An Interests Discovery Approach in Social Networks based on a Semantically Enriched Bayesian Network Model

Akram Al-Kouz; Sahin Albayrak

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Paper

An Interests Discovery Approach in Social Networks based on a Semantically Enriched Bayesian Network Model

Topics: Clustering and Classification Methods; Concept Mining; Context Discovery; Data Analytics; Data Reduction and Quality Assessment; Information Extraction; Interactive and Online Data Mining; Machine Learning; Mining Text and Semi-Structured Data; Web Mining

In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 0IC3K, 300-305, 2012 , Barcelona, Spain

Authors: Akram Al-Kouz and Sahin Albayrak

Affiliation: Technical University of Berlin, Germany

Keyword(s): Interests Discovery, Bayesian Networks, Social Networks.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Business Analytics ; Clustering and Classification Methods ; Computational Intelligence ; Concept Mining ; Context Discovery ; Data Analytics ; Data Engineering ; Data Reduction and Quality Assessment ; Evolutionary Computing ; Information Extraction ; Interactive and Online Data Mining ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Mining Text and Semi-Structured Data ; Soft Computing ; Symbolic Systems ; Web Mining

Abstract: Knowing the interests of users in Social Networking Systems becomes essential for User Modeling. Interests discovery from user’s posts based on standard text classification techniques such as the Bag Of Words fails to catch the implicit relations between terms. We propose an approach that automatically generates an ordered list of candidate topics of interests given the text of the users’ posts. The approach generate terms and segments, enriches them semantically from world knowledge, and creates a Bayesian Network to model the syntactic and semantic relations. After that it uses probabilistic inference to elect the list of candidate topics of interests which have the highest posterior probability given the explicit and implicit features in user’s posts as observed evidences. A primitive evaluation has been conducted using manually annotated data set consisting of 40 Twitter users. The results showed that our approach outperforms the Bag Of Words technique, and that it has promising indications for effectively detecting interests of users in Social Networking Systems. (More)

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Paper citation in several formats:

Al-Kouz, A. and Albayrak, S. (2012). An Interests Discovery Approach in Social Networks based on a Semantically Enriched Bayesian Network Model. In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval (IC3K 2012) - KDIR; ISBN 978-989-8565-29-7; ISSN 2184-3228, SciTePress, pages 300-305. DOI: 10.5220/0004172103000305

@conference{kdir12,
author={Akram Al{-}Kouz. and Sahin Albayrak.},
title={An Interests Discovery Approach in Social Networks based on a Semantically Enriched Bayesian Network Model},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval (IC3K 2012) - KDIR},
year={2012},
pages={300-305},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004172103000305},
isbn={978-989-8565-29-7},
issn={2184-3228},
}

TY - CONF

JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval (IC3K 2012) - KDIR
TI - An Interests Discovery Approach in Social Networks based on a Semantically Enriched Bayesian Network Model
SN - 978-989-8565-29-7
IS - 2184-3228
AU - Al-Kouz, A.
AU - Albayrak, S.
PY - 2012
SP - 300
EP - 305
DO - 10.5220/0004172103000305
PB - SciTePress