Ahmed El

Amine Djebri
email: djebri.emp@gmail.com

Andrea G B Tettamanzi
email: tettamanzi@univ-cotedazur.fr

Fabien Gandon
email: fabien.gandon@inria.fr

Linking and Negotiating Uncertainty Theories Over Linked Data

Keywords: Information systems → Uncertainty, Web data description languages, Ontologies, • Theory of computation → Incomplete, inconsistent, and uncertain databases Uncertainty, Ontology, Negotiation, Linked Data, Semantic Web

There is no credibility insurance measure for the information provided by the Web. In most cases, information cannot be checked for accuracy. Semantic Web technologies aimed to give structure and sense to information published on the Web and to provide us with a machine-readable data format for interlinked data. However, Semantic Web standards do not offer the possibility to represent and attach uncertainty to such data in a way allowing the reasoning over the latter. Moreover, uncertainty is context-dependent and may be represented by multiple theories which apply different calculi.

In this paper, we present a new vocabulary and a framework for handling generic uncertainty representation and reasoning. The meta-Uncertainty vocabulary offers a way to represent uncertainty theories and annotate Linked Data with uncertainty information. We provide the tools to represent uncertainty calculi linked to the previous theories using the LDScript function scripting language. Moreover, we describe the semantics of contexts in uncertainty reasoning with meta-uncertainty. We describe the mapping between RDF triples and their uncertainty information, and we demonstrate the effect on the query writing process in Corese. We discuss the translatability of uncertainty theories and, finally, the negotiation of an answer annotated with uncertainty information.

INTRODUCTION

The Web is a massive source of information, and the emergence of Semantic Web [START_REF] Berners-Lee | The semantic web[END_REF] technologies allowed the transition from a document-driven view of the Web to a data-driven one. As no credibility insurance measure is taken in most cases, information on the Web cannot be checked for accuracy. In fact, many websites can post whatever they want including biased, subjective, incomplete or uncertain information [START_REF] Hawke | Technological Approaches to Improving Credibility Assessment on the Web[END_REF]. In addition, several issues involving the modeling of uncertain Web Data and services are raised by Benslimane et al. [START_REF] Benslimane | The uncertain Web: Concepts, challenges, and current solutions[END_REF] arguing that uncertainty should be modeled and measured in order to be associated with a piece of information or a service. Such a model prepares the ground for querying, searching, composing and harnessing uncertain data. Semantic Web technologies aimed to offer both structure and sense to the existing Web. However, Semantic Web standards do not offer the possibility to represent and attach uncertainty to such pieces of data while keeping standard semantics without further extension.

Various theories exist and can be used to model different aspects or types of uncertainty. Moreover, the choice of one theory among the others depends on the context and the application. Thus we argue that the bridge between uncertainty and the Semantic Web is crucial because of the dependence of many applications on uncertain data, the existence of many uncertainty theories, the need of interoperability and reusability of uncertain data.

First of all, the definition of uncertainty itself is challenging. It can be epistemic, i.e., stemming from our ignorance (incomplete knowledge, lack of a model) of an entity or process, or ontic, i.e., representing the inherent randomness of a phenomenon or system (a roulette, for instance). In addition, the border between these two types of uncertainty is somehow blurred and arbitrary, in that it depends on our point of view and on the level of abstraction of knowledge representation.

Multiple uncertainty theories are currently applied in Artificial Intelligence in order to reconcile the data, correct them, extrapolate or predict new values or simply assess the degree of uncertainty of information or error comparing to a dataset [START_REF] Dubois | Formal Representations of Uncertainty[END_REF]. Probability theory is but one example, if not the best known and most timehonored, of such theories, which focuses on the representation and manipulation of the ontic uncertainty. Other theories are interested in the quantization (or "granulation") of vague data. As the words "tall" or "cold" do not have a specific measure, they still can be modeled using fuzzy-sets and manipulated using fuzzy logic. A non-exhaustive list of uncertainty theories and their formalization can be found in [START_REF] George | On measuring uncertainty and uncertainty-based information: recent developments[END_REF].

To integrate uncertainty in the Semantic Web stack, two aspects must be taken into account:

• Syntax: uncertainty needs to be represented in order to be queried or published. The format must be machine-readable and interchangeable. The vocabulary proposed by the W3C URW3-XG group [START_REF] Kenneth | Uncertainty Reasoning for the World Wide Web: Report on the URW3-XG Incubator Group[END_REF] enables to annotate data with the type, the model and the derivation of uncertainty. The group offers a limited list of models (Fuzzy sets, rough sets, etc.) with which neither information regarding the quantification of uncertainty nor the specificities of each approach and theory are provided. • Semantics: reasoners are used on top of ontologies to infer new triples (based on OWL profiles) or validate the knowledge base with the schema. Direct semantics in OWL [START_REF]OWL 2 web ontology language document overview[END_REF] is intended to give sense to ontology structures compatible with the SROIQ description logic. Faced with uncertainty as described below and assuming that triples can be annotated with uncertainty values, OWL does not provide the necessary tools to manipulate uncertainty, as, with the generation of new information, uncertainty must be provided too.

Several extensions to the standards are done in the Schema layer itself such as FuzzyOWL [START_REF] Stoilos | Fuzzy OWL: Uncertainty and the Semantic Web[END_REF] or PossOWL [START_REF] Safia | Poss-OWL 2: Possibilistic Extension of OWL 2 for an uncertain geographic ontology[END_REF], while other addressed the 'Unifying logic' layer like FuzzyDL [START_REF] Cross | Fuzzy Ontologies: State of the Art Revisited[END_REF]. Such standards can be extended too to enable inferring new information about uncertainty or keep only triples that follow a defined set of rules in the knowledge base.

Dividino et al. [START_REF] Dividino | Querying for provenance, trust, uncertainty and other meta knowledge in RDF[END_REF] presented a framework extending both RDF and SPARQL to enable meta-knowledge querying. Their framework enables the system administrator to define meta-knowledge properties and for each, should define the intended semantics and the knowledge dimension inside the application in a non-standard format. Thus, neither semantics for the dimensions are publishable, nor semantics for uncertainty itself are provided.

Another problem that arises from the different theories is the relationship between theories and data. Some theories are suitable for specific contexts and applications. Which requires either finding the suitable theory to annotate the data, or a suitable transformation of the existing annotations to fit the requirement. Both reasons raise the question of alternatives and negotiation of content with the data sources, alongside with the possibility for users to represent their preferences in queries.

In this paper, we focus on a generic representation for uncertainty information, one that can be publishable and reusable. We summarize the mUnc vocabulary enabling both the representation of uncertainty theories with their calculi and the annotation of data with uncertainty information. We present an extension to RDF Semantics by giving a contextual meaning to Named Graphs. We offer the possibility to use mUnc alongside with our framework built on top of the Corese Semantic Web engine [START_REF] Corby | Corese: A corporate semantic web engine[END_REF] and using the LDScript function scripting language [START_REF] Corby | LDScript: a Linked Data Script Language[END_REF] based on the SPARQL filter language, to map between triples and their uncertainty metadata, using multiple mapping modes. We discuss the translatability of uncertainty theories and propose to extend mUnc to fill that gap, and then discuss the negotiation of theories over HTTP as a special case of conneg (Content Negotiation) where clients can state their preferences in terms of uncertainty theories and servers can select or translate among theories to serve the best possible answer.

The rest of the article is organized as follows. Section 2 first recalls the uncertainty in information theory and discusses a model for the existing uncertainty theories in the literature. Then, Section 3 discusses the integration of uncertainty in the semantic web, offering a presentation of the mUnc vocabulary, discussing the publishing of uncertainty approaches and their calculi on the Semantic Web, and giving an overview of metadata mapping modes and how to query over uncertain Linked Data. Section 4 is dedicated to the translatability of uncertainty theories and the negotiation details. Related works are discussed in Section 5 while the last section summarizes our work and offers a glimpse over the perspectives.

