Bich-Liên Doan

Formalizing for querying learning objects using OWL

The World Wide Web offers an increasing amount of complex and rich educational Web resources that are available for free in various domains. Unfortunately, it is difficult today to have a Web agent that answers precisely a simple query. Semantic Web aims to make Web resources meaningful to automated agents. Ontologies are proposed to provide a formal representation of a shared and common conceptualization of a specific domain. For the description of educational resources several communities are working on the definition of metadata elements. The Learning Technology Standards Committee (LTSC) specifies the Learning Object Metadata (LOM), a set of elements describing the relevant characteristics for learning resources. The goal of this paper is to give a formal and more comprehensive content description of learning resources in order to allow better reusability and retrievals. This description is particularly focused on the semantic relationships between learning resources which constitute an important aspect to access information. It uses OWL, an ontology language for the semantic Web, recently developed by the W3C. OWL provides powerful expressiveness combined with desirable computational properties for reasoning systems due to its correspondence with description logics. The query of the corresponding knowledge base is illustrated using OWL Query Language OWL-QL.

A web page topic segmentation algorithm based on visual criteria and content layout

This paper presents experiments using an algorithm of web page topic segmentation that show significant precision improvement in the retrieval of documents issued from the Web track corpus of TREC 2001. Instead of processing the whole document, a web page is segmented into different semantic blocks according to visual criteria (such as horizontal lines, colors) and structural tags (such as headings <H1>~<H6>, paragraph <P>). We conclude that combining visual and content layout criteria gives the best results for increasing the precision: the ranking of the page is calculated for relevant segments of pages resulting from the segmentation algorithm.

An Educational System Based on Several Ontologies

Metadata are required for finding, exchanging, and managing learning objects. But, their semantics is not clear. Furthermore, the IEEE LOM (Learning Object Metadata) is an IEEE standard since July 2002 but, even if it is widely used, it is not the only one standard used to describe learning objects. The use of ontologies is a key step forward for describing the semantics of information on the Web. We propose to specify the semantics of metadata by using a formal language of ontology, OWL. Furthermore, assuming that we are in an open world and in order to facilitate the exchange of learning resources with other institutions, we propose a solution based on several ontologies.

Unsupervised Approach for Identifying Users' Political Orientations

Opinions, in news media platforms, provide a world wide access to what people think about daily life topics. Thus, exploiting such a source of information to identify the trends can be very useful in many scenarios, such as political parties who are interested in monitoring their impact. In this paper, we present an unsupervised technique to classify users based on their political orientations. Our approach is based on two main concepts: 1 the selection of the aspects and the sentiments users have expressed in their opinions, and 2 the creation of knowledge base from Wikipedia to automatically classify users according to their political orientations. We have tested our approach on two datasets crawled from CNN and Aljazeera. The results show that our approach achieves high quality results.

Using OWL to describe pedagogical resources

In this paper we present an example of a part of a pedagogical ontology for a grande ecole. OWL is intended to help users to formalize ontologies and to be a support for the semantic Web, for example to enable the interchange of resources and the inference of knowledge while querying these resources.

Workshop on Contextual Information Access, Seeking and Retrieval Evaluation

The main purpose of this workshop is to bring together IR researchers working on or interested in the evaluation of approaches to contextual information access, seeking and retrieval, and let them to share their latest research results, to express their opinions on the related issues, and to promote discussion on the future directions of evaluation.

Towards better news article recommendation

News media platforms publish articles about daily events letting their users comment on them, and forming interesting discussions in almost real-time. To keep users always active and interested, media platforms need an effective recommender system to bring up new articles that match user interests. In this article, we show that we can improve the quality of recommendation by exploiting valuable information provided by user comments. This information reveals aspects not directly tackled by the news article on which they have been posted. We call such aspects latent aspects. We demonstrate how these latent aspects can make a crucial difference in the accuracy of future recommendation. The challenge in detecting them is due to the noisy nature of user comments. To support our claim, we propose a novel news recommendation system that (1) enriches the description of news articles by latent aspects extracted from user comments, (2) deals with noisy comments by proposing a model for user comments ranking, and (3) proposes a diversification model to remove redundancies and provide a wide coverage of aspects. We have tested our approach using large collections of real user activities in four news Web sites, namely The INDEPENDENT, The Telegraph, CNN and Al-Jazeera. The results show that our approach outperforms baseline approaches achieving a significantly higher accuracy.

A quantum-based semiotic model for textual semantics

Purpose The paper aims to provide a semiotic interpretation of the role played by entanglement in quantum-based models aimed to information retrieval and suggests possible improvements. Actual models are capable of retrieving documents relevant to a query composed of a keyword and its acceptation expressed by a given context. The paper also considers some analogies between this technique and quantum-based approaches in other disciplines to discuss the consequence of this quantum turn, as epistemology and philosophy of language are concerned. Design/methodology/approach We use quantum geometry to design a formal model for textual semiotics. In particular, the authors refer to Greimas’s work on semantics and information theory, to Eco’s writings on semantic memory and to Lotman’s work on a cybernetic notion of culture. Findings Quantum approaches imply a particular point of view on meaning. Meaning is not a real, positive quality of a given word. It is a net of relations constructed in the text, whose value is progressively determined during the reading process. Furthermore, reading is not a neutral operation: to read is to determine meaning. If it is said that, from a general semiotic point of view, meaning is stored in quantum semantic memories and is read/written by semantic machines, then the operation of “reading/writing” is analogous to the operation of measuring in quantum theory: in other terms, meaning is a value, and this implies an instance (not necessarily human) according to which values are valuable. Research limitations/implications The authors are not proposing a complete quantum semantics. At the present, quantum information retrieval can detect the presence of semantic relations. The authors suggest a way to characterize them, leaving open the problem on how to formalize the document as a vector in four-state semantic space. Practical implications A quantum turn shows deep semiotic implications on the approach to language, which shows an immanent semantic organization not reducible to syntax and morphology. This organization is probabilistic and indeterministic and explains to what extent text fixes the meaning of its lexical units. Social implications In the authors’ perspective, signification is not the exclusivity of a human subject. Criticizing Turing test, the great semiotic and cybernetic scholar Jurij Lotman wrote that if we identify “intelligent” and “human”, we raise the failings of an actual form of intelligence to the rank of an essential characteristic. On this line, meaning is considered as a feature of social, artificial and biological systems. Originality/value The adoption of quantum formalism seems in line with cybernetic framework, involving a probabilistic, non-cartesian point of view on meaning aimed to critically discuss the human–machine relation. Furthermore, Quantum theory (QT) implies a phenomenological point of view on the conditions of possibility of meaning.

Linguistics and Quantum Theory: Epistemological Perspectives

The article will explore the philosophical consequence on the notion of meaning of the adoption of a quantum geometry-based approach in linguistic disciplines such Information Retrieval, which importance grew with Internet and big data. The paper reconstructs an archaeology of the turn to probabilistic and a quantum perspectives in language research and its reasons, we considers some linguistic features which justify the adoption of a quantum perspective (non-distributiveness, failures of the substitutivity principle, periodicity of the meaning function), it underlines two features of quantum models (superposition and entanglement) which seems interesting to represent semantics, we hope to show how quantum models allow a convergence of the interest of different perspectives in philosophy of language.

Personalizing Information Retrieval Using an Extension of a Dung Argumentation

In this paper, we propose an architecture for personalizing information retrieval (IR), exploiting the interactions between the user and the social network. We use an extension of a Dung argumentation framework to show how the precision of the personalized information retrieval system could be improved. We use also social media and search history to define the user-profile which is represented by a restriction of a Description Logic.