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Automated Learning of Social Ontologies

Automated Learning of Social Ontologies

Konstantinos Kotis, Andreas Papasalouros
Copyright: © 2011 |Pages: 20
ISBN13: 9781609606251|ISBN10: 1609606256|EISBN13: 9781609606268
DOI: 10.4018/978-1-60960-625-1.ch012
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MLA

Kotis, Konstantinos, and Andreas Papasalouros. "Automated Learning of Social Ontologies." Ontology Learning and Knowledge Discovery Using the Web: Challenges and Recent Advances, edited by Wilson Wong, et al., IGI Global, 2011, pp. 227-246. https://doi.org/10.4018/978-1-60960-625-1.ch012

APA

Kotis, K. & Papasalouros, A. (2011). Automated Learning of Social Ontologies. In W. Wong, W. Liu, & M. Bennamoun (Eds.), Ontology Learning and Knowledge Discovery Using the Web: Challenges and Recent Advances (pp. 227-246). IGI Global. https://doi.org/10.4018/978-1-60960-625-1.ch012

Chicago

Kotis, Konstantinos, and Andreas Papasalouros. "Automated Learning of Social Ontologies." In Ontology Learning and Knowledge Discovery Using the Web: Challenges and Recent Advances, edited by Wilson Wong, Wei Liu, and Mohammed Bennamoun, 227-246. Hershey, PA: IGI Global, 2011. https://doi.org/10.4018/978-1-60960-625-1.ch012

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Abstract

Learned social ontologies can be viewed as products of a social fermentation process, i.e. a process between users who belong in communities of common interests (CoI), in open, collaborative, and communicative environments. In such a setting, social fermentation ensures the automatic encapsulation of agreement and trust of shared knowledge that participating stakeholders provide during an ontology learning task. This chapter discusses the requirements for the automated learning of social ontologies and presents a working method and results of preliminary work. Furthermore, due to its importance for the exploitation of the learned ontologies, it introduces a model for representing the interlinking of agreement, trust and the learned domain conceptualizations that are extracted from social content. The motivation behind this work is an effort towards supporting the design of methods for learning ontologies from social content i.e. methods that aim to learn not only domain conceptualizations but also the degree that agents (software and human) may trust these conceptualizations or not.

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