Alignment-based Partitioning of Large-scale Ontologies

Ontology alignment is an important task for information integration systems that can make different resources, described by various and heterogeneous ontologies, interoperate. However very large ontologies have been built in some domains such as medicine or agronomy and the challenge now lays in scaling up alignment techniques that often perform complex tasks. In this paper, we propose two partitioning methods which have been designed to take the alignment objective into account in the partitioning process as soon as possible. These methods transform the two ontologies to be aligned into two sets of blocks of a limited size. Furthermore, the elements of the two ontologies that might be aligned are grouped in a minimal set of blocks and the comparison is then enacted upon these blocks. Results of experiments performed by the two methods on various pairs of ontologies are promising

MESAM: A Protégé Plug-in for the Specialization of Models

International audienceNowadays, several efforts are focused on re-using generic platforms to create new systems, in order to make the design process easier and faster. Often, the designer has his own models and resources and would like to reuse the generic system over his resources. That means, he has to integrate his models and resources in the system, and then to directly reuse the generic system. But many problems occur. One of them is that the designer needs to translate his models into the specific format that understood by the system and to use the vocabulary specific to that system. Furthermore, he also needs to translate all the instantiations of his models (i.e. the resources and their metadata). We think that this task is tedious and time-consuming and we want to avoid it. Our objective is to allow the designer to reuse his models (his vocabulary) and his models' instantiations without any change of format or vocabulary. For example, a generic Adaptive Hypermedia System (AHS) is made of a generic adaptation model relying on generic user and domain models. The designer would like to integrate his models and instances in the generic models in order to reuse the generic adaptation engine. Specific systems can be obtained by specializing the generic models. However, this specialization process is not always easy to perform. It has to be supported to make the design process easier and faster. This paper focuses on assisting designers to specialize generic models using their own models. We aim to automate this process which has been so far entirely manual. Our objectives are twofold: to create a support for defining mappings between elements in generic models and elements in the designer's personal models and to help creating consistent and relevant models integrating the generic and specific ones and taking into account the mappings between them. The proposed approach relies on OWL1, a W3C standard and SWRL2, a W3C proposal

Entity Discovery and Annotation in Tables

International audienceThe Web is rich of tables (e.g., HTML tables, speadsheets, Google Fusion tables) that host a considerable wealth of high-quality relational data. Unlike unstructured texts, tables usually favour the automatic extraction of data because of their regular structure and properties. The data extraction is usually complemented by the annotation of the table, which determines its semantics by identifying a type for each column, the relations between columns, if any, and the entities that occur in each cell. In this paper, we focus on the problem of discovering and annotating entities intables. More specifically, we describe an algorithm that identifies the rows of a table that contain information on entities of specific types (e.g., restaurant, museum, theatre) derived from an ontology and determines the cells in which the names of those entities occur. We implemented this algorithm while developing a faceted browser over a repository of RDF data on points of interest of cities that we extracted from Google Fusion Tables. We claim that our algorithm complements the existing approaches, which annotate entities in a table based on a pre-compiled reference catalogue that lists the types of a finite set of entities; as a result, they are unable to discover and annotate entities that do not belong to the reference catalogue. Instead, we train our algorithm to look for information on previously unseen entities on the Web so as to annotate them with the correct type

Alignement de taxonomies pour l'interrogation de sources d'information hétérogènes

Intégrer des sources d'information hétérogènes permet un accès unifié sans modification du contenu. Les schémas des sources sont mis en correspondance de façon à ce qu'il soit possible d'accéder à tout un ensemble de documents provenant de sources multiples, à partir d'un système d'interrogation unique. La spécification de ces mises en correspondance, ou mappings, est ainsi au cœur de l'intégration. Nous proposons d'utiliser différentes techniques d'alignement de taxonomies pour automatiser leur génération. Ces techniques ont été implémentées dans un outil logiciel TaxoMap qui recherche les mappings et, en cas d'échec, donne des indications pour aider les utilisateurs à les spécifier eux-mêmes. Nous présentons et discutons des résultats issus d'expériences réalisées dans le domaine de la microbiologie. Nous testons TaxoMap sur différentes taxonomies issues de domaines variés

