Frédéric Fürst

Heavyweight ontology engineering

An heavyweight ontology is a lightweight ontology (i.e. an ontology simply based on a hierarchy of concepts and a hierarchy of relations) enriched with axioms used to fix the semantic interpretation of concepts and relations Such an ontology can be a domain ontology, an ontology of representation, an ontology of PSM, etc In our work, we argue in favor of using a graph-based solution to deal with the different activities related to Heavyweight Ontology Engineering, in particular ontology representation, ontology operationalisation, ontology evaluation (i.e. verification and validation) and ontology matching Our approach consists in using the graph-based paradigm to represent all the components of an heavyweight ontology (i.e. Concepts, Relations and Axioms) and using graph homomorphism techniques to compare (at the conceptual level) the core components of an heavyweight ontology: the Axioms This explicit graph-based representation of axioms coupled with reasoning capabilities based on graphs homomorphism facilitates both (1) the definition of important notions for Heavyweight Ontology Engineering such as Compatible/Incompatible Axioms or Specialisation/Generalisation of Axioms and (2) the topological comparison of axioms, which in our work is used to define a new approach of ontology matching mainly based on axiom-based ontology morphisms.

Axiom-based ontology matching

: Managing multiple ontologies is now a core question in most of the applications that require semantic interoperability. The semantic web is surely the most significant application of this report: the current challenge is not to design, develop and deploy domain ontologies but to define semantic correspondences among multiple ontologies covering overlapping domains. In this paper, we introduce a new approach of ontology matching named axiom-based ontology matching. As this approach is founded on the use of axioms, it is mainly dedicated to heavyweight ontologies, but it can also be applied to lightweight ontologies as a complementary approach to the current techniques based on the analysis of natural language expressions, instances and/or taxonomical structures of ontologies. This new matching paradigm is defined in the context of the conceptual graphs model, where the projection (i.e. the main operator for reasoning with conceptual graphs which corresponds to homomorphism of graphs) is used as a means to semantically match the concepts and the relations of two ontologies through the explicit representation of the axioms in terms of conceptual graphs. We also introduce an ontology of representation, called MetaOCGL, dedicated to the reasoning of heavyweight ontologies at the meta-level.

The Green Computing Observatory: A Data Curation Approach for Green IT

The Green Computing Observatory (GCO) creates a full-fledged data curation process for green IT, providing a unique facility for the Computer Science and Engineering community. The first barrier to improved energy efficiency is the difficulty of collecting data on the energy consumption of individual components of data centers, and the lack of overall data collection. GCO collects monitoring data on energy consumption of a large computing center, and publish them through the Grid Observatory. A second barrier is making the collected data readily consistent and complete, as well as understandable for further exploitation. For this purpose, GCO opts for an ontological approach in order to rigorously define the semantics of the data (what is measured) and the context of their production (how are they acquired and/or calculated).

Ontological Engineering and Mathematical Knowledge Management: A Formalization of Projective Geometry

The work presented in this paper deals with the formalization of the ontology underlying projective geometry. This formalization is done by using the conceptual graph model which has been defined in the Artificial Intelligence community. Through this experiment, we endeavour to show that applying knowledge representation techniques to mathematical fields is a relevant way to improve the reliability and efficiency of tools dedicated to mathematical knowledge management. Our proposal is based on the construction of knowledge bases (defined according to ontologies) which must be considered as the core of any mathematical knowledge management tool such as mathematical search engines on the web, mathematical intelligent tutoring systems, mathematical theorem provers, etc. This paper also aims at highlighting the contributions provided by ontological engineering when dealing with mathematical knowledge management.

An Ontological Investigation in the Field of Computer Programs

Over the past decade, ontology research has extended into the field of computer programs. The work has sought to define conceptual descriptions of programs in order to master the latter’s design and use. Unfortunately, these efforts have only been partially successful. Here, we present the basis of a Core Ontology of Programs and Software (COPS) which integrates the field’s main concepts. But, above all, we emphasize the method used to build the ontology. Indeed, COPS specializes the DOLCE foundational ontology ([10]) as well as core ontologies of domains (e.g. artefacts, documents) situated on a higher abstraction level. This approach enables us to take into account the “dual nature” of computer programs, which can be considered as both syntactic entities (well-formed expressions in a programming language) and artefacts whose function is to enable computers to process information.