UNCERTAINTY IN INFORMATION

Information uncertainty can present itself in different forms depending on the domain of the definition. Data itself can be wrong from the source: entry mistakes, deceit, ambiguity, sensor errors, etc. In the context of decision-making, we link uncertainty to the outcome by defining uncertain data as "entries leading to a wrong output". Whether the data are incomplete (or imprecise), vague (or fuzzy, ambiguous), incorrect (or invalid). We find the previous specifications under other definition in the literature. For example, Dubois et al. [START_REF] Dubois | Formal Representations of Uncertainty[END_REF] state that incompleteness, uncertainty, graduality, and granularity are different. They consider uncertainty as a measure for ignorance of the truth of a primitive item of information (proposition, statement, a subset of possible values, etc.), and quantified by a numerical or symbolic token located in the metalevel. In their context, the authors discuss assigning to each primitive item (fine grain) of information A, a number д(A) ∈ {0, 1} which evaluates the confidence of an agent in the truth of a proposition asserting v ∈ A. The presented property is also called confidence function, sometimes capacity [START_REF] Choquet | Theory of capacities[END_REF] or fuzzy measure [START_REF] Sugeno | Fuzzy measures and fuzzy integrals: A survey[END_REF]. This measure preserves:

• monotonicity : If A ⊆ B then д(A) ≤ д(B); • д(⊥) = 0, д(⊤) = 1;
The confidence function can have multiple types according to the data in measure. It can be a possibility, necessity or even a probability measure. The former two measures are dual and the latter satisfies the additivity property. Several theories were established in order to formalize uncertainty, such as probability theory, possibility theory, Dempster-Shafer evidence theory, belief functions, etc. Each of the previous has a set of measures and a logic to read or to infer new values. For instance, possibility theory states that given an event A, which is a set of interpretations or outcomes, the possibility of the event to occur is given by the possibility distribution:

Π(A) = sup I ∈A π (I)
While laying under the Open World Assumption, inconsistency is another challenging aspect of uncertain data where the existence of multiple interpretations of the same knowledge base leading to undecidability. As an alternative of two-valued logic, other types of logic are exploited: defeasible logic based on rules or paraconsistent logic are among those. Such alternatives offer to draw conclusions from partial and conflicting information.

Uncertainty is also affected by the context of the reading [START_REF] Dividino | Querying for provenance, trust, uncertainty and other meta knowledge in RDF[END_REF]. A distributive reading of uncertainty means that statements in a set provide each a portion of the uncertainty value of the whole set. Collective reading, on the other hand, assigns to the whole set an uncertainty measure. The two readings are not exhaustive, an alternative would be inheriting the uncertainty measure of the set.

Klir et al. [START_REF] George | On measuring uncertainty and uncertainty-based information: recent developments[END_REF] observe that dealing with uncertainty consists of four distinct levels: representation, calculus, measurement, and development of methodological aspects of the theory. Our focus in this paper is on the first two levels.

Semantic Web [START_REF] Berners-Lee | The semantic web[END_REF] is meant for machines to understand and infer knowledge as humans do while exploring the Web. Granularizing and linking pieces of data allow machines to provide more relative content and help in the query resolution process. Schema and ontology languages offered a backbone to the existing information by providing the vocabulary of a defined topic, the relationships ruling over the different terms and the semantics allowing to logically establish links in between. Several standards (RDF, RDFS, OWL, SPARQL, etc.) of the Semantic Web technology stack enable the representation and querying of RDF data.

To integrate uncertainty in the Semantic Web, this dimension must comply with the standards and, like any other data on the web, must be reusable and publishable. In this section, we offer an ontology covering uncertainty representation, and we show the possibility of replicating the semantics of uncertainty using calculi represented in the LDScript function scripting language.

mUnc: a vocabulary for uncertainty theories

To enable uncertainty representation on the Semantic Web, we need to opt for an interchangeable format to write uncertainty theories. We propose mUnc 1 , an RDFS ontology for uncertainty theories. mUnc (for meta-Uncertainty) enables publishing uncertainty information based on uncertainty theories. Figure 1 gives an overview of the core concepts and properties of mUnc .

We have adopted the definition of sentence and world proposed in the URW3-XG ontology. A sentence is an expression evaluating a truth value, while the world represents the context in which a sentence is stated. Still, unlike the previous definition, both sentences and worlds can be annotated with meta information. For instance, the sentence ex:S1 representing the triple ⟨ex:StefanoTacconi, dbo:height, 188⟩ referring to the height of the football player is stated in the context of the French language chapter of DBpedia [START_REF] Bizer | DBpedia-A crystallization point for the Web of Data[END_REF], assuming that the latter is consistent [START_REF] Cabrio | Classifying Inconsistencies in DBpedia Language Specific Chapters[END_REF]. Uncertainty is considered a specialization of the general concept of meta. This simplifies the task if any future extensions for other types of metadata such as provenance or trust are proposed. We do not include the concept of Agent, as it can be included using other vocabularies like the W3C PROV 2 Ontology.

An uncertainty theory (Uncertainty Approach) is linked to a set of features and operators. The features are the metrics on which uncertainty theory is based to indicate the degree of truth, credibility, or likelihood of a sentence. Each feature links a value to the uncertainty information. The operators represent the logic to apply to the previous values, while the operations are the implemented calculus for such logic. Other concepts in the URW3-XG ontology like the type or the derivation of uncertainty can be represented as features of an uncertainty approach.

To illustrate the previous definitions, we can annotate the previous sentence using probability theory. It can be represented using only one feature: the probability value. We choose three logical operators to include with the definition: and, or, not. The example in Listing 1: Representing uncertainty with mUnc the listing 1 shows how to assert that a sentence ex:S1 is true with a probability of 0.7. For the sake of illustration, we use reification to attach an IRI to the previous sentence, although no preference about metadata representation methods is stated [START_REF] Frey | Evaluation of Metadata Representations in RDF stores[END_REF].

Uncertainty calculi

Semantic Web ontology languages focus on classification ontological knowledge and do not support the provision of procedural attachments or functions inside ontologies. Our model allows linking the features of uncertainty approaches to their proper calculi (arithmetic, logical, comparison, etc). To represent the calculi, we rely on the LDScript function definition language [START_REF] Corby | LDScript: a Linked Data Script Language[END_REF], a programming language whose objects are RDF entities. It is built on top of SPARQL and relies on the SPARQL filter expression language.

LDScript as a language permits variable declaration, assignment, function call, return, etc. Using LDScript, we can define functions named with an IRI and one or several arguments that are variables in the SPARQL syntax. This enables defining uncertainty operations and linking them to uncertainty features.

To continue with the previous example, considering the fact that the sentence ex:S1 is true with a probability of 0.7, and is stated in a context ex:C1 where all contained facts are considered to be true with a probability of 0.9. The probability of a conjunction of two supposed independent events A and B is given by Equation (1):

P(A ∧ B) = P(A) × P(B). (1)
Such value can be calculated for the user using the function referenced by ex:multProbability and defined in LDScript as shown in the following example:

function ex:multProbability(?pA, ?pB){?pA * ?pB} Therefore, binding the function ex:multProbability(0.7, 0.9) during a SPARQL query execution will return 0.63. The former definition of the probabilistic approach using mUnc can be enriched by linking the IRI of the function to the declared feature, simply by adding the triple:

ex:ProbabilityValue ex:and ex:multProbability.