GéOnto : Enrichissement d'une taxonomie de concepts topographiques

National audienceIn this paper we present the GéOnto project, aiming in particular to build an ontology of topographic concepts. This ontology is made by enrichment of a first taxonomy developed beforehand, through the analysis of two types of textual documents: technical database specifications and description of journeys. This work relies on natural language processing and ontology alignment techniques, as well as external knowledge resources such as dictionaries and gazetteers

Analyses linguistiques et techniques d'alignement pour créer et enrichir une ontologie topographique

National audienceOne of the goals of the GéOnto project is to build an ontology of topographic concepts. This ontology results from the enrichment of a first taxonomy developed beforehand, through the analysis of two types of textual documents: technical database specifications and description of journeys. This work relies on natural language processing and ontology alignment techniques, as well as external knowledge resources such as dictionaries and gazetteers.Dans cet article, nous présentons le projet GéOnto dont un des buts est de construire une ontologie de concepts topographiques. Cette ontologie est réalisée par enrichissement d'une première taxonomie de termes réalisée précédemment, et ce grâce à l'analyse de deux types de documents textuels : des spécifications techniques de bases de données et des récits de voyage. Cet enrichissement s'appuie sur des techniques automatiques de traitement du langage et d'alignement d'ontologies, ainsi que sur des connaissances externes comme des dictionnaires et des bases de toponymes

Analyzing the Evolution of Semantic Correspondences between SNOMED CT and ICT-9-CM

International audienceThe combined use of Knowledge Organizations Systems (KOS) including ontologies, terminologies or codification schemas has widespread in e-health systems over the past decades due to semantic interoperability reasons. However, the dynamic aspect of KOS forces knowledge engineers to maintain KOS elements, as well as semantic correspondences between KOS up-to-date. This is crucial to keep the underlying systems exploiting these KOS consistent over time. In this paper we provide a pragmatic analysis of the evolution of mappings between SNOMED CT and ICD-9-CM affected by the evolution of these two KOS

Requirements for Implementing Mappings Adaptation Systems

International audienceOntologies, or more generally speaking, Knowledge Organization Systems (KOS) have been developed to support the correct interpretation of shared data in collaborative applications. The quantity and the heterogeneity of domain knowledge often require several KOS to describe their content. In order to assure unambiguous interpretation, overlapped concepts of different, but domain-related KOS are semantically connected via mappings. However, in various domains, KOS periodically evolve creating the necessity of reviewing the validity of associated mappings. The size of KOS remains a barrier for a manual review of mappings, and rather requires the support of (semi-) automatic solutions. This article describes our experiences in understanding how KOS evolution affects mappings. We present our lessons learned from various empirical experiments, and we derive primary elements and requirements for improving the automation of mapping maintenance

Entity Discovery and Annotation in Tables

International audienceThe Web is rich of tables (e.g., HTML tables, speadsheets, Google Fusion tables) that host a considerable wealth of high-quality relational data. Unlike unstructured texts, tables usually favour the automatic extraction of data because of their regular structure and properties. The data extraction is usually complemented by the annotation of the table, which determines its semantics by identifying a type for each column, the relations between columns, if any, and the entities that occur in each cell. In this paper, we focus on the problem of discovering and annotating entities intables. More specifically, we describe an algorithm that identifies the rows of a table that contain information on entities of specific types (e.g., restaurant, museum, theatre) derived from an ontology and determines the cells in which the names of those entities occur. We implemented this algorithm while developing a faceted browser over a repository of RDF data on points of interest of cities that we extracted from Google Fusion Tables. We claim that our algorithm complements the existing approaches, which annotate entities in a table based on a pre-compiled reference catalogue that lists the types of a finite set of entities; as a result, they are unable to discover and annotate entities that do not belong to the reference catalogue. Instead, we train our algorithm to look for information on previously unseen entities on the Web so as to annotate them with the correct type

Leveraging Adaptive Web with Adaptation Patterns

1529In this paper, we propose a pattern-based approach to express adaptation strategies in a semi-automatic and simple way. This allows the creator of an adaptive system to define elementary adaptations by using and instantiating adaptation patterns. These elementary adaptations can then be combined, allowing to specify adaptation strategies in an easy and flexible manner. We distinguish adaptive navigation according to two main criteria: the selection operations performed in order to obtain resources being proposed to the user and the elements of the domain model involved in the selection process. We present a taxonomy of elementary adaptive navigation techniques. Our approach has been validated using the GLAM adaptation engine. We showed that the GLAM rules can be automatically generated from patternbased adaptations