Anders I Mørch
email: anders.morch@intermedia.uio.no

William K Cheung
email: willliam@comp.hkbu.edu.hk

Kelvin C Wong
email: kcwong@comp.hkbu.edu.hk

Jiming Liu
email: jiming@comp.hkbu.edu.hk

Cynthia Lee
email: cfklee@hkbu.edu.hk

Mason H Lam

Janti P Tang

Grounding Collaborative Knowledge Building in Semantics-Based Critiquing

In this paper we investigate the use of Latent Semantic Analysis (LSA), Critiquing Systems, and Knowledge Building to support computerbased teaching of English composition. We have built and tested an English Composition Critiquing System that make use of LSA to analyze student essays and compute feedback by comparing their essays with teacher's model essays. LSA values are input to a critiquing component to provide a user interface for the students. A software agent can also use the critic feedback to coordinate a collaborative knowledge building session with multiple users (students and teachers). Shared feedback provides seed questions that can trigger discussion and extended reflection about the next phase of writing. We present the first version of a prototype we have built, and report the results from an informal experiment. We end the paper by describing our plans for future work.

Introduction

English is the preferred second language for many people and learning it occurs in many ways. For example, young people are quite apt in learning spoken English phrases when watching TV, browsing the Internet and communicating with peers on mobile phones (e.g. SMS). However, previous studies have shown these influences may have negative effect on vocabulary development [START_REF] Rice | Words from Sesame Street: Learning Vocabulary while Viewing[END_REF][START_REF] Weizman | Lexical Input as Related to Children's Vocabulary Acquisition: Effects of Sophisticated Exposure and Support for Meaning[END_REF]. As a consequence, students' reading and writing skills do not keep pace with listening and speaking. Furthermore, English composition is primarily taught in the classroom and practiced in homework assignments, supported by qualified teachers and parents. These are important but scarce resources, creating an imbalance of textual and oral language exposure. We address this dilemma by augmenting classroom-based composition training integrated with computer support.

The paper is organized as follows. We start by characterizing English composition as a design activity and identify the components of a computer-based design environment to support it. Next, we explain how latent semantic analysis (LSA) can be used to provide feedback on student compositions within this context, and how we have incorporated LSA as part of system architecture. We show a prototype of a critiquing system we have built, discuss our efforts in integrating it with a knowledgebuilding environment (FLE) and report the preliminary findings by comparing LSA with manual teacher feedback on a set of essays.

Related Work

Essay writing can be viewed as a design activity, producing a textual artifact -a document. A document consists of words and sentences. It has structuring (abstraction) and content production (composition) elements [START_REF] Yamamoto | Representational Talkback -An Approach to Support Writing as Design[END_REF]. These are key aspects of any design process. Structuring defines the organization of the document in terms of sentences, paragraphs and sections (i.e. levels of abstraction); whereas content production is about finding words and phrases, and sequencing them into readable sentences, which again become part of paragraphs, and so on. A well-composed essay will communicate certain ideas, topics or themes about some area of shared concern. Intermediate level abstractions, such as paragraphs and sections, serve as placeholders for complex ideas extended over multiple paragraphs, so that the writers and readers can focus on one idea at a time while suppressing unimportant details.

Creative design is said to consist of two sub-activities, action and reflection, supporting composition and abstraction, respectively. Action means to create an artifact by selecting and combining building blocks into functional arrangements and reflection means to evaluate the artifact from multiple viewpoints [START_REF] Mccall | Supporting Reflection-in-Action in the Janus Design Environment[END_REF]. When this occurs without external disruption other than situation-specific feedback, it is referred to as reflection-in-action [START_REF] Schön | The Reflective Practitioner: How Professionals Think in Action[END_REF]. In a good process of design, the designer will rapidly cycle between action and reflection until the design is completed. During this process, the "back talk" of the situation signals to the designer when there is a need to switch to the other mode. This is communicated by e.g. an incomplete drawing, inconsistency in arrangement of parts, a need for restructuring the task, etc.

Design Critiquing

Computational support for reflection-in-action is provided with the critiquing approach [START_REF] Fischer | The Role of Critiquing in Cooperative Problem Solving[END_REF][START_REF] Qiu | Making Critiquing Practical: Incremental Development of Educational Critiquing Systems[END_REF][START_REF] Robbins | Software Architecture Critics in the Argo Design Environment[END_REF]. Critiquing is defined as "presentation of a reasoned opinion about a product or action" created by a user with a computer [START_REF] Fischer | The Role of Critiquing in Cooperative Problem Solving[END_REF]. A Critiquing System integrates computational support for design-as-action and design-as-reflection and operationalizes Schön's notion of "back talk" with computational critics [START_REF] Fischer | The Role of Critiquing in Cooperative Problem Solving[END_REF][START_REF] Robbins | Design Critiquing Systems[END_REF]. Critics make the situation talk back so that non-expert designers can understand it, giving them task-specific feedback about the artifact-under-construction. Examples of critiquing systems are Janus [START_REF] Mccall | Supporting Reflection-in-Action in the Janus Design Environment[END_REF], ArgoUML [START_REF] Robbins | Design Critiquing Systems[END_REF], and The Java Critiquer [START_REF] Qiu | Making Critiquing Practical: Incremental Development of Educational Critiquing Systems[END_REF]. These systems were developed for the domains of kitchen design, UML (Unified Modeling Language) and Java programming, respectively. For example Janus allows designers to model kitchen designs at different levels of abstraction (from appliances to work centers), ArgoUML knows about the elements and relations of UML and can tell the designer when a software architecture diagram violates the rules of UML [START_REF] Robbins | Software Architecture Critics in the Argo Design Environment[END_REF]. Simi-larly, the Java Critiquer identifies statements in a program code that can be improved by readability and best practice [START_REF] Qiu | Making Critiquing Practical: Incremental Development of Educational Critiquing Systems[END_REF]. These critics provide feedback on partially completed software artifacts, pointing out inconsistency and incompleteness in the design.

