Applications of Ontologies and Problem-Solving Methods

By Gomez-Perez, Asuncion; Benjamins, V. Richard | AI Magazine, Spring 1999 | Go to article overview

Applications of Ontologies and Problem-Solving Methods


Gomez-Perez, Asuncion, Benjamins, V. Richard, AI Magazine


One of the main motivations underlying both ontologies and problem-solving methods (PSMs) is to enable sharing and reuse of knowledge and reasoning behavior across domains and tasks. PSMs and ontologies can be seen as complementary reusable components to construct knowledge systems from reusable components. Ontologies are concerned with static domain knowledge and PSMs with dynamic reasoning knowledge. To build full applications of information and knowledge systems from reusable components, both PSMs and ontologies are required in a tightly integrated way. The integration of ontologies and PSMs is a possible solution to the interaction problem (Chandrasekaran 1998), which states that representing knowledge for the purpose of solving some problem is strongly affected by the nature of the problem and the inference strategy to be applied to the problem. Through ontologies and PSMs, this interaction can be made explicit and taken into consideration.

Ontologies

Ontologies aim at capturing domain knowledge in a generic way. An ontology, therefore, provides a commonly agreed understanding of a domain, which can be reused and shared across applications and groups (Uschold and Gruninger 1996). Ontologies provide a common vocabulary of an area and define--with different levels of formality--the meaning of the terms and the relations between them. Ontologies are usually organized in taxonomies and typically contain modeling primitives such as classes, relations, functions, axioms, and instances (Gruber 1993).

Until now, few domain-independent methodologies have been reported to build ontologies. Uschold's methodology (Uschold and Gruninger 1996), Gruninger and Fox's (1995) methodology, and METHONTOLOGY (Gomez-Perez 1998; Fernandez, Gomez-Perez, and Juristo 1997) are the most representative ones, which have in common that they start from the identification of the purpose of the ontology and the need for domain knowledge acquisition. However, having acquired a significant amount of knowledge, Uschold proposes codification in a formal language, and METHONTOLOGY proposes expressing the ontology at the knowledge level as a set of intermediate representations based on tabular and graph notations.

Several languages can be used to formalize the content of an ontology at the symbol level. Usually, a language is attached to a given ontology server. The most representative languages are ONTOLINGUA (Gruber 1993), CYCL (Lenat and Guha 1990), and LOOM (MacGregor 1991). ONTOLINGUA is the language used by the ONTOLOGY SERVER (Farquhar et al. 1997). CYCL is the language used in the CYC Project, and LOOM is the language used by the server called ONTOSAURUS (Swartout et al. 1997).

Although ontologies can be used (Uschold and Gruninger 1996) to communicate between systems, people, and organizations, inter-operate between systems, and support the design and development of knowledge-based and general software systems, the number of applications built that use ontologies to model the application knowledge is small. That is, many times such ontologies have been built just for a given application without special consideration for sharing and reuse. Several problems make it difficult to reuse existing ontologies in applications: Ontologies are dispersed over several servers; the formalization differs depending on the server on which the ontology is stored; ontologies on the same server are usually described with different levels of detail; and there is no common format for presenting relevant information about the ontologies so that users can decide which ontology best suits their purpose. These problems are probably the cause for the relatively small number of known applications until now in areas such as knowledge management, ontology-based brokers, natural language generation, enterprise modeling, knowledge-based systems, and interoperability between systems.

Problem-Solving Methods

PSMs describe the reasoning process of a knowledge-based system in an implementation- and domain-independent manner. …

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