Expert Recommending System with Extended Object-Based Thesauri(XOT) for Social Network Service
Jong-Gook Bae, Jae-Dong Yang, Ho-Sang Jo · Jeongbo gwahaghoe nonmunji. so'peuteuweeo mich eung'yong · 2012
SNS(Social Network Service) characterized by Facebook and Twitter has become the next generation paradigm of obtaining data, information and knowledge on the web. The aim of this paper is to recommend relevant expert communities to users on the social network by exploiting the extended object-based thesauri(XOT). The thesaurus is an extended object-based one taking the IDs of domain experts as its instances. Based on the thesaurus, the recommendation is made by inferencing relationships between concepts. The inference matches the concepts with terms extracted from messages of the SNS users and is directed by user intent captured during the semantic analysis of the message. Since the concept includes IDs of the expert resident in a social network, the experts could be recommended to the users through the social network. To be shared and to be easily reused on SNS, the thesauri are transformed into XTM(Xml Topic Map) after assigning the proper expert IDs for each concept in the thesaurus. For the assignment, we exploit a conventional ranking algorithm applied to each concept, which analyzes papers, reports and related news of the experts to estimate the grade of their expertise. Our inference engine adopts its inference mechanism from object inference proposed in OSEM, though in a quite different context. K-rounge exploiting the inference engine works on the top of Cassandra which is the open source database system dedicated to SNS. Additionally, ten thousands of thesauri including synonyms are constructed for the inference.