As stated before, each function is considered as a resource, due to the IRI defining its name. We can store such functions in SPARQL files all over the Web, and access their code using their reference. As mentioned before, uncertainty depends on a context. A single world may accept different interpretations using different uncertainty measurements. Dubois [START_REF] Dubois | Information Fusion and Revision in Qualitative and Quantitative Settings[END_REF] explains that beliefs are the different views upon a single world. We rely on the fact that data is issued by querying n uncertain data sources s 1 , s 2 , ..., s n , each possibly containing several contexts C i j , each representing consistent information. This means that each context contains a set of triples that do not lead to contradictory reasoning. mUnc does not provide an extension of RDF Semantics. Instead, we rely on the SPARQL query language to provide a mapping between sentences and the uncertainty information presented to the user. Moreover, we consider mUnc as an approach to providing definitions of known and custom uncertainty theories, for which we do not provide any specific semantics. The possibility of defining a calculus alongside with the ontology is an alternative to generalize and to reuse of the shared rules between uncertainty theories such as maximizing or minimizing a feature.

We note U S C i j the uncertainty information about the sentence S cited in the context C i j and U C i j the uncertainty information about the context C i j . Each sentence S stated in a context C i j of a source s i , will be mapped to a combined set of pairs (Uncertainty Feature, Uncertainty Value) issued from both sentence and context metadata (noted ÛS C i j ). This requires defining a metadata-mapping mode (see table 1).

The modes depend on the purpose of the application, the data itself, and the semantics of uncertainty theories. In the first mode, only uncertainty information linked to context C i j is considered. The second mode considers only pairs from the lowest level of granularity, while the third mode enables inheriting context metadata but overrides the values for existing features in uncertainty information linked to the sentence.

In this paper, we use a specific meta mapping mode which relies on uncertainty calculus to evaluate a new set of pairs based on both Listing 2: metaList algorithm in LDScript information from sentences and contexts. In the metaList algorithm we implement and use the last mode in the previous table (see Listing 2), The munc:metaList function is declared in the example as "@public". This keyword is implemented in Corese as many others (@define, @visitor, @trace, . . . ) defining specific routines in the Semantic Web engine. The former keyword allows the previous code to be accessed globally in the engine through its reference, and without the need to rewrite the function with each query. The listing 2 translates the metaList algorithm into LDScript. The result of binding this function in a SPARQL query is a string that groups all uncertainty features and their corresponding values from the Universal Uncertainty Information set of the corresponding sentence.

Corese also implements Linked Functions enabling storing LDScript [START_REF] Corby | LDScript: a Linked Data Script Language[END_REF] functions in external SPARQL query files on the Web. Such functions, referenced by IRIs, may be called at the moment of query

C i j = U S C i j if U S C i j ∅ U C i j otherwise Lowest granularity first Override if Exists ÛS C i j = U S C i j ∪ {(F , V ) | (F , V ) ∈ U C i j , ∄V ′ (F , V ′ ) ∈ U S C i j } Multi-level Uncertainty Calculus ÛS C i j = {(F , V ) | (F , V ) ∈ U C i j , ∄V ′ (F , V ′ ) ∈ U S C i j } ∪{(F , V ) | (F , V ) ∈ U S C i j , ∄V ′′ (F , V ′′ ) ∈ U C i j } ∪{(F , V ) | ∃V C i j , V S C i j (F , V C i j ) ∈ U C i j , (F , V S C i j ) ∈ U S C i j , V = eval(Calculus(F ), V C i j , V S C i j )}
execution. The former feature permits publishing and executing the calculi of uncertainty approaches. Additionally, this feature may be extended to allow the capitalization of existing software libraries from other programming languages like C++ or Java.

The Semantic Web engine also allows defining specific routines preceding the query execution. One can integrate query transformation or precalculations of some variables. We implemented the previous meta-mapping mode in extension to the visitor "@metadata" and enabled rewriting SPARQL queries to simplify querying for uncertainty information. Using "@metadata" and with munc:metaList publicly defined, querying for the height of the football player Stefano Tacconi in a data source is as follows.

NEGOTIATING UNCERTAINTY ON THE SEMANTIC WEB

In addition to the previous two-step process leading to the generation of Universal Uncertainty Information Sets alongside with sentences, users may actually have a preference for one theory or another.In this section, we will discuss the translatability between uncertainty theories and how, using HTTP content negotiation (conneg), users may negotiate the theory they want for their results.

Translating uncertainty between theories

Many examples reject the claim that uncertainty can be represented only using probability theory. However, the belief about uncertainty being the lack of information or the deficiencies due to a shortage of knowledge urge researchers to believe that it may be unified, or at least, that the different views may be linked. Dubois et al. [START_REF] Dubois | On possibility/probability transformations[END_REF] stated that transformation is useful in any problem where heterogeneous uncertain and imprecise data must be dealt with (e.g. subjective, linguistic-like evaluations and statistical data). Zadeh [START_REF] Lotfi | Generalized Theory of Uncertainty: Principal Concepts and Ideas[END_REF] cites the example of Dempster-Shafer theory which is a theory of random sets. The latter are a probability distribution of possibility distributions. An interesting analysis of the possibility-probability transformation and its links to graphical models can be found in [START_REF] Benferhat | On the analysis of probability-possibility transformations: Changing operations and graphical models[END_REF].

With the use of our framework, each and every context will issue an answer to the user. If the answers are annotated with the same theory and the same set of features, this enables ranking the results or offers more options to control the results. In the example of search engines, this could support a uniform criteria to order the results shown to the user. However, on an open Web where several open sources are queried, the results might use different features from different theories.

A translation must offer to transform a Universal Uncertainty Information Set ÛS C i j of a sentence S annotated following an uncertainty approach T 1 , to another set annotated with a different uncertainty approach T 2 . The translatability of theories should take into account several issues such as the symmetry, the reversibility, and the possible loss of information.

To fit in with the previous requirements, we define a translatability relationship between two uncertainty theories as follows: Definition 4.1. A theory T 1 has a translatability relationship with a theory T 2 , if there exists a mapping M : F T 1 → F T 2 from the set of features F T 1 represented in theory T 1 to the set of features F T 2 represented in theory T 2 such that every possible feature of F T 1 is mapped to a set of feature of F T 2 semantically coherent with the uncertainty initially expressed in T 1 . We note

T 1 >| T 2 .
The former definition is valid for all theories that have a relationship allowing the conversion of features from one theory to another, regardless the loss of information. In case the conversion does not generate a loss of information allowing the reversibility of the operation, we define the relationship as follows: Definition 4.2. A theory T 1 has an ideal translatability relationship with a theory T 2 , if T 1 is translatable to T 2 (T 1 >| T 2 ) and there is no loss of information in the translation. We note T 1 ≫ T 2

We should mention that an ideal translatability might not be reversible, regardless the semantics of the translatability. The loss of information disables the backward operation. If the other case is considered, where we have no loss of information, then we can define a full translation as follows: Definition 4.3. A theory T 1 has a full translatability relationship with a theory T 2 , iff T 1 ideally translatable to T 2 (T 1 ≫ T 2 ) and, inversely, T 2 ideally translatable to T 1 (T 2 ≫ T 1 ). We note T 1 ⊗ T 2 .

Using our mUnc vocabulary and the framework previously proposed, we are able to formalize the translation (if it exists) between the different theories. For this, we extended mUnc with the set of the following properties:

• munc:hasTranslation (definition 4.1)

• munc:hasIdealTranslation (definition 4.2)

• munc:hasFullTranslation (definition 4.3)

These QNnames respectively identify the previous definitions. We can note that full translatability being an equivalence relation it allows to form equivalence classes by transitive closure i which we have translatability with no loss of information from a theory T i to any other theory T j of its class. We note this set TC U (T i ).

To illustrate the previous extension, we propose to represent the example proposed in [START_REF] Ben Slimen | Probabilitypossibility transformation[END_REF] about the Optimal Transformation (OT) from probability to possibility. We declare a translatability relationship between probability theory ex:Probability and possibility theory ex:Possibility representing the two different uncertainty theories. We enrich the data source with the triples below, where ex:translateProbaToPoss is an LDScript function.