We believe the critiquing approach can be useful for computer-supported English composition for the following two reasons. First, writing can be modeled as a design activity [START_REF] Yamamoto | Representational Talkback -An Approach to Support Writing as Design[END_REF], and second, critic feedback can supplement teacher feedback on student essays in certain situations (after school hours, in distributed environments). In this context we propose to integrate collaborative knowledge building and LSA with critiquing-Knowledge building to support collaborative reflection and LSA to compute the critic feedback. This is different from past work on critiquing systems and educational applications of LSA. The previous work on LSA has focused on individual learning by integrating it with Intelligent Tutoring Systems [START_REF] Steinhart | Summary Street: An Intelligent Tutoring System for Improving Student Writing Through the use of Latent Semantic Analysis[END_REF]. A goal for us is to provide computer support for both action and reflection, and individual and collaborative learning.

Collaborative Knowledge Building

Knowledge building [START_REF] Scardamalia | Computer Support for Knowledge-Building Communities[END_REF] requires that new knowledge is not simply assimilated with the help of a more knowledgeable person or mediated by a computer system, but also jointly constructed through solving problems with peers by a process of building shared understanding. This type of teaching and learning takes its inspiration from pedagogical models such as problem-based learning and case-based instruction. These are models for teaching that require students to explore open-ended problems and generalize from exemplary cases. The basic idea of knowledge building is that students gain a deeper understanding of a knowledge domain through engaging in a research-like process by generating or responding to problems or questions, proposing tentative answers (personal explanations) and searching for deepening knowledge collaboratively.

Knowledge building and its subsequent refinement, Progressive Inquiry [START_REF] Hakkarainen | Epistemology of Inquiry and Computer-Supported Collaborative Learning[END_REF] are well suited to be supported by Internet technologies such as web-based discussion forums and have received considerable attention in the Computer Supported Collaborative Learning (CSCL) community. A reason for this is that the regularity of knowledge building, which is modeled after scientific discourse, provides students with a well-defined scaffolding structure built into the online learning environments. Knowledge building environments are pedagogically designed discussion forums and include CSILE [START_REF] Scardamalia | Computer Support for Knowledge-Building Communities[END_REF], Knowledge Forum, and Future Learning Environment (FLE) [START_REF] Leinonen | Fle3 > Future Learning Environment. Website hosted[END_REF]. They are used in schools in Canada, Hong Kong and Scandinavia, as well as elsewhere in the world.

The reason for our wish to integrate collaborative knowledge building with a critiquing system is twofold. First, critiquing systems do not provide full support of design-as-reflection because they address primarily individual designers' needs, inspired by Schön's notion of reflective practice [START_REF] Schön | The Reflective Practitioner: How Professionals Think in Action[END_REF]. Knowledge building, on the other hand, can add a multi-user dimension by supporting "collaborative reflection," even though knowledge building was not originally conceived as such. Collaborative reflection occurs during "talk with peers" [e.g. 15] in meaningful contexts, i.e. jointly addressing problems or questions shared by a community of stakeholders in which shared understanding can emerge [START_REF] Arnseth | I: Making Sense of Shared Knowledge[END_REF]. Knowledge building thus becomes an important part of the integrated collaborative learning and problem-solving environment.

Second, one of the authors has previously participated in a study to evaluate a knowledge-building environment (FLE) to support problem-based teaching of natural science in two high school classes in Norway [START_REF] Ludvigsen | Categorization in Knowledge Building: Task-specific Argumentation in a Co-located CSCL Environment[END_REF][START_REF] Mørch | Knowledge Building in Distributed Collaborative Learning: Organizing Information in Multiple Worlds[END_REF]. One of the results of this study was that students found knowledge building difficult. In particular they did not properly understand how to use the message categories to post messages in the forum. This was manifest in that interaction over time became less knowledge-building intense and more task-specific (localized), revolving around the respective schools' local situations, thus grounding the interaction. In the current project we address the grounding problem [START_REF] Arnseth | I: Making Sense of Shared Knowledge[END_REF][START_REF] Baker | The Role of Grounding in Collaborative Learning Tasks[END_REF][START_REF] Brennan | The Grounding Problem in Conversations with and through Computers[END_REF] computationally by integrating a knowledge-building environment with an LSA-based critiquing system.

