Nacéra Bennacer

Semantic mappings in description logics for spatio-temporal database schema integration

The interoperability problem arises in heterogeneous systems where different data sources coexist and there is a need for meaningful information sharing. One of the most representive realms of diversity of data representation is the spatio-temporal domain. Spatio-temporal data are most often described according to multiple and greatly diverse perceptions or viewpoints, using different terms and with heterogeneous levels of detail. Reconciling this heterogeneity to build a fully integrated database is known to be a complex and currently unresolved problem, and few formal approaches exist for the integration of spatio-temporal databases. The paper discusses the interoperation issue in the context of conceptual schema integration. Our proposal relies on two well-known formalisms: conceptual models and description logics. The MADS conceptual model with its multiple representation capabilities allows to fully describe semantics of the initial and integrated spatio-temporal schemas. Description logics are used to express the set of inter-schema mappings. Inference mechanisms of description logics allow us to check the compatibility of the semantic mappings and to propose different structural solutions for the integrated schema.

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.

Matching User Profiles Across Social Networks

Social Networking Sites, such as Facebook and Linkedin, are clear examples of the impact that the Web 2.0 has on people around the world, because they target an aspect of life that is extremely important to anyone: social relationships. The key to building a social network is the ability of finding people that we know in real life, which, in turn, requires those people to make publicly available some personal information, such as their names, family names, locations and birth dates, just to name a few. However, it is not uncommon that individuals create multiple profiles in several social networks, each containing partially overlapping sets of personal information. Matching those different profiles allows to create a global profile that gives a holistic view of the information of an individual. In this paper, we present an algorithm that uses the network topology and the publicly available personal information to iteratively match profiles across n social networks, based on those individuals who disclose the links to their multiple profiles. The evaluation results, obtained on a real dataset composed of around 2 million profiles, show that our algorithm achieves a high accuracy.

Incremental Ontology-Based Extraction and Alignment in Semi-structured Documents

SHIRI is an ontology-based system for integration of semi-structured documents related to a specific domain. The systems purpose is to allow users to access to relevant parts of documents as answers to their queries. SHIRI uses RDF/OWL for representation of resources and SPARQL for their querying. It relies on an automatic, unsupervised and ontology-driven approach for extraction, alignment and semantic annotation of tagged elements of documents. In this paper, we focus on the Extract-Align algorithm which exploits a set of named entity and term patterns to extract term candidates to be aligned with the ontology. It proceeds in an incremental manner in order to populate the ontology with terms describing instances of the domain and to reduce the access to extern resources such as Web. We experiment it on a HTML corpus related to call for papers in computer science and the results that we obtain are very promising. These results show how the incremental behaviour of Extract-Align algorithm enriches the ontology and the number of terms (or named entities) aligned directly with the ontology increases.

Representing and Reasoning for Spatiotemporal Ontology Integration

The World-Wide Web hosts many autonomous and heterogeneous information sources. In the near future each source may be described by its own ontology. The distributed nature of ontology development will lead to a large number of local ontologies covering overlapping domains. Ontology integration will then become an essential capability for effective interoperability and information sharing. Integration is known to be a hard problem, whose complexity increases particularly in the presence of spatiotemporal information. Space and time entail additional problems such as the heterogeneity of granularity used in representing spatial and temporal features. Spatio-temporal objects possess intrinsic characteristics that make then more complex to handle, and are usually related by specific relationships such as topological, metric and directional relations. The integration process must be enhanced to tackle mappings involving these complex spatiotemporal features. Recently, several tools have been developed to provide support for building mappings. The tools are usually based on heuristic approaches that identify structural and naming similarities [1]. They can be categorized by the type of inputs required for the analysis: descriptions of concepts in OBSERVER [2], concept hierarchies in iPrompt and AnchorPrompt [3] and instances of classes in GLUE [4] and FCA-Merge [5]. However, complex mappings, involving spatiotemporal features, require feedback from a user to further refine proposed mappings and to manually specify mappings not found by the tools.

Supporting semantic search on heterogeneous semi-structured documents

This paper presents SHIRI-Querying, an approach for semantic search on semi-structured documents. We propose a solution to tackle incompleteness and imprecision of semantic annotations of semistructured documents at querying time. We particularly introduce three elementary reformulations that rely on the notion of aggregation and on the document structure. We present the Dynamic Reformulation and Execution of Queries algorithm (DREQ) which combines these elementary transformations to construct reformulated queries w.r.t. a defined order relation. Experiments on two real datasets show that these reformulations greatly increase the recall and that returned answers are effectively ranked according to their precision.

Analyses and Fundamental ideas for a Relation Extraction Approach

Relation extraction is a difficult open research problem with important applications in several fields such as knowledge management, web mining, ontology building, intelligent systems, etc. In our research, we focus on extracting relations among the ontological concepts in order to build a domain ontology. In this paper, firstly, we answer some crucial questions related to the text analyses, the word features and the various relation types. Secondly, we use this theoretical analysis and some issues to define the fundamental ideas of our new approach. Our objective is to extract multi-type relations from the text analyses and the existent relations (in the concept hierarchy). Our approach combines a verb centered method, lexical analyses, syntactic and statistic ones. It is based on an exclusive interest to the document style during the statistic process, a rich contextual modelling that strengthens the term cooccurrence selection, a lexical analysis, a use of the existent relations in the concept hierarchy and a stepping between the various extracted relations to facilitate the evaluation made by the domain experts. Thirdly, we present an illustrative example to explain the previous ideas.

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.

Controlled knowledge base enrichment from web documents

The Linked Open Data initiative brought more and more RDF data sources to be published on the Web. However, these data sources contain relatively little information compared to the documents available on the surface Web. Many annotation tools have been proposed in the last decade for the automatic construction and enrichment of knowledge bases. But, while noticeable advances are achieved for the extraction of concept instances, the extraction of semantic relations remains a challenging task when the structures and the vocabularies of the target documents are heterogeneous. In this paper, we propose a novel approach, called REISA, which allows to enrich RDF/OWL knowledge bases with semantic relations using semistructured documents annotated with concept instances. REISA produces weighted relation instances without exploiting lexico-syntactic or structure regularities in the documents. Neighbor domain entities in the annotated documents are used to generate the first sets of candidate relations according to the domain and range axioms defined in a domain ontology. The construction of these candidate sets relies on automated semantic controls performed with (i) the existing knowledge bases and (ii) the (inverse) functionality of the target relations. The weighting of the selected relation candidates is performed according to the neighborhood distance between the annotated domain entities in the document. Experiments on two real web datasets show that (i) REISA allows to extract semantic relationships with interesting precision values reaching 76,5% and that (ii) the weighting method is effective for ranking the relation candidates according to their precision.

A semantic similarity measure for recommender systems

In the past few years, recommender systems and semantic web technologies have become main subjects of interest in the research community. In this paper, we present a domain independent semantic similarity measure that can be used in the recommendation process. This semantic similarity is based on the relations between the individuals of an ontology. The assessment can be done offline which allows time to be saved and then, get real-time recommendations. The measure has been experimented on two different domains: movies and research papers. Moreover, the generated recommendations by the semantic similarity have been evaluated by a set of volunteers and the results have been promising.