Negotiation of Uncertainty Headers

Based on the previous model we can now support the possibility of negotiating answers annotated with different uncertainty theories. Content negotiation can be based on HTTP headers or non-HTTP methods such as query arguments in IRIs. Following the W3C working draft proposed by Svensson et al. [START_REF] Svensson | HTML 5.3. W3C Working Draft[END_REF] we propose that clients may negotiate a representation annotated with a specific uncertainty theory, using q-values to express their preference regarding the uncertainty theories they are to receive. Since uncertainty theories are already defined using mUnc and named with IRIs, both server and client can exchange and verify the conformity of their options. We propose to handle three use cases:

(1) Uncertainty information exists in the queried source in one or many requested uncertainty theories. We answer with the first theory selected by the user. In the example, uncertainty information is issued from a context annotated with evidence.

GET /some/resource HTTP/1. We note that the selection of a suitable translation starts from the transitive closure of full translationsTC U (possibility) offering more information and graduates to the normal translatability relationship. [START_REF] Berners-Lee | The semantic web[END_REF] The data source has no information about the theory and no available translations, we answer the user with the existing information. The default uncertainty information proposed by the server is returned in such case.

GET /some/resource HTTP/1.1 Accept: text/turtle;q=0.9;uncertain="http://example.org/probability", text/turtle;q=0.7;uncertain="http://example.org/evidence"; HTTP/1.1 200 OK Content-Type: text/turtle;uncertain=<http://example.org/possibility>; default=true

RELATED WORKS

mUnc can extend the work done by Cabrio et al. [START_REF] Cabrio | A RADAR for information reconciliation in Question Answering systems over Linked Data 1[END_REF] by enriching the proposed fuzzy labeling algorithms with definitions of other uncertainty theories that can be more suitable to the data. The Linked Data sources can adopt this approach to enrich federated queries with uncertainty information and, progressively, build a consensus-based Linked Data source. A set of other applications such as fake news detection (definition of a theory and logic for fake news), argumentation-based systems and even community-based data sources such as DBpedia can use mUnc to enrich their future content with uncertainty information. Furthermore, mUnc shares the same objectives as the W3C Credible Web community group [START_REF] Hawke | Technological Approaches to Improving Credibility Assessment on the Web[END_REF] as for exchanging data which bears directly on credibility assessment while keeping standardization in data interchange. The focus on uncertainty translatability was mainly in AI-based applications. We point that Semantic Web requirements in term of interoperability and information usage are very important and the suitability of uncertainty theories to different types of data and applications need to be further discussed.

CONCLUSION AND PERSPECTIVES

In this paper, we discussed the representation and publication of uncertainty on the Semantic Web. We presented a vocabulary allowing the representation of uncertainty theories and the annotation of sentences using the Semantic Web standards. We explained the publishing of reusable uncertainty calculus using LDScript. We also offered the possibility to translate between uncertainty theories and to negotiate uncertainty information following a specific theory.

Uncertainty representation is the first step of a long process, including the preliminary calculus of uncertainty values and the propagation of uncertainty among interconnected Linked Data sources. The translation process is also a first step enabling, to some extent, merging uncertain data annotated using different uncertainty approaches. In our future work, we would like to implement context overlapping, allowing the selectivity inside the source between contexts and the optimization of the storage. We would also study the relationship between data, applications, and uncertainty theories that are used.

Figure 1 :

 1 Figure 1: Overview of the mUnc ontology and its core concepts

  @metadataprefix ex: <http://example.org/> . prefix munc: <http://ns.inria.fr/munc/> .SELECT * WHERE { ex:StefanoTacconi dbo:height ?o } Listing 3: Query rewriting using visitors implementing the access to uncertainty information

Fig- ure 2

 2 shows the extension, where each property of the previous set has for domain an uncertainty approach, and for range a blank node pointing to both the destined theory and the IRI of the translation function, written in LDScript.By definition, the translativity properties have a number of algebraic properties: Lemma 4.4. (transitivity of translatability) Let T i , i = 1, 2, 3 be three uncertainty theories. If T 1 >| T 2 and T 2 >| T 3 then T 1 >| T 3 . Lemma 4.5. (reflexivity of translatability) Each uncertainty theory has a full translatability with itself. We note T ⊗ T . Lemma 4.6. (Symmetry of full translatability) Let T 1 ,T 2 be two uncertainty theories. If T 1 ⊗ T 2 then T 2 ⊗ T 1 . Lemma 4.7. (full translatability is an equivalence relation) because it is transitives, symmetric and reflexive.

  ex:Probability munc:hasTranslation ex:Possibility. ex:translateProbaToPoss munc:translateFrom ex:Probability. ex:translateProbaToPoss munc:translateTo ex:Possibility.

Figure 2 :

 2 Figure 2: Extending mUnc ontology with translation properties

Table 1 :

 1 Metadata mapping modes

	Considered level of granularity	Mode	Explanation
	Context	Imposed	ÛS C i j = U C i j
	Lowest granularity only	Overriding	ÛS

ACKNOWLEDGMENTS

We thank Olivier Corby for his insights about Corese and LDScript. This work is supported by the Algerian government.


Marie-Carmen Monje

Christelle Privat

Valérie Gastine

Françoise Nepveu
email: nepveu@cict.fr

Determination of ethylphenol compounds in wine by headspace solid-phase microextraction in conjunction with gas chromatography and :flame ionization detection

Keywords: Ethylphenols, Red wine, Headspace analysis, Solid phase microextaction, Gas chromatography

Headspace solid phase microextraction (HS-SPME) was investigated as a solvent-free alternative method for the extraction and determination of 4-ethylphenol (EP) and 4-ethylguaiacol (EG) in red wine by capillary gas chromatography with !lame ionization detection (FID) and compared to liquid-liquid extraction.

For HS-SPME, better results were obtained with saturated sodium chloride samples, at 55 'C, nsing a 85 µm polyacrylate fi ber. An absorption time of 40 min was needed to reach the absorption equilibrium for EG. This 40-min duration corresponds to the beginning ofEP equilibrium and was selected for the experiments. In these conditions, the calibration graphs were linear in the range 5-5000 µg 1-1 and the sensitivity was nearly the same for the two compounds. The detection limits were in the low µg 1-1 range. In mode! wine solutions, result obtained with the liquid-liquid extraction method exhibit a linear calibration bet ween 25 and 10,000 µg 1-1 with a detection limitofl µg 1-1 , but, the relative standard deviations oftheEP and EG result in the low concentration range ( <50 µg 1-1 ) are higher !han those obtained by HS-SPME (15% compared to 2% for EP and 12% compared to 5% for EG). Taking into account the numerous volatile compounds in wine, HS-SPME is a rapid and valid alternative technique for use in the determination of ethylphenols at trace levels.

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Introduction

Among the volatile phenols contributing to the aroma of wine, some may be produced by yeasts. For exarnple, Brettanomyces/Dekkera produces 4ethylphenol (EP) and 4-ethylguaiacol (EG) (Fig. 1) [1][2][3][4]. The aroma associated with EP in red wine has been described as "animal"-, "horsy" -, '"barnyard 1 ' -, "medicinal"-like [5][START_REF] Olsen | Proceedings of the Wine Industry Workshop[END_REF][START_REF] Towey | [END_REF] while EG has been described as a "smoky"-, "clove"-like arnma [5,[START_REF] Olsen | Proceedings of the Wine Industry Workshop[END_REF][8][9][10]. These volatile phenols are ahnost ubiquitous in red wines but their concentrations vary considerably giving, at higher levels, heavy and undesirable odors. So, winemakers have to control yeast development and understand how winemaking and storage processes may affect the concentration of EP and EG. For this purpose, procedures have to be developed to facilitate rapid wine sarnple analysis both in the laboratory and on-site.

