Abstract
Place name disambiguation is the task of correctly identifying a place from a set of places sharing a common name. It contributes to tasks such as knowledge extraction, query answering, geographic information retrieval, and automatic tagging. Disambiguation quality relies on the ability to correctly identify and interpret contextual clues, complicating the task for short texts. Here we propose a novel approach to the disambiguation of place names from short texts that integrates two models: entity co-occurrence and topic modeling. The first model uses Linked Data to identify related entities to improve disambiguation quality. The second model uses topic modeling to differentiate places based on the terms used to describe them. We evaluate our approach using a corpus of short texts, determine the suitable weight between models, and demonstrate that a combined model outperforms benchmark systems such as DBpedia Spotlight and Open Calais in terms of F1-score and Mean Reciprocal Rank.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
- 2.
- 3.
- 4.
For example via dbr:FreedomWorks dbp:headquarters dbr:Washington,_D.C.
- 5.
- 6.
- 7.
- 8.
- 9.
References
Adams, B., Janowicz, K.: On the geo-indicativeness of non-georeferenced text. In: International AAAI Conference on Web and Social Media (ICWSM), pp. 375–378 (2012)
Adams, B., McKenzie, G., Gahegan, M.: Frankenplace: interactive thematic mapping for ad hoc exploratory search. In: Proceedings of the 24th International Conference on World Wide Web, pp. 12–22. ACM (2015)
Bilenko, M., Mooney, R., Cohen, W., Ravikumar, P., Fienberg, S.: Adaptive name matching in information integration. IEEE Intell. Syst. 18(5), 16–23 (2003)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3(Jan), 993–1022 (2003)
Bunescu, R.C., Pasca, M.: Using encyclopedic knowledge for named entity disambiguation. EACL 6, 9–16 (2006)
Cucerzan, S.: Large-scale named entity disambiguation based on Wikipedia data. EMNLP-CoNLL 7, 708–716 (2007)
Fader, A., Soderland, S., Etzioni, O., Center, T.: Scaling Wikipedia-based named entity disambiguation to arbitrary web text. In: Proceedings of the IJCAI Workshop on User-contributed Knowledge, Artificial Intelligence: An Evolving Synergy, Pasadena, CA, USA, pp. 21–26, 2009 (2011)
Goodchild, M.F., Glennon, J.A.: Crowdsourcing geographic information for disaster response: a research frontier. Int. J. Digit. Earth 3(3), 231–241 (2010)
Gray, R.W.: Exact transformation equations for Fuller’s world map. Cartogr.: Int. J. Geogr. Inf. Geovis. 32(3), 17–25 (1995)
Han, X., Zhao, J., Structural semantic relatedness: a knowledge-based method to named entity disambiguation. In: Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pp. 50–59. Association for Computational Linguistics (2010)
Hu, Y., Janowicz, K., Prasad, S.: Improving Wikipedia-based place name disambiguation in short texts using structured data from DBpedia. In: Proceedings of the 8th Workshop on Geographic Information Retrieval, p. 8. ACM (2014)
Janowicz, K., Hitzler, P.: The digital earth as knowledge engine. Semant. Web 3(3), 213–221 (2012)
Jones, C.B., Purves, R.S.: Geographical information retrieval. Int. J. Geogr. Inf. Sci. 22(3), 219–228 (2008)
Machado, I.M.R., de Alencar, R.O., de Oliveira Campos Jr., R., Davis Jr., C.A.: An ontological gazetteer and its application for place name disambiguation in text. J. Braz. Comput. Soc. 17(4), 267–279 (2011)
Mendes, P.N., Jakob, M., GarcÃa-Silva, A., Bizer, C., Dbpedia spotlight: shedding light on the web of documents. In: Proceedings of the 7th International Conference on Semantic Systems, pp. 1–8. ACM (2011)
Mihalcea, R., Csomai, A.: Wikify!: linking documents to encyclopedic knowledge. In Proceedings of the sixteenth ACM conference on Conference on information and knowledge management, pp. 233–242. ACM (2007)
Milne, D., Witten, I.H.: Learning to link with Wikipedia. In: Proceedings of the 17th ACM conference on Information and knowledge management, pp. 509–518. ACM (2008)
Overell, S., Rüger, S.: Using co-occurrence models for placename disambiguation. Int. J. Geogr. Inf. Sci. 22(3), 265–287 (2008)
Purves, R., Jones, C.: Geographic information retrieval. SIGSPATIAL Spec. 3(2), 2–4 (2011)
Rizzo, G., van Erp, M., Troncy, R.: Benchmarking the extraction and disambiguation of named entities on the semantic web. In: LREC, pp. 4593–4600 (2014)
Sahr, K., White, D., Kimerling, A.J.: Geodesic discrete global grid systems. Cartogr. Geogr. Inf. Sci. 30(2), 121–134 (2003)
Spitz, A., Geiß, J., Gertz, M., So far away, yet so close: augmenting toponym disambiguation and similarity with text-based networks. In: Proceedings of the Third International ACM SIGMOD Workshop on Managing and Mining Enriched Geo-Spatial Data, GeoRich 2016, pp. 2: 1–2: 6. ACM, New York, NY, USA (2016)
Steyvers, M., Griffiths, T.: Probabilistic topic models. Handb. Latent Semant. Anal. 427(7), 424–440 (2007)
Zhang, W., Gelernter, J.: Geocoding location expressions in Twitter messages: a preference learning method. J. Spat. Inf. Sci. 2014(9), 37–70 (2014)
Acknowledgement
The authors would like to acknowledge partial support by the National Science Foundation (NSF) under award 1440202 EarthCube Building Blocks: Collaborative Proposal: GeoLink Leveraging Semantics and Linked Data for Data Sharing and Discovery in the Geosciences.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Ju, Y., Adams, B., Janowicz, K., Hu, Y., Yan, B., McKenzie, G. (2016). Things and Strings: Improving Place Name Disambiguation from Short Texts by Combining Entity Co-Occurrence with Topic Modeling. In: Blomqvist, E., Ciancarini, P., Poggi, F., Vitali, F. (eds) Knowledge Engineering and Knowledge Management. EKAW 2016. Lecture Notes in Computer Science(), vol 10024. Springer, Cham. https://doi.org/10.1007/978-3-319-49004-5_23
Download citation
DOI: https://doi.org/10.1007/978-3-319-49004-5_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-49003-8
Online ISBN: 978-3-319-49004-5
eBook Packages: Computer ScienceComputer Science (R0)