Latent Semantic Analysis

Latent Semantic Analysis (LSA) is a mathematical technique for computing the semantic similarity between words and text segments with the help of a large corpus. The corpus can be a set of related documents. It can also be one document broken down into smaller text segments such as paragraphs or even sentences, as in our case. The input to LSA is the set of text segments, which may need processing by the computer in various ways.

LSA computes the similarity of two input texts (student and teacher) as follows. First, both input texts are segmented to form the corpus. Then, the word-segment association matrix D is constructed. In the matrix D, each row stands for a unique word and each column stands for a text segment. Each cell entry can be the frequency of a given word in a given text segment. As an example, consider the segment "The 2004 IEEE International Conference on Electronic Technology, Electronic Commerce and Electronic Service." If the j th column corresponds to the aforementioned segment and the i th row corresponds to the word "Electronic", then the value in D ij would be 3 as "Electronic" occurs three times in the segment. As weighting the words based on their individual importance is known to be effective in obtaining better matching results, we use entropy values instead of computing D ij given as Once the matrix D is computed, it is decomposed using Singular Value Decomposition (SVD) [START_REF] Strang | Linear Algebra and Its Application[END_REF]. The matrix D is expressed as a unique product of three matrices: D = PλQ' such that P and Q have orthonormal columns and λ contains the singular values on the diagonal. By comparing the diagonal elements of λ, we only need to keep those elements with large values and can set the others to zero, with the effect that the dimension of λ will be reduced. This is equivalent to removing the corresponding columns from P and rows from Q. The new P and Q then define the revised "semantic space." Words that have appeared in similar segments, and segments with similar semantic content, will be positioned near one another [START_REF] Steinhart | Summary Street: An Intelligent Tutoring System for Improving Student Writing Through the use of Latent Semantic Analysis[END_REF]. Words that do not co-occur (e.g. bicycle and bike), but occur in similar contexts will also be grouped together.

After the semantic space has been computed the new D can be "reconstructed" based on the new P and Q. The similarity between two text segments can then be computed by calculating the geometric cosine between their corresponding vectors in D, given as Remark. In order to allow the latest submitted essays to be compared with the corpus in the derived semantic space; one can project them by the SVD results. These are referred to as pseudo documents [START_REF] Deerwester | Indexing by Latent Semantic Analysis[END_REF]. This has the advantage that we only need to compute the SVD once instead of per submission. Additional technical details on LSA can be found in [START_REF] Landauer | An Introduction to Latent Semantic Analysis[END_REF].

Components of a Learning Environment for Essay Writing

We have incorporated LSA together with critiquing and knowledge building to form an integrated learning environment for English Essay Writing. The LSA-based critiquing component of this environment allows us to compare student and model essays and provide critic feedback to the students when they submit their work in progress, whereas the knowledge building component provides support for collaboratively resolving critic feedback that is not well understood by the students on their own, grounding it in a relevant context. The overview of this environment is shown in Figure 1 and the workings of its components are explained below.

The teacher first decides on the topic to be taught and writes and/or collects a set of samples articles and essays that represent the domain in some detail. These samples are then input into the system so that the LSA analyzer can build a semantic space for the domain. Student model essays, suggested answers by teachers, as well as articles from external sources (which could be anything from on-line newspapers to scanned essays of textbooks) constitute this set.

Fig. 1. English composition integrated learning environment system architecture

The students write their essays using the English Composition Critiquing System (see below). When they require assistance they can request automated feedback (critique), which points out missing items in their text (compared with the corpus essays).

Before the text can be input into LSA all the articles are broken down into sentences and preprocessed by techniques such as stop-word removal and stemming as described above. The Analyzer then computes the word-segment association matrix. Singular Value Decomposition [START_REF] Strang | Linear Algebra and Its Application[END_REF] is performed on the matrix and the semantic similarity between all possible sentence pairs, one from the student and the other from the model samples, is computed. This allows the system to identify the sentences in the model essays that contain themes that are missing in the students' submissions.

The final steps are semantic matching and summarization. The identified sentences containing the missing themes can be summarized as a trunk of keywords or short phrases preset by the teacher or automatically by the system, using computational text summarization techniques. This will result in a summary that is reported as critic feedback in the user interface. In the prototype we describe below, we have modeled our critics' feedback based on the phrasing and organization of Hong Kong English teachers' marking schemes. When the critique is presented as feedback immediately after the students have completed a part of their essay, it will allow them to revise their essays in a meaningful context.

The roles of teachers and students could be much more active than merely providing model samples and improving essays based on the predefined critic feedback. Teachers can monitor how well the different themes are handled by the students. They may provide more insight into how individual students incorporate missing themes, and participate as facilitators of student collaboration sessions to provide feedback when the students run out of ideas [START_REF] Littlewood | The Use of Collaborative Interaction Techniques in Teaching EAP[END_REF]. Their participation serves the purpose of supportive interaction through which an expert assists a group of learner to develop a higher level of understanding [e.g. 15] and pushes the learner's zone of proximal development [START_REF] Vygotsky | Mind in Society -The Development of Higher Psychological Processes[END_REF]. A recent large-scale language learning survey has confirmed the observation that most students in East Asian and European countries have a positive attitude towards cooperating in groups in order to achieve common goals, and they would like to see themselves as active participants in the classroom learning process [START_REF] Littlewood | Students' Perception of Classroom Learning in East Asia and Europe[END_REF].