Analysis of volatile compounds such as aromas, pollutants and solvents is a continuous challenge in the food and beverage industries. Although varions ques such as gas chromatography (GC), liquid chromatography (LC) and their combination with mass spectro-metry (MS), can be used for food analysis, most analytical instruments cannot handle the sample matrices directly. Consequently, several multiextractions, have been developed but they are timeconsuming, involve expensive and hazardous solvents, and produce low accuracy due to the manual handling. Some of them have been adapted for the determination of volatile phenols in wine. Chatonan extraction with dichloromethane coupled with a to analyze pentane:diethylether (2:1) extracts of wine containing EG and EP [11]. In both cases, these methods were useful to correlate ethylphenol concentrations with Brettanomyces/Dekkera contamination and determine the ethylphenol concentration range in a large variety of wine samples [1,2,11].

Recently, headspace solid phase microextraction (HS-SPME) coupled to GC has gained a sturdy repupreviously used for the analysis of volatile comused to determine aroma compounds in wine but has still not been optimized for ethylphenol determination [11,15,16].

The purpose of this work was to optimize the through calibration graphs for model wine solutions and through quantitation of volatile phenols in red wine.

Experimental

Reagents

The gases for chromatographic analysis were purchased from AGA (Toulouse, France). The polyacrylate 85 m France). Ethanol was obtained from SDS (Peypin, France), and 4-ethylguaiacol from Lancaster (Strasbourg, France). All other reagents were purchased Fallavier, France).

Preparation of model wine solution and standard solutions

The model wine solution was prepared in distilled water containing 11% (v/v) ethanol, 6 g l 1 glycerine, 2.5 g l 1 tartaric acid, 3 g l 1 lactic acid and 1 g l 1 potassium phosphate. The pH of the resulting solution was 3.

A standard stock solution of each volatile phenol (EP, EG and 3,4-dimethylphenol) was prepared by mass at 1000 mg l 1 in the model wine solution. Secondary standard solutions of EP and EG were prepared by dilution of the stock solution in the model wine solution to obtain concentrations of 10 mg l 1 .

Procedure

Calibration

Calibration graphs for EP and EG in the range g l 1 were plotted in model wine solutions using primary and secondary standard solutions. 3,4-Dimethylphenol was used as internal standard (IS) for calibration and quantitation of the ethylphenol compounds. Its concentration in the sample was 5 mg l 1 for the HS-SPME procedure and 10 mg l 1

Chromatographic analyses

Analyses were carried out on a DANI GC 1000 gas chromatograph equipped with a hydrogen (30 m 0 32 mm 0 25 m) from Alltech (Templeuve, France). Nitrogen was used as carrier gas at a carrier gas and the GC apparatus to trap oxygen and water present in the nitrogen.

Solid-phase microextraction procedure

5 ml vial containing NaCl (1 g), 3,4-dimethylphenol (5 mg l 1 ) and sealed with a septum-type cap. HS-SPME was carried out under magnetic stirring. The solutions were heated to 55 C and extracted with a 85 the vial headspace. The compounds were desorbed by Volatile phenols were extracted from samples according to the method previously described by Chatonnet and Boidron [5]. First, the IS (3,4-dimethylphenol, 10,000 g l 1 ) was added to the sample (50 ml). Then 15 g of ammonium sulfate were added and well dissolved prior to extraction three times with 10, 5 and 2.5 ml of dichloromethane, successively. The combined organic extracts were centrifuged (10,000 for 5 min) to break any emulsion, and washed twice with 5% sodium bicarbonate, then extracted twice with 25 and 12.5 ml of 0.5% sodium hydroxyde solu-pH 1 by adding 20% hydrochloric acid, then extracted with 7.5, 2.5 and 2.5 ml of diethyl ether, successively. The combined ether extracts were slowly concentrated to 0.5 ml at room temperature by evaporation under a chromatographic analysis was performed by injecting 1 l of the concentrated extract.

Statistical analysis

Results are expressed as means S E of n experiments. Statistical comparisons were made using the t-test. The P-value was considered 0 05.

Results and discussion

The study and optimization of HS-SPME conditions need about 10 experimental parameters to be taken into consideration. However, there have been numerous papers written on the extraction of semivolatile and volatile molecules from food and beverages, as well as from waters using SPME. These reports allowed us to rationalize the study on selected parameters In preliminary studies, two types of different poshowed the best results (data not shown) which is in agreement with previous studies [12,13,17]. Moreover, two main types of SPME procedures were compared: the headspace (HS) and the direct immersion (DI) procedures. In model wine solutions, on the analyte: the HS-SPME procedure is more sensitive for EG whereas DI-SPME seems to be better for EP and IS (data not shown). However, red wine has a very complex matrix and also contains non-volatile compounds. For these reasons, we selected the HS-SPME procedure to increase the compared to DI-SPME, as reported in the literature [12,13,15,17].

Optimization of HS-SPME parameters

The effect of ionic strength on the headspace ethyl phenol quantitation was determined by saturating a sample with NaCl (1 g) under magnetic stirring. Results obtained for EP and EG at 55 C with or without NaCl are shown in Fig. 2. Whatever the compound studied, saturation of the samples with NaCl increased peak areas about four times indicating an increase of well known that supersaturation of the sample with salt is more effective for the extraction of analytes onto decreases the solubility of analytes and thus increases rated with 1 g of NaCl have been used for subsequent experiments.

Effect of temperature

HS-SPME is controlled by the equilibria between the sample and the headspace and between the by temperature.

To optimize HS-SPME, the temperature of the solution was varied from 25 to 75 C using an absorption time of 40 min. Results concerning EP and EG are shown in Fig. 3. Peak areas of IS exhibited the same variations as EP (data not shown).

Below 55 C, peak areas of volatile phenols increased with the sample temperature indicating an increase of the headspace concentration. The peak area of EG was a maximum at 55 C and then decreased while EP and IS peak areas continued to increase with increasing temperature. These different temperature At the same time, the responses for the higher boiling compounds, EP and IS, increase corresponding to These observations are in agreement with previous studies with different volatile compounds [16,19,20] and demonstrate that for a complex matrix, selection of an optimum sampling temperature depends strongly on which analytes are of greatest interest or require the greatest sensitivity. Moreover, as reported by Chatonnet et al. [2], the EG:EP ratio in red wine is 1:10.

C. Each test compound is dissolved at 2 mg l 1 in a 2 ml model wine solution containing 1 g of NaCl. Each point represents the mean ( S.E.) of three independent experiments.

Considering these data and experimental results, a temperature of 55 C, giving the highest sensitivity for EG, was selected for subsequent experiments.

with stirring C for the ethylphenol mixtures are shown in Fig. 4. The responses of the compounds are affected by increasing the sampling time from 20 to 80 min corresponding ing [13,15,16]. The equilibrium period required for EG was the shortest (40 min). For EP and IS, the less volatile components of the mixture, increasing the sampling time from 40 to 80 min increased the response, indicating slower equilibration. However, for EP the increase in response between 40 and 60 min was less than between 20 and 40 min (18% versus 82% increase) indicating the beginning of the equilibration. that in saturated salt solutions, the time required for phenols to reach equilibrium was >60 min [12,13]. Indeed, this is probably due to the slow diffusion of the compounds through a saturated salt solution. Although an absorption time of 40 min is not enough to reach equilibrium for all compounds, it was chosen for the subsequent experiments since it gave the highest sensitivity for EG. Moreover, this time was approximately equivalent to that required for a gas chromatographic run.

extraction for the model wine solution

Validation of extraction methods coupled with GC analysis were performed for both EP and EG in the presence of IS with three independent calibration graphs. The calibration graphs obtained by g l 1 (10 data points, 3). These calibration graphs present the following regression co-2 0 998 and 0 00025 for EP; 2 0 989 and 0 00024 for EG (where y is the peak area, in arbitrary units, and x is the concentration, in g l 1 ). The sensitivity represented by the slopes of the calibration graphs, was nearly the same for EP and EG. When the EP and EG concentrations were 50 g l 1 , the R.S.D. was 6% for EP and 5% for EG ( 3 independent determinations). When the EP and EG concentrations were 5000 g l 1 , the R.S.D. was 3% for EP and 2% for EG ( 3 independent determinations).

were linear over a higher concentration range g l 1 , nine data points, 3) for EP and EG. The graphs present the following regres-2 0 999 and 0 00010 for EP; 2 0 997 and 0 00008 for EG. The sensitivity of the method (calibration slope) for EP is a little higher than for EG. When the EP and EG concentrations were 50 g l 1 , the R.S.D. was 15% for EP and 12% for EG ( 3). When the EP and EG concentrations were 5000 g l 1 , the R.S.D. was 2% for EP and 5% for EG ( 3 independent determinations). Whatever the compounds and methods used, the detection limits (signal to noise ratio 3) were in the low g l 1 range.

