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Abstract

The focus of this paper is an quality of ontologies as they relate to interoperating information systems. Quality is not a property of something but a judgment, so must be relative to some purpose, and generally involves recognition of design tradeoffs. Ontologies used for information systems interoperability have much in common with classification systems in information science, knowledge based systems, and programming languages, and inherit quality characteristics from each of these older areas. Factors peculiar to the new field lead to some additional characteristics relevant to quality, some of which are more profitably considered quality aspects not of the ontology as such, but of the environment through which the ontology is made available to its users. Suggestions are presented as to how to use these Factors in producing quality ontologies.

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Date

13.12.2017 14:17:22

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Abstract

In this document we provide an overall view of the state of the art in ontology alignment. It is organised as a description of the need for ontology alignment, a presentation of the techniques currently in use for ontology alignment and a presentation of existing systems. The state of the art is not restricted to any discipline and consider as some form of ontology alignment the work made on schema matching within the database area for instance. Heterogeneity problems on the semantic web can be solved, for some of them, by aligning heterogeneous ontologies. This is illustrated through a number of use cases of ontology alignment. Aligning ontologies consists of providing the corresponding entities in these ontologies. This process is precisely defined in deliverable D2.2.1. The current deliverable presents the many techniques currently used for implementing this process. These techniques are classified along the many features that can be found in ontologies (labels, structures, instances, semantics). They resort to many different disciplines such as statistics, machine learning or data analysis. The alignment itself is obtained by combining these techniques towards a particular goal (obtaining an alignment with particular features, optimising some criterion). Several combination techniques are also presented. Finally, these techniques have been experimented in various systems for ontology alignment or schema matching. Several such systems are presented briefly in the last section and characterized by the above techniques they rely on. The conclusion is that many techniques are available for achieving ontology alignment and many systems have been developed based on these techniques. However, few comparisons and few integration is actually provided by these implementations. This deliverable serves as a basis for considering further action along these two lines. It provide a first inventory of what should be evaluated and suggests what evaluation criterion can be used.

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Date

22. 3.2018 17:30:43

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Abstract

This picture will rapidly change. The twin challenges of massive information overload via the web and ubiquitous computers present us with an unavoidable task: developing techniques to handle multilingual and multi-modal information robustly and efficiently, with as high quality performance as possible. The most effective way for us to address such a mammoth task, and to ensure that our various techniques and applications fit together, is to start talking across the artificial research boundaries. Extending the current technologies will require integrating the various capabilities into multi-functional and multi-lingual natural language systems. However, at this time there is no clear vision of how these technologies could or should be assembled into a coherent framework. What would be involved in connecting a speech recognition system to an information retrieval engine, and then using machine translation and summarization software to process the retrieved text? How can traditional parsing and generation be enhanced with statistical techniques? What would be the effect of carefully crafted lexicons on traditional information retrieval? At which points should machine translation be interleaved within information retrieval systems to enable multilingual processing?

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Abstract

The SAC Subcommittee on Subject Relationships/Reference Structures was authorized at the 1995 Midwinter Meeting and appointed shortly before Annual Conference. Its creation was one result of a discussion of how (and why) to promote the display and use of broader-term subject heading references, and its charge reads as follows: To investigate: (1) the kinds of relationships that exist between subjects, the display of which are likely to be useful to catalog users; (2) how these relationships are or could be recorded in authorities and classification formats; (3) options for how these relationships should be presented to users of online and print catalogs, indexes, lists, etc. By the summer 1996 Annual Conference, make some recommendations to SAC about how to disseminate the information and/or implement changes. At that time assess the need for additional time to investigate these issues. The Subcommittee's work on each of the imperatives in the charge was summarized in a report issued at the 1996 Annual Conference (Appendix A). Highlights of this work included the development of a taxonomy of 165 subject relationships; a demonstration that, using existing MARC coding, catalog systems could be programmed to generate references they do not currently support; and an examination of reference displays in several CD-ROM database products. Since that time, work has continued on identifying term relationships and display options; on tracking research, discussion, and implementation of subject relationships in information systems; and on compiling a list of further research needs.