Brigitte Safar

Bio: Brigitte Safar is an academic researcher from University of Paris-Sud. The author has contributed to research in topics: Ontology alignment & Ontology (information science). The author has an hindex of 11, co-authored 34 publications receiving 355 citations. Previous affiliations of Brigitte Safar include University of Pau and Pays de l'Adour & University of Paris.

TaxoMap in the OAEI 2009 alignment contest

Abstract: TaxoMap is an alignment tool which aims to discover rich correspondences between concepts. It performs an oriented alignment (from a source to a target ontology) and takes into account labels and sub-class descriptions. This new implementation of TaxoMap reduces significantly runtime and enables parameterization by specifying the ontology language and different thresholds used to extract different mapping relations. It improves terminological techniques, with a better use of TreeTagger and introduces new structural techniques which take into account the structure of ontology. Special effort has been made to handle large-scale ontologies by partitioning input ontologies into modules to align. We conclude the paper by pointing out the necessary improvements that need to be made.

TaxoMap alignment and refinement modules: results for OAEI 2010

Abstract: TaxoMap is an alignment tool which aims to discover rich correspondences between concepts (equivalence relations (isEq), subsumption relations (isA) and their inverse (isMoreGnl) or proximity relations (isClose)). It performs an oriented alignment (from a source to a target ontology) and takes into account labels and sub-class descriptions. This new implementation of TaxoMap uses a pattern-based approach implemented in the TaxoMap Framework helping an engineer to refine mappings to take into account specific conventions used in ontologies.

TaxoMap in the OAEI 2008 alignment contest

Abstract: TaxoMap is an alignment tool which aim is to discover rich correspondences between concepts. It performs an oriented alignment (from a source to a target ontology) and takes into account labels and sub-class descriptions. Our participation in last year edition of the competition have put the emphasis on certain limits. TaxoMap 2 is a new implementation of TaxoMap that reduces significantly runtime and enables parameterization by specifying the ontology language and different thresholds used to extract different mapping relations. The new implementation stresses on terminological techniques, it takes into account synonymy, and multi-label description of concepts. Special effort was made to handle large-scale ontologies by partitioning input ontologies into modules to align. We conclude the paper by pointing out the necessary improvements that need to be made.

Repairing queries in a mediator approach

Abstract: In this paper, we study unsatisfiable queries posed to a mediator in an information integration system and expressed in the logical formalism of the information integration system PICSEL2 First, we characterise conflicts as the minimal causes of the unsatisfiability of a query Then, we produce its set of repairs: a repair is a query that does not generate any conflict and that has a common generalisation with the initial query and is semantically close to it

OntoRefiner, a user query refinement interface usable for Semantic Web Portals.

Abstract: We present a user interface, the OntoRefiner system, for helping the user to navigate numerous retrieved documents after a search querying a semantic portal which integrates a very important number of documents. Retrieved answers are filtered and the user could be provided only with the answers which are, according to him, the most relevant. The refinement process is based on two technologies, dynamic clustering close to Galois lattice structure combined to the use of a domain ontology. The Galois lattice structure provides a sound basis for the query refinement process. However, its construction as a whole is a very costly process. So, we propose an approach based on the use of a domain ontology, avoiding the construction of the whole Galois lattice. In the paper, we present the algorithm and experimental results.

Ontology Matching

Abstract: Ontologies tend to be found everywhere. They are viewed as the silver bullet for many applications, such as database integration, peer-to-peer systems, e-commerce, semantic web services, or social networks. However, in open or evolving systems, such as the semantic web, different parties would, in general, adopt different ontologies. Thus, merely using ontologies, like using XML, does not reduce heterogeneity: it just raises heterogeneity problems to a higher level. Euzenat and Shvaikos book is devoted to ontology matching as a solution to the semantic heterogeneity problem faced by computer systems. Ontology matching aims at finding correspondences between semantically related entities of different ontologies. These correspondences may stand for equivalence as well as other relations, such as consequence, subsumption, or disjointness, between ontology entities. Many different matching solutions have been proposed so far from various viewpoints, e.g., databases, information systems, and artificial intelligence. The second edition of Ontology Matching has been thoroughly revised and updated to reflect the most recent advances in this quickly developing area, which resulted in more than 150 pages of new content. In particular, the book includes a new chapter dedicated to the methodology for performing ontology matching. It also covers emerging topics, such as data interlinking, ontology partitioning and pruning, context-based matching, matcher tuning, alignment debugging, and user involvement in matching, to mention a few. More than 100 state-of-the-art matching systems and frameworks were reviewed. With Ontology Matching, researchers and practitioners will find a reference book that presents currently available work in a uniform framework. In particular, the work and the techniques presented in this book can be equally applied to database schema matching, catalog integration, XML schema matching and other related problems. The objectives of the book include presenting (i) the state of the art and (ii) the latest research results in ontology matching by providing a systematic and detailed account of matching techniques and matching systems from theoretical, practical and application perspectives.

