Hubs, authorities, and communities
ACM Computing Surveys (CSUR)
SimRank: a measure of structural-context similarity
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
TextTiling: segmenting text into multi-paragraph subtopic passages
Computational Linguistics
Disambiguating authors in academic publications using random forests
Proceedings of the 9th ACM/IEEE-CS joint conference on Digital libraries
CollabSeer: a search engine for collaboration discovery
Proceedings of the 11th annual international ACM/IEEE joint conference on Digital libraries
Ranking authors in digital libraries
Proceedings of the 11th annual international ACM/IEEE joint conference on Digital libraries
Capturing missing edges in social networks using vertex similarity
Proceedings of the sixth international conference on Knowledge capture
Similar researcher search in academic environments
Proceedings of the 12th ACM/IEEE-CS joint conference on Digital Libraries
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Semantic search techniques have increasingly gained attention in information retrieval literature. Authors are great sources of semantic interpretation for documents, especially in scholarly domains where articles mostly reflect the research interests of the authors. Being able to interpret semantic meanings of documents from their authors would give rise to many interesting applications, especially in academic digital library literature. In this paper, we present taxonomy-based query-dependent schemes for computing author profile similarity. Our schemes have the capability to capture partial similarities, as opposed to traditional topic overlap based approaches. We generalize our schemes so that they can be easily adopted to other application domains. We acquire resources from multiple places such as Wikipedia, CiteseerX, ArnetMiner, and WikipediaMiner as part of our work. We provide encouraging anecdotal results along with suggestions on potential applications of the proposed schemes.