Microtheories for SDI - Accounting for diversity of local conceptualisations at a global level

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.The categorization and conceptualization of geographic features is fundamental to cartography, geographic information retrieval, routing applications, spatial decision support and data sharing in general. However, there is no standard conceptualization of the world. Humans conceptualize features based on numerous factors including cultural background, knowledge, motivation and particularly space and time. Thus, geographic features are prone to multiple, context-dependent conceptualizations reflecting local conditions. This creates semantic heterogeneity and undermines interoperability. Standardization of a shared definition is often employed to overcome semantic heterogeneity. However, this approach loses important local diversity in feature conceptualizations and may result in feature definitions which are too broad or too specific. This work proposes the use of microtheories in Spatial Data Infrastructures, such as INSPIRE, to account for diversity of local conceptualizations while maintaining interoperability at a global level. It introduces a novel method of structuring microtheories based on space and time, represented by administrative boundaries, to reflect variations in feature conceptualization. A bottom-up approach, based on non-standard inference, is used to create an appropriate global-level feature definition from the local definitions. Conceptualizations of rivers, forests and estuaries throughout Europe are used to demonstrate how the approach can improve the INSPIRE data model and ease its adoption by European member states

Computing the least common subsumer and the most specific concept in the presence of cyclic ALN-concept descriptions

Computing least common subsumers (lcs) and most specific concepts (msc) are inference tasks that can be used to support the „bottom up” construction of knowledge bases for KR systems based on description logic. For the description logic ALN, the msc need not always exist if one restricts the attention to acyclic concept descriptions. In this paper, we extend the notions lcs and msc to cyclic descriptions, and show how they can be computed. Our approach is based on the automata-theoretic characterizations of fixed-point semantics for cyclic terminologies developed in previous papers.An abridged version of this technical report has been published at KI'98

Computing the least common subsumer and the most specific concept in the presence of cyclic ALN-concept descriptions

Computing least common subsumers (lcs) and most specific concepts (msc) are inference tasks that can be used to support the „bottom up” construction of knowledge bases for KR systems based on description logic. For the description logic ALN, the msc need not always exist if one restricts the attention to acyclic concept descriptions. In this paper, we extend the notions lcs and msc to cyclic descriptions, and show how they can be computed. Our approach is based on the automata-theoretic characterizations of fixed-point semantics for cyclic terminologies developed in previous papers.An abridged version of this technical report has been published at KI'98