The LSA-based critiquing and knowledge building environment marks the contours of a "double-loop" learning process (see Figure 1), alternating between inner (individual) and outer (collaboration) phases. The process can be repeated several times before the students submit their final essay for grading or commenting on by the teacher. In a good process of writing, we anticipate this learning environment will support reflection-in-action at two levels: 1) individual (inner loop) activity when students switch between essay composition and modification by responding to well understood critique and 2) collaborative (inner + outer loop) activity by entering a collaborative mode of interaction through responding to critique that is not well understood or where the understanding can be broadened or made more interesting for the students by sharing their ideas with others. Whether or not our computational environment can provide adequate scaffolding for reflection-in-action in English essay writing at these two levels is currently a hypothesis. Its conceptual basis and technological platform are provided in this paper.

System Prototypes

In order to support English essay writing as a design activity based on the models and techniques presented above we decided to reuse and integrate existing systems, making modifications when necessary. When selecting the critiquing component we considered both ArgoUML [START_REF] Robbins | Software Architecture Critics in the Argo Design Environment[END_REF] and the Java Critiquer [START_REF] Qiu | Making Critiquing Practical: Incremental Development of Educational Critiquing Systems[END_REF]. The latter has the advantage that it supports the design of a textual artifact (program code), but ArgoUML has the advantage it is a freely downloadable, open source system. We decided on ArgoUML due to its accessibility. However, we had to modify the system extensively, with the effect its UML features are no longer visible (see Figure 2).

The system building approach we took was to start with the existing system, removing all the features we did not need and adding the features that are unique to our domain. In retrospect we could have built the equivalent functionality from scratch in about the same time. However, some of the Argo features we have hidden, such as the building block palette and to-do list we anticipate to be useful in future versions of our system. For example, the current version requires students to input their essays in terms of characters and words (i.e. the composition area is a text processing window), whereas LSA Analyzer requires sentences as input. Furthermore, we know from previous studies that designers work at multiple levels of abstraction [START_REF] Akin | How Do Architects Design[END_REF], which in the context of essay composition means words, sentences, paragraphs, sections, and other higher level structures [START_REF] Yamamoto | Representational Talkback -An Approach to Support Writing as Design[END_REF]. Therefore, providing support for direct manipulation of intermediate-level building blocks is another way to extend the current system. It will allow the students to acquire skills in organization as well as composition, and it may simplify LSA preprocessing by reducing the need for sentence segmentation. Furthermore, to-do lists that can keep track of overlooked critic messages and suggest when they should be attended to, can help students to manage multiple missing subthemes. This is part of future work. Fig. 2. The English composition critiquing system has a "Submit" button to generate LSAbased critique and a "Discussion" button to trigger a knowledge building session (Figure 3)

For the knowledge-building component we decided on another open source system, FLE (Future Learning Environment) [START_REF] Leinonen | Fle3 > Future Learning Environment. Website hosted[END_REF]. FLE is a knowledge building environment developed in accordance with the progressive inquiry model [START_REF] Hakkarainen | Epistemology of Inquiry and Computer-Supported Collaborative Learning[END_REF]. It is an asynchronous, web-based groupware for computer-supported collaborative learning. It is designed to support collaborative learning in the form of a discussion forum with message categories (knowledge types) named after the stages of the progressive inquiry model. These stages and corresponding categories can help students improve their collaboration and ability to solve open-ended problems. The categories that are provided with the system (Fle3) are "problem," "my explanation", "scientific explanation", "summary" and "comment." Two of these categories are displayed in Figure 3.

Figure 3 shows the reader's interface of the knowledge-building forum of Fle3 from a simulated session involving two students who have been invited to join the forum by a coordinator agent to resolve a missing essay topic. The missing essay topic is picked up by the agent and serves as a seed question. In knowledge building these initial questions are often formulated by teachers, based on their knowledge of the subject to be taught. In this case it is handled by a software agent based on its ability to identify students who receive the same feedback, and a belief that two students receiving the same feedback have something in common that they can resolve by information sharing and discussion. The reason why discussion may be the appropriate form of resolving the feedback is based on the fact that missing sub-themes define open-ended questions, i.e. they can be addressed in many different ways. We have not yet tested these claims, but it builds on our previous (empirical-based, system building) work on integrating agents with FLE [START_REF] Dolonen | Integrating Software Agents with FLE[END_REF] and adaptive user interface agents [START_REF] Liu | An Adaptive User Interface Based on Personalized Learning[END_REF].