For EP and EG, the reproducibility of analysis at 50 and 5000 g l 1 The ability of both methods to quantify EP and EG was evaluated on two different wines, one rich in ethylphenols (Wine-1), and one poor in ethylphenols (Wine-2). The results are shown in Table 1. Whatever the wine tested and the method employed, the concentrations determined for EP were similar ( 0 43 in Wine-1 and 0.2 in Wine-2,

3) indicating the good selectivity of both methods for EP. However, the variability of EP concentration in Wine-2 was higher (3%). For EG concentrations, whereas there was no fact that the EG concentration in Wine-2 is close to its lower quantitation limit (25 g l 1 ) evaluated by note that this quantitation limit was determined in model wine solutions, a less complex matrix than red wine. Moreover, the variability of EG concentration (19%) than HS-SPME (6%).

The important variability of the results obtained by compounds, could be due to the uncontrolled temperature of extraction. In this procedure, the volatility of In contrast, HS-SPME always allows the same temperature (55 C) to be used, thus limiting this problem.

Conclusions

The HS-SPME procedure is a solvent-free method presenting major advantages: simplicity, rapidity, high sensitivity and small sample volume. The HS-SPME method is appropriate for the quantitative analysis of ethylphenols in red wine even if the gas chromatograms obtained by HS-SPME present more peaks HS-SPME method can be combined with an autosampler. We are now examining the possibility of developing a fast monitoring method for the routine analysis of volatile phenols in wine for quality management in winemaking.

Fig. 1 .

 1 Fig. 1. Structures of 4-ethylphenol and 4-ethylguaiacol.

  1 . A split/splitless injector was used in the splitless mode (ratio 1:25) and maintained at 250 C. The detector was kept at 250 C. The column program was as follows: initial temperature 40 C at 10 C min 1 , kept at 180 C for 0.5 min, second ramp to 205 C at 3 C min 1 , kept at 205 C for 0.5 min, third ramp to 230 C at 10 C min 1 , kept at 230 C for 5 min.

Fig. 2 .

 2 Fig. 2. Ionic strength effect on HS-SPME of volatile phenols (5 mg l 1 C (extraction time: 40 min). Each result represents the mean ( S.E.) of three independent experiments.

Fig. 3 .

 3 Fig. 3. Temperature effect on HS-SPME of volatile phenols (2 mg l 1 ) from a 2 ml model wine solution containing 1 g of NaCl by a S.E.) of three independent experiments.

  were determined ( 3) with different model wine solutions containing IS. At the lowest concentration, both compounds exhibited lower R.S.D. values within the SPME procedure than 2.5 times less) while the R.S.D. values were similar for the two methods at higher concentrations. It is important to note that all these optimization experiments were done with ethylphenol compounds by the two techniques was then checked in red wine. extraction for red wine extraction methods was compared for red wines quantitation. As shown in Figs. 5 and 6, the retention times of EG and EP were 18.3 min for EG and 20.6 min for EP. These values are close to those obtained in model wine solutions not shown). a given wine (Wine-1), EP and EG peaks were procedure to concentrate volatile phenols. Then, the tion presented fewer peaks than that obtained after HS-SPME.

(

  Wine-1). EP: 4-ethylphenol; EG: 4-ethylguaiacol; IS: internal standard. difference between the two methods for Wine-1 ( 0 68, 3) indicating the good selectivity of both dure showed lower values than HS-SPME ( 0 05, 3) for Wine-2. This can be explained by the HS-SPME of ethylphenol compounds from red wine (Wine-1). EP: 4-ethylphenol; EG: 4-ethylguaiacol; IS: internal standard.

Table 1

 1 Determination of EP and EG concentrations ( g l 1 ) in two wines Concentrations of EP and EG are expressed as means ( S.E.) of three independent experiments.

			a
	Extraction	Wine-1		Wine-2
	method	EP	EG	EP	EG
	HS-SPME	3536 ( 152) 280 ( 8) 109 ( 2)	56 ( 2)
		3350 ( 84)	247 ( 8)	90 ( 15) 36 ( 4)
	extraction

a

Acknowledgements

We thank Dr. I. Seiller and the personnel of the Microbiology Department, ENFA (Toulouse, France), for technical assistance and for providing instrumentation for chromatography.


Ricardo Barros Sampaio
email: rsampaio.br@gmail.com

Anass El Haddadi
email: anass.elhaddadi@gmail.com

Wahiba Bahsoun
email: wahiba.bahsoun@irit.fr

Bernard Dousset
email: bernard.dousset@irit.fr

Data and Social Network Analysis on Malaria

Keywords: Scientific and technological vision, bibliometric, graphs, social networks, geo-strategy, visualization, interactivity, neglected diseases, malaria I

In order to understand the existent and in formation relationships that might surface from scientific publications it is necessary to use methods for data and network analysis, besides the concepts of information flow and knowledge creation within organizations. The aim of this work was to show the application of tools and methods for data and network analysis and how those methods can bring forward proposals to improve the understanding of a specific disease, Malaria, within the scientific publications. The study of relational data retrieved from different databases might be elucidating in an environment, health system, which happens to be quite complex due to its many elements involved. How these elements interact within each other in order to fulfill the need to serve the population needs to be understood. The study of scientific publications is just one of the areas that might help decision makers have a better grasp on how to deal and focus on the needed areas.

INTRODUCTION

The use of data and social analysis tools has become a very important technique in order to find and retrieve relevant information. With the use of massive storage spaces and great amounts of data new technologies for sorting, retrieving and visualizing this information is necessary. The use of data analysis on scientific publications facilitates and bring forward information that could not been found with ancient data analysis methods.

The research universe defined for this study is Malaria disease contained in the so-called neglected tropical diseases (NTD). The term NTD refers to a group of diseases that are especially endemic in low-income populations living in tropical and subtropical countries. However, up to this date, there are no clear or agreed definitions of what a neglected disease is constituted by. According to the 2010 report from the World Health Organization (WHO) "Working to Overcome the Global Impact of Neglected Tropical Diseases" the NTDs affect more than 1 billion people around the world.

The known diseases are defined by the WHO as types I, II and III where Malaria is considered a type III disease. The definition includes the wealth of a country between rich and poor, the state of its development between developed and developing and most importantly a measure of the burden of diseases by the incidence of the disease within the population (CEWG 2012). The definitions themselves are combined such that:

Type I diseases: are incident in both rich and poor countries, with large numbers of vulnerable populations in each, example cancer.

Type II diseases: are incident in both rich and poor countries, but with a substantial proportion of the cases in poor countries, example tuberculosis.

Type III diseases: are those that are overwhelmingly or exclusively incident in developing countries, example malaria.

With regard to investments in the area, those diseases do not receive adequate attention for its treatment and eradication, which perpetuates the condition of people who are exposed to them [START_REF] Morel | Coauthorship Network Analysis: A Powerful Tool for Strategic Planning of Research, Development and Capacity Building Programs on Neglected Diseases[END_REF].

