AIRS 2015: Information Retrieval Technology pp 387-393 | Cite as
A Query Expansion Approach Using Entity Distribution Based on Markov Random Fields
Abstract
The development of knowledge graph construction has prompted more and more commercial engines to improve the retrieval performance by using knowledge graphs as the basic semantic web. Knowledge graph is often used for knowledge inference and entity search, however, the potential ability of its entities and properties for better improving search performance in query expansion remains to be further excavated. In this paper, we propose a novel query expansion technique with knowledge graph (KG) based on the Markov random fields (MRF) model to enhance retrieval performance. This technique, called MRF-KG, models the joint distribution of original query terms, documents and two expanded variants, i.e. entities and properties. We conduct experiments on two TREC collections, WT10G and ClueWeb12B, annotated with Freebase entities. Experiment results demonstrate that MRF-KG outperforms traditional graph-based models.
Keywords
Knowledge graph Entity MRF Query expansionNotes
Acknowledgments
This work is supported in part by Chinese National Program on Key Basic Research Project (973 Program, grant No.2013CB329304, 2014CB744604), and the Natural Science Foundation of China (grant No. 61272265, 61402324).
References
- 1.Dalton, J., Dietz, L., Allan, J.: Entity query feature expansion using knowledge base links, pp. 365–374 (2014)Google Scholar
- 2.Diaz, F., Metzler, D.: Improving the estimation of relevance models using large external corpora. In: SIGIR, pp. 154–161 (2006)Google Scholar
- 3.Evgeniy, G., Michael, R., Amarnag, S.: Facc1: freebase annotation of clueweb corpora (2013)Google Scholar
- 4.Kotov, A., Zhai, C.: Tapping into knowledge base for concept feedback: leveragingconceptnet to improve search results for difficult queries. In: WSDM, pp. 403–412. ACM (2012)Google Scholar
- 5.Lv, Y., Zhai, C.X.: Positional relevance model for pseudo-relevance feedback. In: SIGIR, pp. 579–586. ACM (2010)Google Scholar
- 6.Metzler, D., Croft, W.B.: A markov random field model for term dependencies. In: SIGIR, pp. 472–479. ACM (2005)Google Scholar
- 7.Metzler, D., Croft, W.B.: Latent concept expansion using markov random fields. In: SIGIR, pp. 311–318. ACM (2007)Google Scholar
- 8.Pan, D., et al.: Using dempster-shafer’s evidence theory for query expansion based on freebase knowledge. In: Banchs, R.E., Silvestri, F., Liu, T.-Y., Zhang, M., Gao, S., Lang, J. (eds.) AIRS 2013. LNCS, vol. 8281, pp. 121–132. Springer, Heidelberg (2013)CrossRefGoogle Scholar