Towards Multi-target Search of Semantic Association
Semantic association represents group relationship among objects in linked data. Searching semantic associations is complicated, which involves the search of multiple objects and the search of their group relationships simultaneously. In this paper, we propose this kind of search as a multi-target search, and we compare it to traditional search tasks, which we classify as single-target search. A novel search model is introduced, and the notion of virtual document is used to extract linguistic information of semantic associations. Multi-target search is finally fulfilled by a PageRank-like ranking scheme and a top-K selection policy considering object affinity. Experiments show that our approach is effective in improving retrieval precision on semantic associations.
KeywordsLinked data Semantic association Multi-target search
The work was supported by the National High-Tech Research and Development (863) Program of China (No. 2015AA015406), the Open Project of Jiangsu Key Laboratory of Data Engineering and Knowledge Service (No. DEKS2014KT002), and National Natural Science Foundation of China (No. 61472077). We would like to thank Xing Li for his efforts in implementation and evaluations.
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