@inproceedings{mika2005ontologies,
  abstract = {In our work we extend the traditional bipartite model of ontologies with the social dimension, leading to a tripartite model of actors, concepts and instances. We demonstrate the application of this representation by showing how community-based semantics emerges from this model through a process of graph transformation. We illustrate ontology emergence by two case studies, an analysis of a large scale folksonomy system and a novel method for the extraction of community-based ontologies from Web pages.},
  author = {Mika, Peter},
  booktitle = {The Semantic Web - ISWC 2005, Proceedings of the 4th International Semantic Web Conference, ISWC 2005, Galway, Ireland, November 6-10},
  editor = {Gil, Yolanda and Motta, Enrico and Benjamins, V. Richard and Musen, Mark A.},
  file = {mika2005ontologies.pdf:mika2005ontologies.pdf:PDF},
  groups = {public},
  interhash = {5ea12110b5bb0e3a8ad09aeb16a70cdb},
  intrahash = {426c2fd559bb4e41c4f67d4eed0a39c7},
  lastdatemodified = {2006-09-26},
  lastname = {Mika},
  longnotes = {[[http://citeseer.ist.psu.edu/739485.html citeseer]]},
  own = {notown},
  pages = {522-536},
  pdf = {mika05-ontologies.pdf},
  publisher = {Springer},
  read = {notread},
  series = {Lecture Notes in Computer Science},
  timestamp = {2007-09-11 13:31:32},
  title = {Ontologies Are Us: A Unified Model of Social Networks and Semantics.},
  url = {http://dx.doi.org/10.1007/11574620_38},
  username = {dbenz},
  volume = 3729,
  year = 2005
}

@inproceedings{mori2006extracting,
  abstract = {Social networks have recently garnered considerable interest. With the intention of utilizing social networks for the Semantic Web, several studies have examined automatic extraction of social networks. However, most methods have addressed extraction of the strength of relations. Our goal is extracting the underlying relations between entities that are embedded in social networks. To this end, we propose a method that automatically extracts labels that describe relations among entities. Fundamentally, the method clusters similar entity pairs according to their collective contexts in Web documents. The descriptive labels for relations are obtained from results of clustering. The proposed method is entirely unsupervised and is easily incorporated into existing social network extraction methods. Our method also contributes to ontology population by elucidating relations between instances in social networks. Our experiments conducted on entities in political social networks achieved clustering with high precision and recall. We extracted appropriate relation labels to represent the entities.},
  author = {Mori, Junichiro and Tsujishita, Takumi and Matsuo, Yutaka and Ishizuka, Mitsuru},
  bibsource = {DBLP, http://dblp.uni-trier.de},
  booktitle = {International Semantic Web Conference},
  crossref = {DBLP:conf/semweb/2006},
  ee = {http://dx.doi.org/10.1007/11926078_35},
  file = {mori2006extracting.pdf:mori2006extracting.pdf:PDF},
  groups = {public},
  interhash = {457973d894180bd95e99bb6f7bb5cbc5},
  intrahash = {f1a145a60c3e4d39e91b39a7c1178110},
  pages = {487-500},
  timestamp = {2009-06-01 15:32:20},
  title = {Extracting Relations in Social Networks from the Web Using Similarity Between Collective Contexts},
  username = {dbenz},
  year = 2006
}