of what can be found on http://www.slackersbible.org/. Why is most content missing from Wikibooks' copy? Did you simply abandon the transfer to Wikibooks, or was that the full book when the copy was done, and the idea of developing on Wikibooks abandoned?--Chealer 17:01, 2 July 2007 (UTC)

title: User talk:Reub2000


text:
Movie Making Manual/CUS Story

While some original ideas were brainstormed, none of them were of appropriate ease/length for a beginning (short) movie, often by very small amounts. A number of public domain works, mostly by H. P. Lovecraft (focusing largely on The Call of Cthulhu), were also considered and deemed difficult but possible.

The following process was (mentally) used:

 * 1) Are we able to use the story?
 * 2) Is the story interesting?
 * 3) How many characters are there? Would we have enough volunteers (read: conscripts) to make such an attempt viable?
 * 4) How easy is the story to convert from a narrative to a script format? Would a major overhaul be necessary, resulting only in general similarity; or is it easily adaptable?
 * 5) What is the setting? Are we able to find locations/costumes/etc. necessary? What grovelling/paying would be needed?
 * 6) From what perspective would we be following the story? Would we have the camera literally 'in the eye' of the protagonist, seeing what he sees; or would we use the standard omnipresent view?
 * 7) Do we have enough equipment? Is the equipment we have capable of what we want it to do?
 * 8) Can we afford it?

This was the (mental) analysis for The Call of Cthulhu (not regimented as provided below, but more collectively brainstormed):

The Call of Cthulhu was written in the 1920's so the copyright has expired. The story is interesting to an audience more interested in the more abstract stories, however more average audiences would probably be confused and not understand.

The number of main characters is enough so that with a little shuffling, the film could be made. However, the most problematic thing is the extras needed (eg. police officers in raid on cultists, shipmates on voyage to R'lyeh, etc.). If techniques such as using lighting to block out the more specific features on people's faces, and actors were moved around (eg. an actor is the main in one scene/for one character, and acts as an extra in the rest), it could be achieved. Costumes would also be difficult (requiring a number of 1900's-style police uniforms, among other things), but if we asked around through our contacts in stage and acting, we might be able to scrounge up a few. If worse came to worst, we could always improvise.

The story isnt as much not in the write style to convert to script format, the conversion would simply be difficult. Hopefully, this wouldn't be that much of a problem. If the story could be simplified (see the first note) so that it is easier to understand without detracting from the story, this would be preferable.

The story will be following a number of characters, ostensibly in the search of the main for the reason for a number of unusual events, not in the least the cause of death of his great-uncle. This will involve the 'frame' of the protagonist searching out the various other characters for their stories relating to his search, and from there showing from the characters point of view the events they are describing. This would be done in a 'flashback' sort of way, not by filming events then having a narrative over the top.

The most difficult 'setting' would be the portrayal of the city of R'lyeh and Cthulhu, however we might be able to make do using miniatures for shots of the city/god itself and never actually showing actors/surroundings and either R'leyh or Cthulhu in the same shot.

Our equipment is well within range.

Seeing as though we should be able to beg and grovel for most scenes/costumes, the majority of money would probably have to be spent on the miniatures. Using off-cuts provided by contacts in various industries such as that may prove useful.

It was a few weeks later that a number of short stories (all written by the same author) were found, and an email was sent to inquire as to the possibility of adapting his works to film (Feb 23). A reply was received a week later giving his approval.

title: Movie Making Manual/CUS Story


text:
Movie Making Manual/CUS Finance

As a first-time independent studio depending on volunteer workers and conscripts to get by, obviously the amount of funding that could be found would be very little.

Personal Funds
The main source of funds for the project would be coming out of our own pockets. Fortunately, people were relatively free with volunteering things such as time, equipment (film equipment, transport, etc), etc. Actual monies needed for purchase/hire of equipment/space was thankfully not excessive.

Grants
The government grants provided by the Commonwealth of Australia target directors of age above mine, as well as being heavily biased towards directors which have already completed a number of films.

Fundraising
With no large volunteer force, no goods/services and no target consumer group, the fundraising idea was essentially scrapped from the beginning.

Backing
Finding a person or group willing to back this project are much of the same attitude as the government, unwilling to invest in a risky, unproven young group of people.

Thankfully for us, our limited number of contacts meant that we would be able to beg and plead for rights to film at certain locations, in return for a spot thanking the benefactors on the credits. The rest (equipment, transport, etc) was easily managed through our minimal funds.

title: Movie Making Manual/CUS Finance


text:
User:Suruena

I have mainly contributed to:
 * Ada Programming

TO DO list

 * Add note about if there is a place (or link) where one can go to make suggestions or ask newbie questions or comments. For example, I wonder what "fine-grained control over visibility" means. An example would be great! Thanks! lmj