It's imperative that new tools and approaches being developed focus on facilitating public policy planning and foster the management of innovation in countries' public health systems [START_REF] Vasconcellos | Enabling Policy Planning and Innovation Management through Patent Information and Co-Authorship Network Analyses: A Study of Tuberculosis in Brazil[END_REF]. Recent studies on Leishmaniasis [START_REF] Ramos | Bibliometric analysis of leishmaniasis research in Medline (1945-2010)[END_REF] and Chagas disease [START_REF] González-Alcaide | Scientific authorships and collaboration network analysis on Chagas disease: papers indexed in PubMed (1940-2009[END_REF] used the Medline database and helped us with the understanding and alignment of our work. A comparison with results for co-authorship research [START_REF] Newman | Coauthorship networks and patterns of scientific collaboration[END_REF] has also been made in regards to the same database analyzing the relationships within the scientific community.

According to the World Malaria Report [6] in 2010 there were an estimated 219 million cases and 660.000 deaths. Malaria is present in over 100 countries and threatens half of the world's population. In sub-Saharan Africa malaria is the single largest cause of death for children under five. However there is an expectation of 75% reduce in malaria cases by 2015 compared to year 2000 due to investments and collaborative work. Investments in the area have grown significantly in the past years from less than US$ 100 million in 2000 to US$ 1.71 billion in 2010. The increased investments have their consequences on the significant growth of research on the area. Figure 1 shows a world map of malaria endemic countries such that the countries in orange and red are the most affected ones.

II. DATA ANALYSIS OF A NEGLECTED DISEASE

The use of data analysis for neglected diseases through bibliometric or social network on scientific publications is a way to help managers on different positions cope with the necessity to view and take decisions on those areas based on reliable information regarding research. The use of tools and technologies on those areas has helped to find and manage information on large sets of data.

A. METHOD FOR DATA TREATMENT

For the data treatment and analyses we used the platform software Tetralogie. The software allows several and remote users to conduct strategic analysis from heterogeneous textual data through analysis of conventional and innovative methods. With statistical methods and exploratory data analysis methods, it shows strategic information such as actors identity, mobility, emergence and evolution of topics, concepts and terminology. Another feature of the software is that it allows graphical visualization for understanding human activities and their interactions as well as their evolution in a decision-making perspective. It combines static and dynamic visualization where the static aspect is based on a representation space, in which the precepts of graph theory are applied. The dynamic visualization applies features and capabilities to further understand evolution of the data and facts in respect to time. Tetralogie allowed us to identify the role of data within the structure, analyzing their neighborhood, filtering, k-core, transitivity, back to the source documents, to partition the graph or to focus on its structural features [START_REF] Gay | Cartographie de réseaux d'alliances et analyse stratégique[END_REF].

B. DATA RETRIEVED

For this study we have used the PubMed database, which accesses the Medline database. PubMed is a free database accessing references and abstracts on life sciences and biomedical topics. The access to the database occurred during the months of May and June, 2013. We have decided to take into consideration only the publications and patents from 2000 to 2012 included.

The research has been made taking into consideration authors of articles, countries where those authors were from, journals used for the scientific publications besides MeSH (Medical Subject Headings). Data files were retrieved from PubMed with the term/descriptor "Malaria" for title and abstract.

In regards to the number of publications for the years studied we have retrieved 27.880 articles containing a total of 51.720 authors from 121 different countries. The number of journals added up to 2.330, however only 412 of those had 10 or more articles in the area. The number of MeSH words filed on those articles were 8.890 from which the top 09 MeSH words were mentioned more then 10.000 times.

III. NETWORK ANALYSIS

The pattern of relationships of a system can be represented as a network where system components are the vertices and the connections between them ties. The structure of these networks and the specific pattern that might arise from its analyses has a major impact on the behavior of the system and its informational structures. Understand patterns in existing sociotechnical networks and to intervene in these networks is one of the possibilities raised by this study.

The Network Analysis or Social Network Analysis (SNA) includes an extensive set of methods for assessing the structure of social groups [START_REF] Nooy | Exploratory Social Network Analysis with Pajek[END_REF], [START_REF] Wasserman | Social Network Analysis: Methods and Applications[END_REF]. The use of SNA enables differentiated perception of a social environment. With this tools a new phenomena can be investigated and new hypotheses can be established. The SNA is derived from both the social sciences and the exact sciences, and their applications are enhanced by the ability to compute mathematical calculations and modeling as well as the expansion of communication networks and the use of digital environments.

The SNA brought interest from researchers in different fields of knowledge. In attempt to understand the impact of the relationship structure on networked environments, SNA gave rise to several analysis methods, which were based on the relationships between individuals or other type of elements, in a structured shaped network.

A social network consists of one or more finite sets of actors (vertices) and the defined relationships between them. The actors in a social network can be either individuals or companies, analyzed as individual units, as well as collective social units, for example, departments within an organization, public service agencies, nation-states of a continent or the world. The concept of actor is flexible, allowing different levels of aggregation, which allows its adaptation to different research problems [START_REF] Wasserman | Social Network Analysis: Methods and Applications[END_REF].

A network is a simplified representation of a system that is reduced to an abstract structure consisting largely of patterns of relationships among the vertices. The vertices and lines can have extra information like names or size so that more details about the system can be captured. However, even with this extra information still much information is lost in the process of reducing an entire system on a network representation [START_REF] Newman | Coauthorship networks and patterns of scientific collaboration[END_REF].

Besides these scholars, work done in the area of biotechnology and in the area of neglected diseases in NTDs endemic countries has been a reference to this scientific work. Dr. Carlos Morel Medici has published a series of articles using the methods of SNA to relate co-authorship in scientific research [START_REF] Morel | Coauthorship Network Analysis: A Powerful Tool for Strategic Planning of Research, Development and Capacity Building Programs on Neglected Diseases[END_REF]. In France Dr. Brigitte Gay has conducted research on partnerships in the biotechnological industry and how these partnerships can define the success or failure of their companies [START_REF] Gay | Innovation and network structural dynamics: Study of the alliance network of a major sector of the biotechnology industry[END_REF], [START_REF] Gay | Firm dynamic governance of global innovation by means of flexible networks of connections[END_REF].

IV. RESULTS

The first step on our study was to understand the growth in regards to the numbers of publications and authors per year as seen in figure 2.

As can be seen on the figure, more publications have a direct relation to increased number of authors. With more and new researchers involved the area tend to be more dynamic and new finds could help solve issues related to the disease. The total number of publications and authors has increased steadily the past 13 years which might collaborate with the investments in the area and the reduce expectation of malaria cases. The total number of publications has almost double on those years while the number of authors in the area has increased almost 2,5 times.

A. COUNTRIES, JOURNALS AND AUTHORS PUBLICATIONS

In regards to country publications we can see a growth in interest, which has more than doubled the number of publications since 2000 in most countries but more so on countries with endemic problems such as India, Thailand, Nigeria, Kenya and Brazil. Those countries are becoming more involved in research, and due to its close relation to the disease there is an urge to find solutions. However, countries such as USA, UK, France and Australia still play an important role on research and publications. The financial support received by those countries on research and technologies has helped millions of people. Figure 3 shows an overview of the world map in terms of publications on PubMed. The countries with a lighter green have more publications than countries with darker green. In the past few years countries such as India, Brazil and China, which are part of the "BRICS", have had an important role over those researches.