Evaluation and Preliminary Results

In order to assess the feasibility of our system regarding its ability to suggest missing themes for students to consider when revising their essays, we conducted an experiment. Seven high school students in Hong Kong were invited to write a 400 to 500word essay on the topic "Write an essay about the experience of traveling to China". At the same time, a teacher was asked to provide a number of sub-themes (25 in this study) of this topic, which the students were expected to include in their essays.

The teacher assessed the finished essays to identify the sub-themes that were missing, based on the set of predefined sub-themes. Then the essays were assessed by our system. Each text segment in the student essay was compared with each sample segment suggested by the teacher. If the semantic similarity (which was represented by the cosine value calculated by LSA) was below a preset threshold, we considered the sub-theme of the sample segment to be missing in the student essay. Finally, the missing sub-themes identified by the teacher and our system were compared to evaluate the performance of the system. The system identified 35 missing sub-themes in the 7 student essays, 22 of them were judged to be correct (i.e., also identified by the teacher as missing sub-themes), whereas the remaining 13 were considered inappropriate. Based on this we get a tentative precision rate of 63%. A reason for this low number is the small size of the corpus.

We used a corpus of about 3,000 words to build the semantic space. This is smaller corpus than what has been used in related studies, such as TASA-all (a large knowledge space consisting of text samples from the K12 (grade 1-12) curriculum in the United States) [START_REF] Steinhart | Summary Street: An Intelligent Tutoring System for Improving Student Writing Through the use of Latent Semantic Analysis[END_REF]. The TASA-all corpus comprises approximately eleven million words. We believe that a larger corpus for constructing our semantic space will further improve the accuracy of our system in identifying missing sub-themes. We will explore this hypothesis by varying corpus size for different domains in our further work

Conclusions and Directions for Further Work

Many students find essay writing stressful because they do not have sufficient ideas to fully cover the topic they are asked to write about. They usually run out of ideas before they have completed their essays. When the class size is large and when running in-class writing exercises, it is difficult for teachers to give proper feedback to individual students on missing topics to address in their rewrite, because it requires considerable amount of teachers' time.

We believe the use of our semantic-based critiquing system can support students by autonomously suggesting what missing sub-themes they should pay attention to when revising their essays. Students can submit their draft essays to the system for feedback whenever they are running out of ideas. If the feedback is incomplete or poorly understood (e.g. due to LSA truncation steps) the students can enter a systeminitiated, contextualized discussion forum that provides support for collaborative knowledge building using the pedagogically informed progressive inquiry model. We believe that this can help students to enrich their essay content with more vocabulary in a context that is meaningful to them, thus grounding their learning activity. We are also interested in how the students view the critiquing system and to what extent the knowledge-building forum will be used. On the technical (algorithmic) side, we want to investigate the factors that will affect the performance of LSA in the essay-writing domain. For instance, how to determine both the optimal number of dimensions of the semantic space and the optimal threshold value for similarity matching are questions that require further research to answer.

Fig. 3 .

 3 Fig. 3. The Fle3 Web-based knowledge-building forum shows a persistent storage of discussions related to students' missing essay topics. An agent initiates a thread when it discovers an essay topic that is missing by more than one student. The KB forum has not yet been fully integrated into our environment and it has not yet been tested with students

Acknowledgments

This work was partly supported by Faculty Research Grant of Hong Kong Baptist University-FRG/02-03/II-38 and Group Research Grant of Research Grant Council (RGC) -HKBU 2/03/C. The first author was as a Visiting Research Scholar at CS department, Hong Kong Baptist University while this paper was written, and he thanks InterMedia and Faculty of Education, University of Oslo for a sabbatical leave.


Roy D Pea

Eric Hoffert

Video workflow in the learning sciences: Prospect of Emerging

come   L'archive ouverte pluridisciplinaire


domain: shs info info.info-tt

text:
Andrea Bertino

Luca Foppiano

Laurent Romary

Pierre Mounier

Leveraging Concepts in Open Access Publications

Keywords: Named Entity Recognition and Disambiguation (NERD), Entity-Fishing, Open Access, Monographs, Digital Publishing Platforms

de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Challenges and Perspectives for OA Digital Monographs

The publication of scholarly monographs in OA is of great benefit to researchers, both as authors and as readers: it increases the visibility of their publications, ensures a wider dissemination of research results in international and interdisciplinary contexts, enables added value such as comprehensive indexing and also allows the innovative design of traditional formats. However, adopting open access publishing models for scholarly monographs is still slower than for scholarly essays [START_REF] Eve | Open access publishing and scholarly communications in non-scientific disciplines[END_REF]. In order to simplify the integration of monographs in the Open Access universe, several infrastructures and services have been developed in recent years: OAPEN (Open Access Publishing in European Networks, 2010); OpenEdition Books (2012); Open Monograph Press (2012); Ubiquity Press (2012); The Directory of Open Access Books (DOAB, 2012); Knowledge Unlatched (2012); and the JSTOR Open Access Book Programme (2016). Nevertheless, the landscape of academic publishing has so far remained highly fragmented along various national, linguistic and subject-specific lines, particularly in SSH (OPERAS Consortium, 2017). The current uncoordinated situation represents a major obstacle to the optimal dissemination of research results of the SSH disciplines and their impact on the structures of open science.

