Learning domain ontology for tag recommendation
Djuana, Endang, Xu, Yue, & Li, Yuefeng (2011) Learning domain ontology for tag recommendation. In Diaz, Fernando, Hovy, Eduard, King, Irwin, Li , Juanzi, Metzler, Donald, Moens, Marie-Francine, et al. (Eds.) Proceeding of the 3rd ACM Workshop on Social Web Search and Mining, ACM (Association for Computing Machinery) Press, Beijing Hotel, Beijing. (In Press)
Recently, user tagging systems have grown in popularity on the web. The tagging process is quite simple for ordinary users, which contributes to its popularity. However, free vocabulary has lack of standardization and semantic ambiguity. It is possible to capture the semantics from user tagging and represent those in a form of ontology, but the application of the learned ontology for recommendation making has not been that flourishing. In this paper we discuss our approach to learn domain ontology from user tagging information and apply the extracted tag ontology in a pilot tag recommendation experiment. The initial result shows that by using the tag ontology to re-rank the recommended tags, the accuracy of the tag recommendation can be improved.
Citation countsare sourced monthly fromand citation databases.
These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.
Citations counts from theindexing service can be viewed at the linked Google Scholar™ search.
Full-text downloadsdisplays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.
|Item Type:||Conference Paper|
|Additional Information:||SWSM 2011 has taken place in Beijing, China on July 28, 2011 during the 34th Annual International ACM SIGIR conference. The aim of the workshop is to provide a forum for researchers and practitioners to discuss ideas related to searching and mining the social Web, with a special focus on the analysis of user generated content during human crises (e.g., earthquakes, terrorist attacks, etc.). The ubiquitous nature of Web-enabled devices, including desktops, laptops, tablets, and mobile phones, enables people to participate and interact with each other in various Web communities. Examples of such communities include forums, newsgroups, blogs, microblogs, bookmarking services, photo sharing platforms, and location-based services. Hence, the rapidly evolving social Web provides a platform for communication, information sharing, and collaboration. A vast amount of heterogeneous data (composed of e.g., text, photos, video, links) has been generated by the users of various social communities, which offers an unprecedented opportunity for studying novel theories and technologies for social Web search and mining. The goal of the workshop is to provide a forum for discussing and exploring social media topics related to Web search and information retrieval, Web mining, social network analysis, semantic Web, natural language processing, and computational advertising. In addition to paper presentations, we will solicit invited talks and a panel that will stress the interdisciplinary challenges of social search and mining.|
|Keywords:||User tagging, ontology learning, tag recommendation|
|Subjects:||Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > LIBRARY AND INFORMATION STUDIES (080700) > Information Retrieval and Web Search (080704)|
|Divisions:||Past > Schools > Computer Science|
Past > QUT Faculties & Divisions > Faculty of Science and Technology
|Copyright Owner:||Copyright 2011 ACM|
|Deposited On:||02 Sep 2011 08:46|
|Last Modified:||02 Sep 2011 12:57|
Repository Staff Only: item control page