POLYPHONET: an advanced social network extraction system from the web
By Yutaka Matsuo, Junichiro Mori, Masahiro Hamasaki, Takuichi Nishimura, Hideaki Takeda, Kôiti Hasida, Mitsuru Ishizuka. The Web Conference, 2006
345 citations recorded by the source corpus.
Social networks play important roles in the Semantic Web: knowledge management, information retrieval, ubiquitous computing, and so on. We propose a social network extraction system called POLYPHONET, which employs several advanced techniques to extract relations of persons, detect groups of persons, and obtain keywords for a person. Search engines, especially Google, are used to measure co-occurrence of information and obtain Web documents.Several studies have used search engines to extract social networks from the Web, but our research advances the following points: First, we reduce the related methods into simple pseudocodes using Google so that we can build up integrated systems. Second, we develop several new algorithms for social networking mining such as those to classify relations into categories, to make extraction scalable, and to obtain and utilize person-to-word relations. Third, every module is implemented in POLYPHONET, which has been used at four academic conferences, each with more than 500 participants. We overview that system. Finally, a novel architecture called Super Social Network Mining is proposed; it utilizes simple modules using Google and is characterized by scalability and Relate-Identify processes: Identification of each entity and extraction of relations are repeated to obtain a more precise social network.
Subfields: Information Systems, Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/e47635e3dc21cf1b4633afd337acb38acadac53b
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Information Systems
In Statistical and Nonlinear Physics
Citations in the corpus
- Measuring semantic similarity between words using web search engines (2007, methodology): “Semantic similarity measures have been used in Semantic Web related applications such as automatic annotation of Web pages [7], community mining [23, 19], and keyword extraction for inter-entity relation representation [26].”