The H2020 HIRMEOS Project

The HIRMEOS project focuses on the monograph as a significant mode of scholarly communication in SSH and tackles the main obstacles to the full integration of five large-scale platforms supporting open access content. The main objective of HIRMEOS is to optimise five OA digital platforms for the publication of monographs from the SSH and to ensure their interoperability. An integrated publishing system would support scientific work by fostering basic research activities -the so-called scholarly primitives -i.e. writing, finding, annotating, referencing, assessing, exemplifying, presenting, as well as elementary activities in the digital field such as searching in browsers, connecting digital texts, collecting data, scanning and creating standards of data handling (data practices) [START_REF] Palmer | Scholarly information practices in the online environment: Themes from the literature and implications for library service development[END_REF]. HIRMEOS intends to transform collections of passive documents into corpora of enriched texts. More specifically, the participating platforms will be enhanced with services that enable identification, authentication and interoperability (via DOI, ORCID, FundRef), the annotation of monographs, the gathering of usage and alternative metric data, as well as using tools -like entity-fishingthat enrich the text with linked data.

Entity Resolution Service

With the digital information explosion, over the last few decades the extraction and resolution of entities has been studied extensively [START_REF] Milne | Extracting corpus specific knowledge bases from wikipedia[END_REF][START_REF] Cucerzan | Large-scale named entity disambiguation based on wikipedia data[END_REF] and has become a crucial task in large-scale text mining activities. Entity extraction and resolution is the task of determining the identity of entities mentioned in a text against a knowledge base representing the reality of the domain under consideration. This could be the recognition of generic Named Entities suitable in general purpose subjects, like person name, location, organisation name and so on, but also the resolution of specialist entities in different domains. Entity-fishing addresses these needs and provides a generic service for entity extraction and disambiguation (NERD) against Wikidata, supporting possible further adaptations for applications to specialist domains. This allows it to be independent of a particular framework and usage scenario for maximum reuse. Entity-fishing API allows the processing of different input (raw or partially annotated texts, PDF, search query), different languages and different formats. Entity-fishing employs supervised Machine Learning algorithms for both the recognition and the disambiguation tasks using training data generated from Wikipedia article structures [START_REF] Milne | Learning to link with wikipedia[END_REF].

Entity-fishing Integration: Applications, Use Cases and beyond

The integration of the service during the HIRMEOS project was supervised and measured by different levels of increasing complexity (from the access to the API to the creation of new services using the generated data). After having successfully completed the basic integration to the API, each partner could choose to use the annotations based on their own needs and practices. The results have been summarised as a set of use cases, providing an initial feedback on common needs among publishing platforms. One of the most implemented use cases was the enhanced facet search based on entities extracted from the library content. Targeting Named Entity of Person and Location, the users were able to further restrict their search to some content-based information. This functionality required processing the entire collection and indexing all the generated annotations. A variant of this idea was the automatic generation of a word cloud at the repository level; in this way the users were able to access the most important concepts present in the monographs hosted at the digital library. An interesting evolution which could improve the search quality would be the generation of the word cloud at the search or book level, the cloud being updated at each query as the user narrows down the number of results. The annotations could in this way help to achieve better and quicker understanding of the specific and disciplinary language of certain monographs and so encourage non-specialists to use them. In this way, exploiting NERD annotations would foster interdisciplinary research. Other partners worked on a different approach, more focused on enhancing the visualisation of the monograph, by supporting annotation generated by entity-fishing to the monographs' landing page in order to automatically annotate content and seamlessly visualise it to the users. This visualisation could be further enhanced by having a slider at the side of the page that allows users to reduce or increase the detail of the annotations based on occurrence or type of entity. Finally, another interesting aspect pursued was the possibility to group books by their content extracted entities. Linking related books is one of the keys to boosting dissemination. Classic recommendation systems attempt to suggest additional articles or products the user might be interested in by exploiting the user's purchase history or the navigation. This could have a big impact on the dissemination in open access monograph catalogues. A simpler approach (which would not require collecting any user data) would be to process the generated annotations with clustering techniques. This would enable more cluster configurations, exploiting different aspects (domains, similarity, frequency, etc) of the collection annotations. There are indeed many more ideas that could be implemented to improve the user experience in terms of navigation or search. The search box could be improved by adding an additional layer dealing with disambiguation in the query in order to expand it to match the correct concept among several ambiguous possibilities. Entity-fishing could be also integrated in the process of feature generation as recommended by the CORE (https://core.ac.uk/), extracting concepts to be used as keywords for linking research outputs together. Another challenging idea is the possibility to extract all events and temporal expressions in order to build a timeline visualisation graph at the collection or book level. These use cases are applicable not only to other publishing platforms in SSH but potentially to any open access repository.

bertino@sub.uni-goettingen.de


Axel Modave
email: axel.modave@ensta-paristech.fr

Xavier Antoine
email: xavier.antoine@univ-lorraine.fr

Christophe Geuzaine
email: christophe.geuzaine@uliege.be

Christophe Geuzaine An

An efficient domain

Solving high-frequency time-harmonic scattering problems using finite element techniques is challenging, as such problems lead to very large, complex and indefinite linear systems. Optimized Schwarz domain decomposition methods (DDMs) are currently a very promising approach, where subproblems of smaller sizes are solved in parallel using direct solvers, and are combined in an iterative procedure.