Ontology Personalization: An Approach Based on Conceptual Prototypicality

With the current emergence of Cognitive Sciences and the development of Knowledge Management applications in Social and Human Sciences, Subjective Knowledge becomes an unavoidable subject and a real challenge, which must be integrated and developed in Ontology Engineering and Ontology-based Information Retrieval. This paper introduces a new approach dedicated to the Personalization of a Domain Ontology. Inspired by works in Cognitive Psychology, our work is based on a process which aims at capturing the user-sensitive degree of truth of the categorisation process, that is the one which is really perceived by the end-user. Practically, this process consists in decorating the Specialisation/Generalisation links (i.e. the ISA links) of the hierarchy of concepts with a specific gradient. As this gradient is defined according to the three aspects of the semiotic triangle (i.e. intensional, extensional and expressional dimension), we call it Semiotic-based Prototypicality Gradient. It enrichs the initial formal semantics of an ontology by adding a pragmatics defined according to a context of use which depends on parameters like culture, educational background and/or emotional context of the end-user.

Prototypicality Gradient and Similarity Measure: A Semiotic-Based Approach Dedicated to Ontology Personalization

This paper introduces a new approach dedicated to the Ontology Personalization. Inspired by works in Cognitive Psychology, our work is based on a process which aims at capturing the user-sensitive relevance of the categorization process, that is the one which is really perceived by the end-user. Practically, this process consists in decorating the Specialization/Generalization links (i.e. the is-a links) of the hierarchy of concepts with 2 gradients. The goal of the first gradient, called Conceptual Prototypicality Gradient, is to capture the user-sensitive relevance of the categorization process, that is the one which is perceived by the end-user. As this gradient is defined according to the three aspects of the semiotic triangle (i.e. intentional, extensional and expressional dimension), we call it Semiotic based Prototypicality Gradient. The objective of the second gradient, called Lexical Prototypicality Gradient, is to capture the user-sensitive relevance of the lexicalization process, i.e. the definition of a set of terms used to denote a concept. These gradients enrich the initial formal semantics of an ontology by adding a pragmatics defined according to a context of use which depends on parameters like culture, educational background and/or emotional context of the end-user. This paper also introduces a new similarity measure also defined in the context of a semiotic-based approach. The first originality of this measure, called SEMIOSEM, is to consider the three semiotic dimensions of the conceptualization underlying an ontology. Thus, SEMIOSEM aims at aggregating and improving existing extensional-based and intentional-based measures. The second originality of this measure is to be context-sensitive, and in particular user-sensitive. This makes SEMIOSEM more flexible, more robust and more close to the end-user’s judgment than the other similarity measures which are usually only based on one aspect of a conceptualization and never take the end-user’s perceptions and purposes into account.

SemioSem: A Semiotic-Based Similarity Measure

This paper introduces a new similarity measure called SemioSem . The first originality of this measure, which is defined in the context of a semiotic-based approach, is to consider the three dimensions of the conceptualization underlying a domain ontology: the intension (i.e. the properties used to define the concepts), the extension (i.e. the instances of the concepts) and the expression (i.e. the terms used to denote both the concepts and the instances). Thus, SemioSem aims at aggregating and improving existing extensional-based and intensional-based measures, with an original expressional one. The second originality of this measure is to be context-sensitive, and in particular user-sensitive. Indeed, SemioSem is based on multiple informations sources: (1) a textual corpus, validated by the end-user, which must reflect the domain underlying the ontology which is considered, (2) a set of instances known by the end-user, (3) an ontology enriched with the perception of the end-user on how each property associated to a concept c is important for defining c and (4) the emotional state of the end-user. The importance of each source can be modulated according to the context of use and SemioSem remains valid even if one of the source is missing. This makes our measure more flexible, more robust and more close to the end-users judgment than the other similarity measures which are usually only based on one aspect of a conceptualization and never take the end-users perceptions and purposes into account.

Ontology Matching Based on the Comparison of Axioms Represented with Conceptual Graphs

Managing multiple ontologies is now a core question in most of the applications that require semantic interoperability. The semantic Web is surely the most significant application of this report: the current challenge is not to design, develop and deploy domain ontologies but to define semantic correspondences among multiple ontologies covering overlapping domains. In this paper, we introduce a new approach of ontology matching named axiom-based ontology matching. As this approach is founded on the use of axioms, it is mainly dedicated to heavyweight ontologies, but it can also be applied to lightweight ontologies as a complementary approach to the current techniques based on the analysis of natural language expressions, instances and/or taxonomical structures of ontologies. This new matching paradigm is defined in the context of the conceptual graphs model (CG), where the projection (i.e. the main operator for reasoning with CG which corresponds to homomorphism of graphs) is used as a means to semantically match the concepts and the relations of two ontologies through the explicit representation of the axioms in terms of conceptual graphs.