 * New chapters:
 * Representation clauses
 * Interfacing: hardware, other languages
 * High-Integrity, Ravenscar, SPARK
 * Real-time
 * ASIS
 * DSA, PolyORB
 * Static analysis
 * Ada Bug Finder: http://white-elephant.ch/abf/
 * GNAT stack: http://www.safety-club.org.uk/tools_t14.html?opt=tdisp
 * SoftCheck inspector: http://www.sofcheck.com/products/inspector.html
 * http://cfg.w3x.org/projects.html
 * http://www.iste.uni-stuttgart.de/ps/bauhaus/index-english.html
 * http://en.wikipedia.org/wiki/List_of_tools_for_static_code_analysis
 * Experimental compilers (LLVM, Gela)
 * Contributing page:
 * Pragmas/attribute conventions
 * It's not needed to link always to both RM
 * Coding conventions
 * Images: Ada types (SVG)
 * Navigation links to the next and previous pages (like in the Spanish version)
 * Put the list of objectives of the chapter at its beginning
 * Standard libraries:
 * Finish the description of every module
 * Separate standard library units from compiler dependent ones
 * Categorize standard library units: generic, nested packages, optional, implementation dependent
 * Links to the standard types and exceptions ( =  Ada Programming/Libraries/Standard).

 * Improve the see also section:
 * More links to Wikipedia
 * Ada Gems
 * Explicit references to the Ada Quality and Style Guide
 * References to the Ada 95 and Ada 83 Rationale
 * Templates for linking to the covers (ARM, SG, Rationale)
 * Links always to Ada Programming and the parent section? Those links are already available in the navigation bar
 * Put always the Ada Reference Manual subsection, even when it does not apply?
 * Put always links to both the ARM 95 & ARM 2005?
 * Cite templates: AARM, Rationale, Ada-Issues...
 * Template Template:Ada/pragma adds "pragma" and Template:Ada/restriction adds "pragma Restriction". New templates Template:Ada/pragma name and Template:Ada/restriction name?
 * Improved Index
 * Development stages
 * Minimize maintaiment: do not repeat structure in subpages
 * Template:Ada/Operators — Update wikilinks
 * Exercises
 * Scope of every part: tutorial, links, annexes (pragmas, keywords, attributes),... Which parts will be printed?
 * Changes to the language (Ada 83, Ada 95, Ada 2005)
 * List of exceptions defined in the Standard Libraries?
 * Tools
 * GNAT Tools (gnatelim, gnatmetric, ...)
 * Language translators from/to other languages
 * Creation of bindings
 * Dynamic libraries

 * Coding conventions:
 * ARM
 * GNAT
 * GNU
 * SPARK
 * Other projects

 * Language subsets/supersets:
 * Ravenscar profile
 * SPARK programming language
 * ZBRA (Boeing)
 * A#
 * JGNAT

 * More compilers, and tools

 * New wikibooks:
 * Ada for C and C++ programmers
 * Ada for Java programmers
 * Ada for Fortran programmers
 * Ada for COBOL programmers
 * Ada for Pascal programmers
 * Ada (+ GNAVI) for Delphi programmers
 * Ada (+ GNAVI) for Visual Basic programmers
 * Ada for Eiffel programmers
 * SPARK
 * Ada Encyclopaedia

 * Language Quick Reference Guide. Like, but for Ada 2005 and including syntax

Templates

 * Ada/83/Cite AI
 * Ada/83/Cite UI : Uniformity Issues
 * Ada/95/Cite AC : Ada Commentaries
 * Ada/Cite asis issues : ASIS 1999
 * issn in cite journal

Experiments

 * /Template sandbox
 * /Sandbox

Statistics

 * My statistics

title: User:Suruena


text:
User talk:Suruena

Every comment is wellcome in this page, so let me know. I like to discuss the topic before editing an article (sometimes even small changes, well... changes that other people think are small but they aren't), so any constructive suggestion will help.

Book of the month
Programming:Ada has been nominated on Book_of_the_month/September_2005_voting. Prehaps you would like to support our Nomination.

--Krischik 10:34, 2 August 2005 (UTC)

Speedy renaming of Template:Adakw to Template:Ada/kw
Note: Moved from WB:VFD - Aya T E C 16:12, 25 August 2005 (UTC)

This do not require a vote, but I don't known other place to ask to move this page. We are migrating the entire Ada Programming wikibook (formerly Programming:Ada) to the new naming policy, and we have the alias Ada for templates. To avoid polluting the namespace, all the pages of the wikibook will be subpages of those two domain names (Ada Programming and the alias Ada).

We need the help of an administrator to move the Template:Adakw to Template:Ada/kw. After that, the old template Template:Adakw should be deleted, as well as many other template names listed in the Category:Candidates for speedy deletion. Please, do not use any template to indicate the requested renaming or removing because currently the second is a redirection to the first, and it's heavily used. Thank you very much. --surueña 10:27, August 25, 2005 (UTC)

 * I don't understand why you need an administrator to do this. Simply edit the page Template:Ada/kw to contain whatever it is you want it to contain, then mark the old template for deletion when it's no longer required. Perhaps I'm missing something here? - Aya T E C 16:12, 25 August 2005 (UTC)

title: User talk:Suruena