Knowledge Engineering: Practice and Patterns: 17th International Conference, Ekaw 2010, Lisbon, Portugal, October 11-15, 2010, Proceedings

Cimiano, Philipp (Editor)

ISBN 10: 3642164374 / ISBN 13: 9783642164378

Verlag: Springer-Verlag New York Inc, 2010

Beschreibung

1st edition. 613 pages. 9.10x6.20x1.30 inches. In Stock.

Bestandsnummer des Verkäufers x-3642164374

Bibliografische Details

• Titel: Knowledge Engineering: Practice and Patterns: 17th International Conference, Ekaw 2010, Lisbon, Portugal, October 11-15, 2010, Proceedings
• Verlag: Springer-Verlag New York Inc
• Erscheinungsjahr: 2010
• Sprache: Englisch
• ISBN-10: 3642164374
• ISBN-13: 9783642164378
• Einband: Paperback
• Zustand: Brand New
• Gewicht: 0,93 Kilogramm

Über diesen Titel

Inhaltsangabe

Knowledge Management and Knowledge Engineering is a fascinating ?eld of re- 1 search these days. In the beginning of EKAW , the modeling and acquisition of knowledge was the privilege of – or rather a burden for – a few knowledge engineers familiar with knowledge engineering paradigms and knowledge rep- sentationformalisms.While the aimhasalwaysbeentomodelknowledgedecl- atively and allow for reusability, the knowledge models produced in these early days were typically used in single and very speci?c applications and rarely - changed. Moreover, these models were typically rather complex, and they could be understood only by a few expert knowledge engineers. This situation has changed radically in the last few years as clearly indicated by the following trends: – The creation of (even formal) knowledge is now becoming more and more collaborative. Collaborative ontology engineering tools and social software platforms show the potential to leverage the wisdom of the crowds (or at least of “the many”) to lead to broader consensus and thus produce shared models which qualify better for reuse. – A trend can also be observed towards developing and publishing small but 2 3 4 high-impactvocabularies(e.g.,FOAF ,DublinCore ,GoodRelations)rather than complex and large knowledge models.