Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Algorithmic detection of semantic similarity
WWW '05 Proceedings of the 14th international conference on World Wide Web
A personalized search engine based on web-snippet hierarchical clustering
WWW '05 Special interest tracks and posters of the 14th international conference on World Wide Web
POLYPHONET: an advanced social network extraction system from the web
Proceedings of the 15th international conference on World Wide Web
Proceedings of the 15th international conference on World Wide Web
Novel association measures using web search with double checking
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Extracting keyphrases to represent relations in social networks from web
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
A Web-Based Relatedness Measure by Conditional Query
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Discovering relationship types between users using profiles and shared photos in a social network
Multimedia Tools and Applications
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Modeling and naming general entity-entity relationships is challenging in construction of social networks. Given a seed denoting a person name, we utilize Google search engine, NER (Named Entity Recognizer) parser, and CODC (Co-Occurrence Double Check) formula to construct an evolving social network. For each entity pair in the network, we try to label their categories and relationships. Firstly, we utilize an open directory project (ODP) resource, which is the largest human-edited directory of the web, to build a directed graph, and then use three ranking algorithms, PageRank, HITS, and a Markov chain random process to extract potential categories defined in the ODP. These categories capture the major contexts of the designated named entities. Finally, we combine the ranks of these categories and tf*idf scores of noun phrases to extract relationships. In our experiments, total 6 evolving social networks with 618 pairs of named entities demonstrate that the Markov chain random process is better than the other two algorithms.