Mesures sémantiques à base de connaissance : de la théorie aux applicatifs

The notions of semantic proximity, distance, and similarity have long been considered essential for the elaboration of numerous cognitive processes, and are therefore of major importance for the communities involved in the development of artificial intelligence. This thesis studies the diversity of semantic measures which can be used to compare lexical entities, concepts and instances by analysing corpora of texts and ontologies. Strengthened by the development of Knowledge Representation and Semantic Web technologies, these measures are arousing increasing interest in both academic and industrial fields.This manuscript begins with an extensive state-of-the-art which presents numerous contributions proposed by several communities, and underlines the diversity and interdisciplinary nature of this domain. Thanks to this work, despite the apparent heterogeneity of semantic measures, we were able to distinguish common properties and therefore propose a general classification of existing approaches. Our work goes on to look more specifically at measures which take advantage of ontologies expressed by means of semantic graphs, e.g. RDF(S) graphs. We show that these measures rely on a reduced set of abstract primitives and that, even if they have generally been defined independently in the literature, most of them are only specific expressions of generic parametrised measures. This result leads us to the definition of a unifying theoretical framework for semantic measures, which can be used to: (i) design new measures, (ii) study theoretical properties of measures, (iii) guide end-users in the selection of measures adapted to their usage context. The relevance of this framework is demonstrated in its first practical applications which show, for instance, how it can be used to perform theoretical and empirical analyses of measures with a previously unattained level of detail. Interestingly, this framework provides a new insight into semantic measures and opens interesting perspectives for their analysis.Having uncovered a flagrant lack of generic and efficient software solutions dedicated to (knowledge-based) semantic measures, a lack which clearly hampers both the use and analysis of semantic measures, we consequently developed the Semantic Measures Library (SML): a generic software library dedicated to the computation and analysis of semantic measures. The SML can be used to take advantage of hundreds of measures defined in the literature or those derived from the parametrised functions introduced by the proposed unifying framework. These measures can be analysed and compared using the functionalities provided by the library. The SML is accompanied by extensive documentation, community support and software solutions which enable non-developers to take full advantage of the library. In broader terms, this project proposes to federate the several communities involved in this domain in order to create an interdisciplinary synergy around the notion of semantic measures: http://www.semantic-measures-library.org This thesis also presents several algorithmic and theoretical contributions related to semantic measures: (i) an innovative method for the comparison of instances defined in a semantic graph - we underline in particular its benefits in the definition of content-based recommendation systems, (ii) a new approach to compare concepts defined in overlapping taxonomies, (iii) algorithmic optimisation for the computation of a specific type of semantic measure, and (iv) a semi-supervised learning-technique which can be used to identify semantic measures adapted to a specific usage context, while simultaneously taking into account the uncertainty associated to the benchmark in use. These contributions have been validated by several international and national publications.Les notions de proximité, de distance et de similarité sémantiques sont depuis longtemps jugées essentielles dans l’élaboration de nombreux processus cognitifs et revêtent donc un intérêt majeur pour les communautés intéressées au développement d'intelligences artificielles. Cette thèse s'intéresse aux différentes mesures sémantiques permettant de comparer des unités lexicales, des concepts ou des instances par l'analyse de corpus de textes ou de représentations de connaissance (i.e. ontologies). Encouragées par l'essor des technologies liées à l'Ingénierie des Connaissances et au Web sémantique, ces mesures suscitent de plus en plus d'intérêt à la fois dans le monde académique et industriel.Ce manuscrit débute par un vaste état de l'art qui met en regard des travaux publiés dans différentes communautés et souligne l'aspect interdisciplinaire et la diversité des recherches actuelles dans ce domaine. Cela nous a permis, sous l'apparente hétérogénéité des mesures existantes, de distinguer certaines propriétés communes et de présenter une classification générale des approches proposées. Par la suite, ces travaux se concentrent sur les mesures qui s'appuient sur une structuration de la connaissance sous forme de graphes sémantiques, e.g. graphes RDF(S). Nous montrons que ces mesures reposent sur un ensemble réduit de primitives abstraites, et que la plupart d'entre elles, bien que définies indépendamment dans la littérature, ne sont que des expressions particulières de mesures paramétriques génériques. Ce résultat nous a conduits à définir un cadre théorique unificateur pour les mesures sémantiques. Il permet notamment : (i) d'exprimer de nouvelles mesures, (ii) d'étudier les propriétés théoriques des mesures et (iii) d'orienter l'utilisateur dans le choix d'une mesure adaptée à sa problématique. Les premiers cas concrets d'utilisation de ce cadre démontrent son intérêt en soulignant notamment qu'il permet l'analyse théorique et empirique des mesures avec un degré de détail particulièrement fin, jamais atteint jusque-là. Plus généralement, ce cadre théorique permet de poser un regard neuf sur ce domaine et ouvre de nombreuses perspectives prometteuses pour l'analyse des mesures sémantiques.Le domaine des mesures sémantiques souffre d'un réel manque d'outils logiciels génériques et performants ce qui complique à la fois l'étude et l'utilisation de ces mesures. En réponse à ce manque, nous avons développé la Semantic Measures Library (SML), une librairie logicielle dédiée au calcul et à l'analyse des mesures sémantiques. Elle permet d'utiliser des centaines de mesures issues à la fois de la littérature et des fonctions paramétriques étudiées dans le cadre unificateur introduit. Celles-ci peuvent être analysées et comparées à l'aide des différentes fonctionnalités proposées par la librairie. La SML s'accompagne d'une large documentation, d'outils logiciels permettant son utilisation par des non informaticiens, d'une liste de diffusion, et de façon plus large, se propose de fédérer les différentes communautés du domaine afin de créer une synergie interdisciplinaire autour la notion de mesures sémantiques : http://www.semantic-measures-library.orgCette étude a également conduit à différentes contributions algorithmiques et théoriques, dont (i) la définition d'une méthode innovante pour la comparaison d'instances définies dans un graphe sémantique - nous montrons son intérêt pour la mise en place de système de recommandation à base de contenu, (ii) une nouvelle approche pour comparer des concepts représentés dans des taxonomies chevauchantes, (iii) des optimisations algorithmiques pour le calcul de certaines mesures sémantiques, et (iv) une technique d'apprentissage semi-supervisée permettant de cibler les mesures sémantiques adaptées à un contexte applicatif particulier en prenant en compte l'incertitude associée au jeu de test utilisé. Ces travaux ont été validés par plusieurs publications et communications nationales et internationales