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Context Representation in Domain Ontologies and Its Use for Semantic Integration of Data

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Context Representation in Domain Ontologies and Its Use for Semantic Integration of Data Context Representation in Domain Ontologies and Its Use for Semantic Integration of Data Guy Pierra Laboratory of Applied Computer Science (LISI) National Engineering School for Mechanics and Aerotechnics (ENSMA), Poitiers 86960 Futuroscope Cedex - France [email protected] Abstract. The goal of this paper is to identify various aspects of context-awareness needed to facilitate semantics integration of data, and to discuss how this knowledge may be represented within ontologies. We first present a taxonomy of ontologies and we show how various kinds of ontologies may cooperate. Then, we compare ontologies and concep- tual models. We claim that their main difference is the consensual na- ture of ontologies when conceptual models are specifically designed for one particular target system. Reaching consensus, in turn, needs specific models of which context dependency has been represented and mini- mized. We identify five principles for making ontologies less contextual than models and suitable for data integration and we show, as an exam- ple, how these principles have been implemented in the PLIB ontology model developed for industrial data integration. Finally, we suggest a road map for switching from conventional databases to ontology-based databases without waiting until standard ontologies are available in every domains. 1 Introduction A number of computer science problems, including heterogeneous database inte- gration, natural language processing, intelligent document retrieval would ben- efit from the capability to model the absolute meaning of things of a domain, independently of any particular use of them. In the structured-data universe, information is represented as data. Indeed, many studies have been performed to integrate heterogeneous and autonomous databases [8], in particular using domain ontologies [33]. Distributed architecture models have been developed, where mediators [34] provide uniform access to heterogeneous data sources. Me- diators export integrated schemas that reconcile data both at the structural (schematic heterogeneity) and at the meaning level (semantic heterogeneity). If large progresses have been made to automate schema integration at the struc- tural level, using in particular new model management techniques [3], the major challenge remains the automation of semantic integration of several heteroge- neous schemas. Such an automation would need enabling programs to identify unambiguously: S. Spaccapietra (Ed.): Journal on Data Semantics X, LNCS 4900, pp. 174–211, 2008. c Springer-Verlag Berlin Heidelberg 2008 Context Representation in Domain Ontologies 175 – those data having exactly the same semantic meaning, – those data that are similar and may be converted in or compared with each others by given processes, and – those data having no semantic commonality. In the above list, data may be either atomic or structured data like tuple or entity instance. On the Internet, data is not the only means for representing information, another one is largely used, namely documents. Large progresses were achieved by search engines to retrieve over the Internet the most relevant documents with respect to a user query expressed as a sentence of words. Un- fortunately, if semantic of both the query and target documents are not made computer-sensible, it is impossible to retrieve documents dealing with the query subject without using exactly the same words (e.g., worker instead of employee, size instead of length or convertible instead of car). Here again some kinds of computer interpretable representation of word meaning is needed: – in a first step to improve search engine in order to retrieve which documents are semantically relevant for a topic defined by a set of words, even when the same words are not used, and – in a second step, to retrieve which documents might provide answer to a user query. Both kinds of information integration requiring explicit representation of mean- ing, these last ten years a lot of work has been done to develop domain ontology models1 intended to capture the a priori nature of reality of some Universe of Discourse (UoD), as independently as possible from any particular use of this reality. Once defined, such representations may then be used to reconcile various information sources addressing this UoD at the meaning level. The word ontology is now extensively used in a number of computer science domains, including e.g., knowledge management, natural language processing, database, object oriented modeling. Ifthereseemstobesomeconsensuson what an ontology structure should be - categories (classes), properties, logical relationships - the focus of the various approaches is so different that the same word seems to represent quite different realities, and that ontologies developed, e.g., for natural language processing seems to be nearly useless for e.g., database integration, and conversely. The goal of this paper is twofold. The first goal is to investigate the concept of an ontology in a structured-data integration perspective. We claim that the major difference between ontologies and conceptual models is the existence of a consensus that founds ontologies as a shared meaning, and that consensus, in turn, needs representation of the modeling context. As an example, it is easy to reach consensus on the fact that the resistance is an essential property of a resistor.Buttheresistance depends upon the temperature where the resistance is measured, and it is nearly impossible to reach consensus on the temperature 1 In this paper, we only consider domain ontologies, and not upper-level generic on- tologies. Thus, for short, ontology means domain ontology. 176 G. Pierra where the resistance should be measured since this temperature depends upon the resistor target usage. To include in the ontology model a mechanism allowing to represent with each resistance value the temperature where it was evaluated (value-evaluation context awareness) enables consensus since it allows each user category to select its own resistance measuring process while making explicit commonality and differences. Thus, we propose five principles allowing to make ontologies much more generic through context representation, and thus more suitable for heterogeneous data integration. The second goal is to show how these principles have been implemented in the ontology model we have devel- oped over the last 15 years. Discussed within an international standardization project (see Annex A), the PLIB ontology model (officially ISO 13584), was initially developed for giving meaning to technical data and for providing for automatic integration of heterogeneous engineering data sources. The overview of PLIB presented in this paper allows both to illustrate the context representa- tion principles that we propose, and to show some typical uses of this ontology model. The content of this paper is as follows. In the next section, we discuss the various kinds of ontologies needed for representing semantics. We distinguish between document-oriented linguistic ontology (LO) and information-modeling- oriented conceptual ontology (CO). In section 3, we investigate the differences between ontologies and models, and we propose mechanisms to represent both modeling and value context within an ontology. In section 4, we outline how PLIB ontologies are specified (and exchanged) thanks to an executable specification de- fined in the EXPRESS data specification language, and we present how context- awareness mechanisms are represented in the PLIB ontology model. In section 5, we present a formal model of PLIB ontologies, including the mapping capa- bilities to external ontologies, and we outline how such ontologies may be used to integrate various heterogeneous data bases. We suggest, in section 6, a road map for switching from conventional databases to ontology-based databases. A discussion of related work regarding context representation, ontology models and data integration is presented in section 7. Conclusion is presented in section 8. Some standards being quoted in the paper, Annex A outlines the standardization activities around PLIB. 2 Concept Ontologies and Linguistic Ontologies Since the term ontology was borrowed from philosophy and introduced in the computer science vocabulary, many definitions have been offered. The most com- monly cited definition is one by T. Gruber ”An ontology is an explicit specifica- tion of a conceptualization”, therefore ”shared ontologies” provide for ”knowl- edge sharing” [15]. In all the ontology models, such a conceptualization consists of three parts : – primitive items of the ontology (where items are either classes or properties) are those items ”for which we are not able to give a complete axiomatic Context Representation in Domain Ontologies 177 definition; we must rely on textual documentation and a background of knowledge shared with the reader” [15], – defined items are those items for which the ontology provides a complete axiomatic definition by means of necessary and sufficient conditions, and – logical relationships (or inference rules) provide for reasoning over ontology items, and for supporting some problem-solving activities over the ontology domain. The agreed definition and structure description leave open what may be consid- ered as the major criteria for classifying ontologies and ontology models: whether their area of interest consists of beings (what does exist in the world) or of words (how beings are apprehended and reflected in a particular natural language). We call linguistic ontologies (LO) those
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