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Ontologies for Knowledge Management
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7.1. INTRODUCTION
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This chapter discusses a number of aspects of the usage of ontologies in Knowledge Management (KM) context, as well as in some speci c Semantic Web applications. The semantic annotation of unstructured content, that is linking it to ontologies, is relevant to the subject, but not addressed in detail here annotation and its usage for indexing, hyperlinking, visualization, and navigation is discussed in 3. We start with a simple motivating scenario, showing some bene ts of using ontologies in database-like setting. Next, we clarify the meaning and typical usage of some terms related to ontologies (e.g., taxonomy, knowledge base, metadata) and discuss different types of ontology: upper-level versus domain-speci c; lightweight versus heavyweight, and so on. Two of the possible roles of ontologies are discussed: as a database schema and as a topic hierarchy. The different requirements and restrictions speci c to the ontologies used in these roles are commented. The remainder of the chapter presents the PROTON ontology, as an example for an ontology designed to support a number of KM and Semantic Web applications. Some concrete ontology design examples and recommendations are given in this context.
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Semantic Web Technologies: Trends and Research in Ontology-based Systems John Davies, Rudi Studer, Paul Warren # 2006 John Wiley & Sons, Ltd
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ONTOLOGIES FOR KNOWLEDGE MANAGEMENT
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7.2. ONTOLOGY USAGE SCENARIO
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Formal knowledge representation (KR) is about building models1 of the world, of a particular domain or a problem, which allow for automatic reasoning and interpretation. Such formal models are called ontologies and can be used to provide formal semantics (i.e., machine-interpretable meaning) to any sort of information: databases, catalogs, documents, web pages, etc. The association of information with such formal models makes the information much amenable to machine processing and interpretation. Semantic repositories2 allow for storage, querying, and management of structured data. They can serve as a replacement for database management systems (DBMS), offering easier integration of diverse data and more analytical power. In a nutshell, a semantic repository can dynamically interpret metadata schemata and ontologies, which determine the structure and the semantics of data and of queries against that data. Compared to the approach taken in relational DBMSs, this allows for (i) easier changes to and combinations of data schemata and (ii) automated interpretation of the data. As an example, let us imagine a typical database populated with the information that John is a son of Mary. It will be able to answer just a couple of questions: Which are the son(s) of Mary and Of whom is John the son Given simple family relationships ontology (as the one presented in Figure 7.1), a semantic repository could handle much bigger set of
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Figure 7.1 Hierarchy of family relations.
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The typical modeling paradigm is mathematical logic, but there are also other approaches, rooted in information and library science. KR is a very broad term; here we only refer to one of its main streams. 2 Semantic repository is not a well-established term. See http://www.ontotext.com/ inference/semantic_repository.html for a more elaborate introduction.
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questions. It will be able infer the more general fact that John is a child of Mary and, even more generally, that Mary and John are relatives (which is true in both directions because hasRelative is de ned to be symmetric in the ontology). Further, if it is known that Mary is a woman, a semantic repository will infer that Mary is the mother of John, which is a more speci c inverse relation. Although simple for a human to infer, the above facts would remain unknown to a typical DBMS and indeed to any other information system, for which the model of the world is limited to datastructures of strings and numbers with no automatically interpretable semantics.
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