Expertise identification using email communications
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Implicit user modeling for personalized search
Proceedings of the 14th ACM international conference on Information and knowledge management
The "Spree" Expert Finding System
ICSC '07 Proceedings of the International Conference on Semantic Computing
COMPSAC '08 Proceedings of the 2008 32nd Annual IEEE International Computer Software and Applications Conference
Visual analysis of documents with semantic graphs
Proceedings of the ACM SIGKDD Workshop on Visual Analytics and Knowledge Discovery: Integrating Automated Analysis with Interactive Exploration
ExpertRank: An Expert User Ranking Algorithm in Online Communities
NISS '09 Proceedings of the 2009 International Conference on New Trends in Information and Service Science
You are who you know: inferring user profiles in online social networks
Proceedings of the third ACM international conference on Web search and data mining
Emerging topic detection on Twitter based on temporal and social terms evaluation
Proceedings of the Tenth International Workshop on Multimedia Data Mining
Social similarity as a driver for selfish, cooperative and altruistic behavior
WOWMOM '10 Proceedings of the 2010 IEEE International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM)
Exploring entity relations for named entity disambiguation
HLT-SS '11 Proceedings of the ACL 2011 Student Session
Dynamic Item Recommendation by Topic Modeling for Social Networks
ITNG '11 Proceedings of the 2011 Eighth International Conference on Information Technology: New Generations
A wikipedia based semantic graph model for topic tracking in blogosphere
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
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Studying the text messages of a user such as his posts in Facebook or his tweets in Twitter can help in detecting his topics of interests. User in Social Network Systems (SNS) posts text messages about a wide diverse of topics. Posts usually written in a non-standard language, which make it not applicable to the standard Natural Language Processing (NLP) techniques used to catch the relations between words in text. In many cases there are semantic relations between the contained entities of posts that can infer the interest of the user. Bag-Of-Words (BOW) based text classification techniques classify this kind of messages to a wide diverse of topics, but they fail in catching the implicit semantic relation between the contained entities. In this paper we propose a technique to discover the implicit semantic relations between entities in text messages, which can infer the interests of a user. The proposed technique based on a semantically enriched graph representation of entities contained in text messages generated by a user, a new algorithm (Root-Path-Degree) is invented and used to find the most representative sub-graph that reflects the semantic implicit interests of the user. An evaluation was done using manually annotated posts of 687 Facebook users. Precision and Recall results showed our technique performs better than the standard BOW technique.