It is well-known that the convergence rate of these methods strongly depends on the transmission condition enforced on the interfaces between the subdomains. Local transmission conditions based on high-order absorbing boundary conditions (HABCs) have proved well suited [ , ]. They represent a good compromise between basic impedance conditions (which lead to suboptimal convergence) and the exact Dirichlet-to-Neumann (DtN) map related to the complementary of the subdomain (which is expensive to compute). However, a direct application of this approach for domain decomposition configurations with cross-points, where more than two subdomains meet, does not provide satisfactory results.

We present an improved DDM that efficiently addresses configurations with cross points. Noting that these points actually are corners for the subdomains, our strategy consists in incorporating a corner treatment developed for HABCs into the DDM procedure. After a presentation of the key aspects of the methods, the effectiveness of our approach is discussed with two-dimensional finite element results.

. Helmholtz problem with HABC and corner treatment

In order to solve scattering problems set on infinite or very large domains by finite element methods, a common strategy consists in computing the numerical solution only on a truncated computational domain, and using a non-reflecting treatment at the artificial boundary, such as a HABC or a perfectly matched layer (PML). To describe our approach, we consider a two-dimensional Helmholtz problem defined on a rectangular computational domain Ω:

∆u + k 2 u = s, in Ω, ∂ n f u + Bu = 0, on Γ f , ( f = 1 . . . 4) ( )
where k(x) is the wavenumber, s(x) is a source term, Γ f is an edge of the rectangular domain, ∂ n f is the exterior normal derivative on Γ f ⊂ ∂Ω, and B is a non-reflecting boundary operator. Following [ ],

we use a Padé-type HABC, which is obtained by approximating to exact DtN operator for the half-space problem, with constant k and s = 0 outside. It corresponds to using, for each edge Γ f ,

Bu = -ıkαu - 2ıαk M N ∑ i=1 c i (u f ,i + u), on Γ f ,
and introducing N auxiliary fields {u f ,i } N i=1 governed by the auxiliary equations

∂ 2 ττ u f ,i + k 2 (α 2 c i + 1)u f ,i + α 2 (c i + 1)u = 0, on Γ f , (i = 1 . . . N) ( )
where ∂ 2 ττ is the second-order tangent derivative, α = e ıφ/2 , c i = tan 2 (iπ/M) and M = 2N + 1. The accuracy of the numerical solution at the boundary depends on the number N and the angle φ [ ].

Because of the spatial derivative in equation ( ), additional boundary conditions must be prescribed on the auxiliary fields at the boundary of the edges (i.e. at the corner of the domain) to close the system. Following [ ], we introduce new relations that ensure the compatibility of the system without any supplementary approximation. With these relations, the auxiliary fields defined on adjacent edges are coupled at the common corner. For the fields {u f ,i } N i=1 defined on Γ f , having an adjacent edge Γ f , the boundary conditions at the corner P f ,c = Γ f ∩ Γ f can be written as

∂ n f ,c u f ,i + Du f ,i = 0, on P f ,c , (i = 1 . . . N) ( ) with Du f ,i = -ıkαu f ,i - 2ıαk M N ∑ j=1 c j (α 2 c i + 1 -α 2 )u f ,i -α 2 (c i + 1)u f , j α 2 c i + α 2 c j + 1 = 0, on P f ,c , (i = 1 . . . N)
where ∂ n f ,c is the exterior normal derivative at P f ,c ⊂ ∂Γ f , and {u f , j } N j=1 are the auxiliary fields defined on Γ f . Finally, the problem consists in solving the main field u(x) on the domain with boundary conditions on the edges (equation ( )) and N auxiliary fields on each edge with boundary conditions at the corners (equations ( )-( )). See [ ] for a three-dimensional version of this strategy.

. A non-overlapping DDM with cross-point treatment

The Helmholtz problem defined on Ω is decomposed into subproblems defined on non-overlapping subdomains

Ω I (I = 1 . . . N dom ), with Ω = I Ω I and Ω I Ω J = / 0 if I = J.
We consider here a structured decomposition of a rectangular domain Ω into an array of rectangular subdomains (see figure ). The edges of each subdomain Ω I are denoted Γ I, f ( f = 1 . . . 4). Each edge could be an interface edge (if there is a neighboring subdomain beyond the edge) or a boundary edge (if it belongs to the boundary of Ω). At each iteration of the DDM algorithm, the subproblems are solved in parallel, and data are exchanged at the interfaces between the subdomains to synchronize the solutions. The additive Schwarz DDM can be described as follows, at iteration + 1:

Ω 1 Ω 2 Ω 3 Ω 4 Γ g 1,f g2,g g 4,f,d,i g 1,f,c,i * * * * • • • • • • ⇌ Transmission
• For all subdomain Ω I , compute u +1 I solution to

∆u +1 I + k 2 u +1 I = s, in Ω I , ∂ n I, f u +1 I + Bu +1 I = g I, f , on Γ I, f , ( f = 1 . . . 4)
where g I, f is a transmission variable if Γ I, f is an interface edge, or it is set to zero if Γ I, f is a boundary edge.

