Ontological engineering: Principles, methods, tools and languages

Abstract

In 1991, the DARPA Knowledge Sharing Effort ([88], p. 37) envisioned a new way to build intelligent systems. It proposed the following: Building knowledge-based systems today usually entails constructing new knowledge bases from scratch. It could be instead done by assembling reusable components. System developers would then only need to worry about creating the specialized knowledge and reasoners new to the specific task of their system. This new system would interoperate with existing systems, using them to perform some of its reasoning. In this way, declarative knowledge, problem-solving techniques and reasoning services would all be shared among systems. This approach would facilitate building bigger and better systems and cheaply. Static knowledge is modeled by means of ontologies while problem solving methods specify generic reasoning mechanisms. Both types of components can be viewed as complementary entities that can be used to configure new knowledge-based systems from existing reusable components. Since DARPA's idea, considerable progress has been made in developing the conceptual bases to build technology that allows reusing and sharing knowledge components. Ontologies and problem solving methods (PSMs) have been created to share and reuse knowledge and reasoning behavior across domains and tasks. In this evolution, the most important fact has been the emergence of the Semantic Web. According to [10], the Semantic Web is an extension of the current Web in which information is given well-defined meaning, better enabling computers and people to work in cooperation. This cooperation can be achieved by using shared knowledge components, and so ontologies and PSMs have become key instruments in developing the Semantic Web. Currently, ontologies are widely used in knowledge engineering, artificial intelligence and computer science, in applications related to knowledge management, natural language processing, e-commerce, intelligent integration information, information retrieval, database design and integration, bio-informatics, education, etc. In this chapter, we present the basics about ontologies, and show what activities should be carried out during the ontology development process, what principles should be followed in ontology design, and what methods, methodologies, software tools and languages are available to give support to each one of these activities. First, in Sect. 1.2, we define the word 'ontology' and we briefly explaining its roots in philosophy. Section 1.3 is devoted to explain which are the main components that can be used to model ontologies. In Sect. 1.4, we present the main ontology design principles. In Sect. 1.5, we describe the ontology development process in the context of the Semantic Web, where ontologies can be highly distributed and present many links among each other (hence the notion of networked ontologies). In Sect. 1.6, we describe the development of ontologies and the life cycle. In Sect. 1.7, we describe the methods, methodologies and tools commonly used for the whole ontology development process or only for specific activities. Among them we pay attention to those aimed at ontology learning, which reduce the effort needed during the knowledge acquisition process; at ontology merging, which generates a unique target ontology from several source ontologies; at ontology alignment, which establishes different types of mappings between ontologies (hence preserving the original ones); and at ontology evaluation, which evaluates ontology content. In the implementation activity description, we present ontology languages that can be used to implement ontologies. Finally, conclusions and future lines of research are presented in Sect. 1.8. © 2006 Springer-Verlag Berlin Heidelberg.