Information retrieval

Abstract: The major change in the second edition of this book is the addition of a new chapter on probabilistic retrieval. This chapter has been included because I think this is one of the most interesting and active areas of research in information retrieval. There are still many problems to be solved so I hope that this particular chapter will be of some help to those who want to advance the state of knowledge in this area. All the other chapters have been updated by including some of the more recent work on the topics covered. In preparing this new edition I have benefited from discussions with Bruce Croft, The material of this book is aimed at advanced undergraduate information (or computer) science students, postgraduate library science students, and research workers in the field of IR. Some of the chapters, particularly Chapter 6 * , make simple use of a little advanced mathematics. However, the necessary mathematical tools can be easily mastered from numerous mathematical texts that now exist and, in any case, references have been given where the mathematics occur. I had to face the problem of balancing clarity of exposition with density of references. I was tempted to give large numbers of references but was afraid they would have destroyed the continuity of the text. I have tried to steer a middle course and not compete with the Annual Review of Information Science and Technology. Normally one is encouraged to cite only works that have been published in some readily accessible form, such as a book or periodical. Unfortunately, much of the interesting work in IR is contained in technical reports and Ph.D. theses. For example, most the work done on the SMART system at Cornell is available only in reports. Luckily many of these are now available through the National Technical Information Service (U.S.) and University Microfilms (U.K.). I have not avoided using these sources although if the same material is accessible more readily in some other form I have given it preference. I should like to acknowledge my considerable debt to many people and institutions that have helped me. Let me say first that they are responsible for many of the ideas in this book but that only I wish to be held responsible. My greatest debt is to Karen Sparck Jones who taught me to research information retrieval as an experimental science. Nick Jardine and Robin …

Ontology matching

Abstract: We present a literature review regarding articles on ontology matching published in the last decade.It serves the purpose of offering an up-to-date review of the field and showing its evolution trends.Over 1600 papers have been sorted according to a classification framework that we have defined.This framework helps in identifying the distribution of the load work in the last decade.Practitioners have been consulted to contrast and validate the results of the review. The amount of research papers published nowadays related to ontology matching is remarkable and we believe that reflects the growing interest of the research community. However, for new practitioners that approach the field, this amount of information might seem overwhelming. Therefore, the purpose of this work is to help in guiding new practitioners get a general idea on the state of the field and to determine possible research lines.To do so, we first perform a literature review of the field in the last decade by means of an online search. The articles retrieved are sorted using a classification framework that we propose, and the different categories are revised and analyzed. The information in this review is extended and supported by the results obtained by a survey that we have designed and conducted among the practitioners.

Ontology alignment for linked open data

Abstract: The Web of Data currently coming into existence through the Linked Open Data (LOD) effort is a major milestone in realizing the Semantic Web vision. However, the development of applications based on LOD faces difficulties due to the fact that the different LOD datasets are rather loosely connected pieces of information. In particular, links between LOD datasets are almost exclusively on the level of instances, and schema-level information is being ignored. In this paper, we therefore present a system for finding schema-level links between LOD datasets in the sense of ontology alignment. Our system, called BLOOMS, is based on the idea of bootstrapping information already present on the LOD cloud. We also present a comprehensive evaluation which shows that BLOOMS outperforms state-of-the-art ontology alignment systems on LOD datasets. At the same time, BLOOMS is also competitive compared with these other systems on the Ontology Evaluation Alignment Initiative Benchmark datasets.

Automatic Ontology Matching via Upper Ontologies: A Systematic Evaluation

Abstract: ?Ontology matching? is the process of finding correspondences between entities belonging to different ontologies. This paper describes a set of algorithms that exploit upper ontologies as semantic bridges in the ontology matching process and presents a systematic analysis of the relationships among features of matched ontologies (number of simple and composite concepts, stems, concepts at the top level, common English suffixes and prefixes, and ontology depth), matching algorithms, used upper ontologies, and experiment results. This analysis allowed us to state under which circumstances the exploitation of upper ontologies gives significant advantages with respect to traditional approaches that do no use them. We run experiments with SUMO-OWL (a restricted version of SUMO), OpenCyc, and DOLCE. The experiments demonstrate that when our ?structural matching method via upper ontology? uses an upper ontology large enough (OpenCyc, SUMO-OWL), the recall is significantly improved while preserving the precision obtained without upper ontologies. Instead, our ?nonstructural matching method? via OpenCyc and SUMO-OWL improves the precision and maintains the recall. The ?mixed method? that combines the results of structural alignment without using upper ontologies and structural alignment via upper ontologies improves the recall and maintains the F-measure independently of the used upper ontology.