Another area for our study was the Journal' publications which is also an important feature to be analysed in scientific research. More journals interested in the area directly result in more publications and more research. Table 1 shows the journals with the highest number of publications over the past 13 years. The journals with higher degree of interest in the area, in regards to the number of publications are Malaria Journal in the top of the list with 2.110 publications over the period followed by The American Journal of Tropical Medicine and Hygiene with 1.209 publications. As for the authors we can notice on table 2 that some of them have a higher number of publications, much greater than the average researcher on the area. The top 10 authors for example have more than 150 publications (11 are shown in the table) over the period while the mean number of publications per author is 2,3. This demonstrates a high concentration or centrality in a network. One problem that we might encounter in researching over authors publications is theirs alias or different formats for ones own names such as smith t or smith .t .a which might characterize the same person. To partially resolve the problem, Tetralogie creates a synonymous table for authors and other elements as needed. For the data analysed there were a table with 7.036 author synonymous, so for those authors all their publications were added up to one name. The understanding of the participation of those authors during the period is also important. Figure 4 shows an analysis on individual authors called "Histogram of the Individual" over the period from 2000 to 2012, where the red bar demonstrate the number of publications for that particular year. As an example we have chosen authors with different characteristics such as author that has just come into the field in regards to publications (top graph) and only has published on the past 5 years (2008 to 2012). Authors that have not published during a period of time (middle graph) (dud not publish on 2006, 2007 This type of analysis can be used to verify individual behaviours on specific authors, such patterns on publications.

B. SOCIAL AND MORPHOLOGICAL NETWORKS ANALYSIS

As we have mentioned on this study new forms of analysis must take place in order to better understand the information that is available. One of those forms is the network analysis, which consists of an analysis over the relationship amongst people or words over those publications. The most common and already in use over scientific publications is the social network analysis where authors, which have co-author a paper, demonstrate a relationship and so can be analyzed as such. One important issue related to visual analysis is the ability to understand what has been presented, which might become a problem when we are dealing with large sets of data such as the case of scientific publications. Before we can make any sense of what might appear on a network graph it is necessary to sort the data that is most important or relevant at the moment.

Figure 5 shows 3 tables for an author relationship with a lower density for the ones in the right compared to the previous one in the left. The left most table on figure 5 shows the authors with 5 or more publications, over the period study, and its relationships. An author defined on the x axis, that have coauthor a paper with another author, on y axis, will have a red dot over its column and on the respective line for its companion. Once we were dealing with more than 50.000 authors, the size of the table, without the correct filters might become an issue. Noticed that on that figure there is a great density where most of the authors have relationships with several of its peers. The red line crossing the center is where the author relates to itself on the x and y axis and is not considered. Because the table is a symmetric matrix all the dots on the left of the center line will appear on the right side of that line.

One of the analyses for that graph is the clustering coefficient, which were 0,496 for the Malaria co-authorship. The clustering coefficient measures network ''clustering'' or ''transitivity,'' which is the probability that two of a scientist's coauthors have themselves coauthored a paper. In science a high clustering coefficient would suggest that people are working together quite often, while a very detached network, as in patents for example, might suggest that people very rarely contribute with people outside its group. This demonstrates that on the malaria research environment people tend to collaborate quite often with each other. This is also true for other areas of research [START_REF] Newman | Coauthorship networks and patterns of scientific collaboration[END_REF].

The middle table shows the relations amongst authors that have worked together on at least two occasions (publications) and the right most table authors that have worked together on at least three occasions. From the rightmost table we focus our analysis on the area where there is a greater concentration (top left) of relationships and from there we draw figure 6.

The social network analysis consists of understanding the connections (relations) existent over the network. Each red dot on figure 6 denotes a researcher and each line a relationship with another researcher. People with more connections have worked with more people and in consequence they have a higher centrality. Some interesting connections happen also amongst clusters. On Figure 6 we have 3 clusters that are connected with each other by few people. Those researchers might not have a higher centrality degree (absolute number of connections) but are key to keep the network together and help the flux of information. For this network we are showing authors that have at least 5 publications over the period and that have worked together on publications on three different occasions. The graph is only a partial view of the third table on figure 5.

A number of conclusions could be made with a simple visualization analysis over the graph. The authors, for example, with higher centrality might have the ability to influence a greater number of people. On the other hand, those same authors are exposed to a great number of ideas. As for the researchers that make the connections between clusters we can say that they might work as gate keepers and are the ones responsible to share new ideas to their own group or close related friends. A closer study over each individual author and its own work is necessary to further understand the possibilities and implications of such a graph but for the purpose of this work we have only raised the issue without a deeper analysis. The graph helps us to pinpoint the most important players and the ones we should pursue a further study. Another network visualization can be done using morphological analysis. Figure 7 shows the network over the MeSH words. Each red dot characterizes a key word used on a paper such as "carbon monoxide", "heme Oxygenase-1", "electron transport", "malaria" and so forth. The analysis consists in viewing how often that particular word comes along with related words. This might show a characteristic of research. As one example we have found that metal words very often come along together forming a cluster, such as silver, mercury and others and usually in researches done by countries in south east Asia. Words that often come along on the same papers might demonstrate a type of research or methods been used over time.

Once again our study focuses on the ability to use such graph analysis to understand and point the important nodes, words in these case, which deserves a further analysis. A diachronic analysis over the evolution of MeSH words in a scientific area might also be of great importance.

V. CONCLUSION

This study was an attempt to apply some methods to analyze data focusing on Malaria, a neglected disease. We have used data and network analysis tools on scientific databases. This line of research have been made by different authors before and now we aimed to put together some of the techniques used.

As for the findings on the field we can say that there have been an increase interest by the scientific community over the years by the increase number of publications and a steady growth of new scientists on the area. In regard to the countries investing on the area, new players are coming forward, especially among the countries with high rate of neglected disease incidence, such as India. As for the network analysis we could see that there is a high collaboration due to its clustering coefficient, which is in accordance with the scientific communities. Within those authors we found some key players in terms of the number of publications and those players might be a point for a further study. This study however has not shown deeper studies over specific authors evolutions using network analysis or morphological evolution using a similar methods. Those studies are part of our current work and might be demonstrated on future analysis.

Figure 1 -

 1 Figure 1 -World Map of Malaria Endemic countries. font (WHO,2012)

Figure 3 -Figure 2 -

 32 Figure 3 -Country Publication

Figure 4 -Figure 5 -

 45 Figure 4 -Author Individual Publication per Year

Figure 6 -

 6 Figure 6 -Author relationship network

Figure 7 -

 7 Figure 7 -MeSH words relationship network

Table 1 -

 1 Top Journals by the number of publications per year

Journals Number of Publications per Year (2012 -2001) Sum '12 '11 '10 '09 '08 '07 '06 '05 '04 '03 '02 '01 '00

  Malaria Journal 2110 409 361 350 308 250 161 112 59 43 41 16 0 0

	American Journal of Tropical Medicine	1209 82 82 95 87 65 152 135 100 110 102 71 82 46
	Plos One	841 278 219 136 91 78 37 2 0 0 0 0 0 0
	Tropical Medicine International Health	550 25 32 24 31 33 48 61 54 66 56 49 34 37
	Trans of the Royal
	Society of Tropical Medicine and	510 17 12 38 49 37 43 48 43 32 49 50 45 47
	Hygiene
	Journal of Infected Diseases	429 37 34 30 40 34 38 40 48 31 29 25 23 20
	Infection and Immunity	433 21 22 30 24 28 32 42 50 47 34 33 26 44
	Acta Tropical	413 58 25 44 44 30 20 39 43 47 23 18 11 11
	Trends in Parasitology	387 22 29 31 27 49 32 26 34 29 37 32 39 0
	Lancet	358 22 24 37 29 17 21 27 23 45 22 30 30 31

Table 2 -Author Publication Journals Number of Publications per Year (2012 -2001) Sum '12 '11 '10 '09 '08 '07 '06 '05 '04 '03 '02 '01 '00

 2

	White, N	310 37 29 25 20 27 21 22 22 26 21 21 18 21
	Looareesuw 221 0 1	4	3 13 16 19 18 29 30 25 37 26
	Kremsner, P 208 9 15 13 19 12 18 16 22 18 20 17 14 15
	Marsh, K	193 12 16 14 19 16 20 16 22 15 8 17 10 8