• For all interface between neighboring subdomains Ω I and Ω J , update the transmission variables g +1 I, f and g +1 J,g according to

g +1 I, f = -g J,g + 2Bu +1 J , on Γ J,g , g +1 J,g = -g I, f + 2Bu +1 I , on Γ I, f , where Γ I, f = Γ J,g is the common interface edge.
The convergence of this algorithm is accelerated by using an iterative Krylov method (GMRES) on the top of the procedure for updating the transmission variables. See e.g. [ ] for more details.

Because the HABC is used as transmission operator (on the interface edges) and as boundary operator (on the boundary edges), the description of the DDM algorithm is incomplete. It should include the auxiliary fields defined on the edges and, since these fields are governed by equation ( ), additional conditions should be prescribed at the boundary of the edges. The boundary points of each edge Γ I, f are denoted P I, f ,c (c = 1, 2). Each point could be a cross point (at the cross of two interface edges), a corner point (at the cross of two boundary edges) or a boundary point (at the cross of one interface edge and one boundary edge). They are represented with the symbols •, • and * on figure .

At iteration + 1 of the DDM algorithm, we have the additional operations:

• For all boundary/interface edge Γ I, f , compute the auxiliary field u +1 I, f ,i solution to

∂ 2 ττ u +1 I, f ,i + k 2 (α 2 c i + 1)u +1 I, f ,i + α 2 (c i + 1)u +1 I = 0, on Γ I, f , ∂ n I, f ,c u +1 I, f ,i + Du +1 I, f ,i = g I, f ,c,i , at P I, f ,c , (c = 1, 2)
where g I, f ,c,i is a transmission variable if the adjacent edge of Γ I, f at the boundary point P I, f ,c is an interface edge, or it is set to zero if the adjacent edge is a boundary edge.

• For all boundary/cross point shared by the edges Γ I, f and Γ J, f of neighboring subdomains Ω I and Ω J , update the transmission variables g I, f ,c,i and g J, f ,d,i according to:

g +1 I, f ,c,i = -g J, f ,d,i + 2Du +1 J, f ,i on P J, f ,d , g +1 J, f ,d,i = -g I, f ,c,i + 2Du +1 I, f ,i , on P I, f ,c
, where P I, f ,c = P J, f ,d is the common point.

The auxiliary fields of two adjacent edges of one subdomain are coupled by the operator D at the common corner. All these operations are rather naturally included in the DDM algorithm. A GMRES is used for updating all the transmission variables, which are now associated to shared edges and shared points.

. Preliminary finite element results

In order to verify and to analyze the efficiency of the proposed DDM, we present finite element results obtained with two D benchmarks. The numerical scheme is based on a Galerkin method adapted from [ ], with meshes made of triangles, nodal finite elements, and second-degree basis functions. The simulations are made with the GetDP and GetDDM environments [ ].

In the first benchmark, we consider the scattering of an incident plane wave by a disk. The scattered field is computed on a rectangular domain Ω, which is partitioned into six subdomains (figure a). A Neumann condition is prescribed on the boundary of the disk, and the HABC with the corner treatment is used on the exterior boundary. In the DDM algorithm, transmission operators based on an optimized impedance condition [ ] and the HABC are tested. In the former case, a specific treatment ensures the compatibility between the impedance condition (on the interface edges) and the HABC (on the boundary edges) at the boundary points. The effect of the boundary/cross-point treatments is analyzed by keeping or removing the corresponding terms in the finite element scheme. For both kinds of transmission operator, the boundary-point treatment is required for converging towards the good solution. Indeed, without this treatment, the residual decreases with the iteration number, but the error stagnates (figures b and c, respectively). By contrast, the error decreases correctly when the boundary-point treatment is enabled. In that case, the convergence is faster with the transmission operator based on the HABC, and it is even faster with the cross-point treatment.

We finally address a more challenging benchmark with a heterogeneous medium: the Marmousi model, which represents a realistic geological structure (figure a). Although the HABC was initially derived by assuming a constant wavenumber, it provides good accuracy for problems with heterogeneous media [ ], and we have observed that it accelerates the convergences of the DDM for the Marmousi benchmark (results not shown here). On figure , the convergence of the method is compared for different domain partitions. For a larger number of subdomains (which is required to solve large problems), the DDM converges significantly faster with a multi-dimensional partition than with the monodimensional partition, which confirms the efficiency and the interest of our approach.

  Figure : Example of domain decomposition (2 × configuration) with the transmission variables.

(

  Figure : Scattering benchmark: Configuration and solution (a). History of residual (b) and L 2 -error (b) for transmission operators based on the basic impedance condition (red lines) or the HABC (black lines) with/without point treatments. Parameters: N = 4 and φ = π/3

[

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