%0 Journal Article %1 citeulike:7864195 %A Tang, Jie %A Zhang, Jing %A Jin, Ruoming %A Yang, Zi %A Cai, Keke %A Zhang, Li %A Su, Zhong %C Hingham, MA, USA %D 2011 %I Kluwer Academic Publishers %J Mach. Learn. %K expert-finding social-network %N 2 %P 211--237 %R 10.1007/s10994-010-5212-9 %T Topic Level Expertise Search over Heterogeneous Networks %U http://dx.doi.org/10.1007/s10994-010-5212-9 %V 82 %X In this paper, we present a topic level expertise search framework for heterogeneous networks. Different from the traditional Web search engines that perform retrieval and ranking at document level (or at object level), we investigate the problem of expertise search at topic level over heterogeneous networks. In particular, we study this problem in an academic search and mining system, which extracts and integrates the academic data from the distributed Web. We present a unified topic model to simultaneously model topical aspects of different objects in the academic network. Based on the learned topic models, we investigate the expertise search problem from three dimensions: ranking, citation tracing analysis, and topical graph search. Specifically, we propose a topic level random walk method for ranking the different objects. In citation tracing analysis, we aim to uncover how a piece of work influences its follow-up work. Finally, we have developed a topical graph search function, based on the topic modeling and citation tracing analysis. Experimental results show that various expertise search and mining tasks can indeed benefit from the proposed topic level analysis approach.

@article{citeulike:7864195, abstract = {{In this paper, we present a topic level expertise search framework for heterogeneous networks. Different from the traditional Web search engines that perform retrieval and ranking at document level (or at object level), we investigate the problem of expertise search at topic level over heterogeneous networks. In particular, we study this problem in an academic search and mining system, which extracts and integrates the academic data from the distributed Web. We present a unified topic model to simultaneously model topical aspects of different objects in the academic network. Based on the learned topic models, we investigate the expertise search problem from three dimensions: ranking, citation tracing analysis, and topical graph search. Specifically, we propose a topic level random walk method for ranking the different objects. In citation tracing analysis, we aim to uncover how a piece of work influences its follow-up work. Finally, we have developed a topical graph search function, based on the topic modeling and citation tracing analysis. Experimental results show that various expertise search and mining tasks can indeed benefit from the proposed topic level analysis approach.}}, added-at = {2017-11-15T17:02:25.000+0100}, address = {Hingham, MA, USA}, author = {Tang, Jie and Zhang, Jing and Jin, Ruoming and Yang, Zi and Cai, Keke and Zhang, Li and Su, Zhong}, biburl = {https://www.bibsonomy.org/bibtex/233c280ae4bfd8f8fc595de473db34b54/brusilovsky}, citeulike-article-id = {7864195}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1938277}, citeulike-linkout-1 = {http://dx.doi.org/10.1007/s10994-010-5212-9}, citeulike-linkout-2 = {http://www.springerlink.com/content/rq1605260802844u}, day = 17, doi = {10.1007/s10994-010-5212-9}, interhash = {184d63d699cb5ccc7bf3d1c34dd9dc19}, intrahash = {33c280ae4bfd8f8fc595de473db34b54}, issn = {0885-6125}, journal = {Mach. Learn.}, keywords = {expert-finding social-network}, month = feb, number = 2, pages = {211--237}, posted-at = {2015-11-16 04:39:00}, priority = {2}, publisher = {Kluwer Academic Publishers}, timestamp = {2023-06-05T01:15:36.000+0200}, title = {{Topic Level Expertise Search over Heterogeneous Networks}}, url = {http://dx.doi.org/10.1007/s10994-010-5212-9}, volume = 82, year = 2011 }