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Title:
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APPROXIMATING RELATION EXTRACTION FOR COMMUNITY MINING IN HETEROGENEOUS SOCIAL NETWORKS |
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Author(s):
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Nima Haghpanah , Masoud Akhoondi , Hassan Abolhassani |
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ISBN:
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978-972-8924-44-7 |
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Editors:
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Pedro Isaías , Miguel Baptista Nunes and João Barroso (associate editors Luís Rodrigues and Patrícia Barbosa) |
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Year:
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2007 |
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Edition:
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V I, 2 |
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Keywords:
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Relation Extraction, Community Mining, Genetic algorithm, Approximation, Graph Mining |
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Type:
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Full Paper |
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First Page:
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171 |
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Last Page:
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178 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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Community mining in heterogeneous social networks is different from traditional community mining approaches in that it
assumes the existence of more than one kind of relations between objects. It consists of two steps. Relation extraction in
which the goal is to combine existing relations to form a relation based on the query provided by the user; and
community mining which identifies community structure in the extracted relation. In this paper we are mainly concerned
with the first step. Previously proposed algorithms for relation extraction are either not applicable to large data sets or can
handle simple queries only. In this paper, we propose two genetic algorithms which use different kind of operators to
search the problem space. These algorithms work in general situations, and are more scalable, interpretable, and
extensible than previous methods. Experiments and analysis of the results on DBLP dataset show the power of our
algorithms in handling complex queries in large data sets. |
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