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Social networking applications for relation extraction research

Author: JiangChaoNan
Tutor: DingZuoChun
School: Nanjing University of Technology and Engineering
Course: Information Science
Keywords: Named Entity Recognition Role Labeling Relation extraction Ontology of social relations SWRL rules Jess Implicit Relational Mining
CLC: G350
Type: Master's thesis
Year: 2010
Downloads: 245
Quote: 2
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Abstract


Since search engines appeared so far , blowing a lot of information , but most of them are duplicate information . Search engine returns too many results but still difficult to find useful information. If there is a way to effectively filter search results can only extract the key information that people need , and in the network diagram form , rather than merely presented in the form of text , then the efficiency of access to information and will certainly greatly increased. Based on this, the field of social networks for relationships between named entities extraction conducted in-depth research , try to build a social network for the field of social relations body contains two or more named entities extracted sentences corresponding words as a description of the relationship between the entities . It also defines a set of SWRL rules and Jess inference engine combines the implicit ontology of social relations were tapped. In the named entity recognition task , the paper focuses names and organization names are identified , drawing on the idea of ??semantic role labeling , using the Viterbi algorithm, automatic annotation of sentence fragments in each sub-word names or organization names represent different roles , while according to the names and organization names into words characteristics , summed qualifying word formation rules for pattern matching in order to obtain the final recognition results. This corpus has been open to the real test , the experimental results show that this method is higher than recall accuracy rate close to 70%. This result proves the effectiveness of the method . In relation extraction task, this comprehensive ontology engineering into the seven-step method and the iterative method , to build a social network for field applied to the Internet on corporate social relationships within the industry body . SWRL also designed a series of rules to be introduced in conjunction with the social body Jess rule inference engine , try the concept of strict logical relationship ontology reasoning to dig out the implicit social relationships between entities . Finally get ( Entity Relationship Entity ) triples the relationship between the library and deposited greatly refined the information content , improve the efficiency of access to information .

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