The Power of Ontologies for Knowledge Management

Ontologies, as a tool for knowledge management, contribute to organize and make explicit the knowledge modeller intentions. Let’s take a look.

Tacit knowledge, as defined by Nonaka and Takeuchi, is the type of knowledge residing in people’s minds, while explicit knowledge is the type of knowledge not residing in people’s minds but in structured documents and central repositories of knowledge, for example [Nonaka, 2008] . A knowledge management project is an effort developed by companies in order to collect the tacit knowledge, which is distributed among the employees, and turn it into a company asset as explicit knowledge [Davemport, 1998].

Ontologies have their origin in the Philosophy field and refer to a system of categories that commit to a certain vision of the world [Guarino, 1998]. They are applied in computer science in order to capture domain knowledge and to explicit the assumptions that define the knowledge’s intended meaning [Guarino, 1998]. The picture below shows a simple example where two people use the same vocabulary to communicate. They use the same vocabulary representation (the word “Orange”), but they refer to different meanings: one talks about the color, while the other talks about the fruit. This is a common communication problem when exchanging information using documents or when integrating software systems. Applying ontologies to capture domain knowledge allows the knowledge engineer, responsible for a knowledge management project, to identify the company’s knowledge, structure it, and store it independently from the people who use that knowledge on their daily activities.

Ambiguity - Knowledge Management

The problem of ambiguity: two people talking to each other and using the same vocabulary, while the intended meanings are different.

Ontologies are currently being applied to the petroleum exploration chain to make it possible to capture the semantics behind the representations defined for geological reservoir models. They also provide support for data interoperability among the software systems applied to the petroleum exploration chain. Endeeper develops a family of ontology-based software systems. Petrographic software systems like Petroledge, Hardledge, and RockViewer are based on the Petrographic Ontology, which was created by [Abel, 2001]. Strataledge is based on the Stratigraphic Ontology, which was created by [Lorenzatti, 2010]. Both ontologies are maintained by Endeeper and are in constant evolution in order to better capture the geological knowledge applied by companies to describe rocks and evaluate the quality of petroleum reservoirs.

References

  • Abel, M. The study of expertise in Sedimentary Petrography and its significance for knowledge engineering (in Portuguese). (Doctoral Thesis ). Informatics Institute Federal University of Rio Grande do Sul, Porto Alegre, 2001. 239 p.
  • Davenport, Thomas H., David W. De Long, and Michael C. Beers. “Successful knowledge management projects.” MIT Sloan Management Review 39.2 (1998): 43.
  • Guarino, N. Formal Ontology in Information Systems In: N. GUARINO (Ed.). Formal Ontology in Information Systems, FOIS’98. Trento, Italy: IO Press, 1998. Formal Ontology in Information Systems p.6-8 June 1998.
  • Lorenzatti, A. Ontology for Imagistic Domains: combining textual and pictorial primitives (in Portuguese). (Master Dissertation). Informatics Institute Federal University of Rio Grande do Sul, Porto Alegre, 2010.
  • Nonaka, Ikujiro. The knowledge-creating company. Harvard Business Review Press, 2008.

Alexandre Lorenzatti

Knowledge and software engineer at Endeeper
Alexandre has a Computer Science background focused in Knowledge Modelling and Software Engineering. Currently, he works with Computer Science applied to Geology, creating software solutions to the petroleum exploration chain.

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