Reflections on 25+ years of knowledge acquisition.

B.J. Wielinga

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

In this paper I give a short reflection on Knowledge Acquisition as a subfield of AI and Knowledge Engineering over the last 25 years or so. My major message is that knowledge modeling is an underrated but still important method to reduce the complexity problems that arise in constructing knowledge-based applications. Scale - as apparent in the Semantic Web - is another important parameter in recent developments in Knowledge Acquisition: it requires other techniques than those of the 1980s. Natural Language Processing is the most promising way forward, but also the most difficult source of the acquisition of formalized knowledge. I will argue that some of the lessons learned in building knowledge-based systems may carry over to reasoning in the Semantic Web and to knowledge acquisition from natural language web sources. © 2012 Elsevier Ltd.
Original languageEnglish
Pages (from-to)211-215
JournalInternational Journal of Human-computer Studies
Volume71
Issue number2
Early online date22 Oct 2012
DOIs
Publication statusPublished - 2013

Fingerprint

Dive into the research topics of 'Reflections on 25+ years of knowledge acquisition.'. Together they form a unique fingerprint.

Cite this