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Card sorts are a knowledge elicitation technique in which participants are given a collection of items and are asked to partition them into groups based on their own criteria. Information about the participant's knowledge structure is inferred from the groups formed and the names used to describe the groups through various methods ranging from simple quantitative statistical measures (e.g. co-occurrence frequencies) to complex qualitative methods (e.g. content analysis on the group names). This paper introduces a new technique for analyzing card sort data that uses quantitative measures to discover rich qualitative results. This method is based upon a distance metric between sorts that allows one to measure the similarity of groupings and then look for clusters of closely related sorts across individuals. By using software for computing these clusters, it is possible to identify common concepts across individuals, despite the use of different terminology. [ABSTRACT FROM AUTHOR]

Integration of ontologies of information sources and consumers is an important phase in achieving web-based interoperability. The present work describes an approach for identifying certain semantic conflicts while integrating ontologies of heterogeneous information sources. This paper is focused on the identification of homonymy and synonymy between elements in ontologies. In the present work the concepts of homonymy and synonymy are synonymous to naming conflicts and entity identifier conflicts, respectively, and partial synonymy is synonymous to schema isomorphism conflicts. The concept of the mask of interoperability is introduced for the identification of synonymy. The mask of interoperability is expressed in a declarative way as a set of rules, which can then be used for resolution of conflicts during integration of ontologies. As proof of concept, ontologies are implemented using the XML-based ontology language Ontology Web Language (OWL), and the rules are implemented using the emerging rule language Semantic Web Rule Language (SWRL). This representation in OWL and SWRL allows the ontology to be executable, flexibly extendable and platform-independent. The OWL facts and SWRL rules are used by the Jess and Bossam reasoning engine to identify semantic homonymy and synonymy. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Knowledge is an inherently dynamic entity continuously changing and evolving. In many cases, the coexistence of different versions of the same core knowledge is a necessity. So is the availability of the proper environment and tools to deal with knowledge versioning. In this paper, a framework of knowledge versioning management is proposed and implemented dealing with hybrid knowledge representation models using frames and rules. This framework facilitates knowledge version handling and maintenance, improving, in parallel, knowledge sharing and reuse. Knowledge components are stored in a set of tables and handled as data under the auspices of a database management system. The proper structure of tables and their relationships allows the creation of independent knowledge modules. Several knowledge modules can be assembled to construct higher level modules, which finally form versions of knowledge. Corresponding knowledge base versions consist of several knowledge modules easy to handle and process in various application areas. The proposed framework has been implemented and thoroughly examined in an application area of great importance, such as pest management. [ABSTRACT FROM AUTHOR]

Abstract: Granular computing is a mathematical tool in artificial intelligence. An information system is an important model in the field of artificial intelligence, and an information structure is its basic structure. Set‐valued information systems are the generalization of single‐valued information systems. This paper investigates information structures in a set‐valued information system from granular computing viewpoint, that is, information structures are viewed as granular structures. Information structures in a set‐valued information system are first described through set vectors. Then, the dependence between information structures in the same set‐valued information system is depicted from 2 aspects, and information distance for calculating the difference between information structures in the same set‐valued information system is proposed. Next, properties of information structures in a set‐valued information system are given by means of inclusion degree, information distance, condition information amount, and lower approximation operator. Finally, group, mapping, and lattice characterizations of information structures in a set‐valued information system are obtained. These results will be helpful for understanding the essence of structure and uncertainty in set‐valued information systems. [ABSTRACT FROM AUTHOR]