Skip to main content
SpringerLink
Log in

Measuring Semantic Relatedness with Knowledge Association Network

  • Conference paper
  • First Online:
Database Systems for Advanced Applications (DASFAA 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11446))

Included in the following conference series:

Abstract

Measuring semantic relatedness between two words is a fundamental task for many applications in both databases and natural language processing domains. Conventional methods mainly utilize the latent semantic information hidden in lexical databases (WordNet) or text corpus (Wikipedia). They have made great achievements based on the distance computation in lexical tree or co-occurrence principle in Wikipedia. However these methods suffer from low coverage and low precision because (1) lexical database contains abundant lexical information but lacks semantic information; (2) in Wikipedia, two related words (e.g. synonyms) may not appear in a window size or a sentence, and unrelated ones may be mentioned together by chance. To compute semantic relatedness more accurately, some other approaches have made great efforts based on free association network and achieved a significant improvement on relatedness measurement. Nevertheless, they need complex preprocessing in Wikipedia. Besides, the fixed score functions they adopt cause the lack of flexibility and expressiveness of model. In this paper, we leverage DBPedia and Wikipedia to construct a Knowledge Association Network (KAN) which avoids the information extraction of Wikipedia. We propose a flexible and expressive model to represent entities behind the words, in which attribute and topological structure information of entities are embedded in vector space simultaneously. The experiment results based on standard datasets show the better effectiveness of our model compared to previous models.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://en.wikipedia.org/wiki/Main_Page.

  2. 2.

    http://dbpedia.org.

  3. 3.

    https://radimrehurek.com/gensim/wiki.html.

  4. 4.

    http://dbpedia.org/sparql.

  5. 5.

    https://dumps.wikimedia.your.org/.

  6. 6.

    https://wiki.dbpedia.org/downloads-2016-10.

  7. 7.

    https://en.wikipedia.org/wiki/Wikipedia:Namespace.

  8. 8.

    http://babelnet.org.

References

  1. Agirre, E., Alfonseca, E., Hall, K.B., Kravalova, J., Pasca, M., Soroa, A.: A study on similarity and relatedness using distributional and WordNet-based approaches. In: Conference of the North American Chapter of the Association of Computational Linguistics, Proceedings, Boulder, Colorado, USA, 31 May–5 June 2009, pp. 19–27 (2009)

    Google Scholar 

  2. Bordes, A., Usunier, N., García-Durán, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: 27th Annual Conference on Neural Information Processing Systems, Lake Tahoe, Nevada, USA, 5–8 December 2013, pp. 2787–2795 (2013)

    Google Scholar 

  3. Fan, J., Lu, M., Ooi, B.C., Tan, W., Zhang, M.: A hybrid machine-crowdsourcing system for matching web tables. In: IEEE 30th International Conference on Data Engineering, Chicago, ICDE 2014, pp. 976–987 (2014)

    Google Scholar 

  4. Gabrilovich, E., Markovitch, S.: Computing semantic relatedness using Wikipedia-based explicit semantic analysis. In: IJCAI, Hyderabad, India, 6–12 January 2007, pp. 1606–1611 (2007)

    Google Scholar 

  5. Gong, X., Xu, H., Huang, L.: HAN: hierarchical association network for computing semantic relatedness. In: AAAI, New Orleans, Louisiana, USA, 2–7 February 2018 (2018)

    Google Scholar 

  6. Grover, A., Leskovec, J.: Node2vec: scalable feature learning for networks. In: ACM SIGKDD, San Francisco, CA, USA, 13–17 August 2016, pp. 855–864 (2016)

    Google Scholar 

  7. Han, X., Zhao, J.: Structural semantic relatedness: a knowledge-based method to named entity disambiguation. In: ACL, Uppsala, Sweden, 11–16 July 2010, pp. 50–59 (2010)

    Google Scholar 

  8. Hassan, S., Mihalcea, R.: Semantic relatedness using salient semantic analysis. In: AAAI, San Francisco, California, USA, 7–11 August 2011 (2011)

    Google Scholar 

  9. Iacobacci, I., Pilehvar, M.T., Navigli, R.: SensEmbed: learning sense embeddings for word and relational similarity. In: ACL, Beijing, China, 26–31 July 2015, Volume 1: Long Papers, pp. 95–105 (2015)

    Google Scholar 

  10. Leong, C.W., Mihalcea, R.: Measuring the semantic relatedness between words and images. In: IWCS, Oxford, UK, 12–14 January 2011 (2011)

    Google Scholar 

  11. Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. CoRR (2013)

    Google Scholar 

  12. Milne, D., Witten, I.H.: An effective, low-cost measure of semantic relatedness obtained from Wikipedia links. In: AAAI Workshop on Wikipedia and Artificial Intelligence: An Evolving Synergy, pp. 25–30 (2008)

    Google Scholar 

  13. Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: EMNLP, Doha, Qatar, 25–29 October 2014, pp. 1532–1543 (2014)

    Google Scholar 

  14. Perozzi, B., Al-Rfou, R., Skiena, S.: DeepWalk: online learning of social representations. In: ACM SIGKDD, KDD 2014, pp. 701–710 (2014)

    Google Scholar 

  15. Pirrò, G.: Reword: semantic relatedness in the web of data. In: AAAI, Toronto, Ontario, Canada, 22–26 July 2012 (2012)

    Google Scholar 

  16. Pucher, M.: WordNet-based semantic relatedness measures in automatic speech recognition for meetings. In: ACL, Prague, Czech Republic, 23–30 June 2007 (2007)

    Google Scholar 

  17. Qadir, A., Mendes, P.N., Gruhl, D., Lewis, N.: Semantic lexicon induction from twitter with pattern relatedness and flexible term length. In: AAAI, Austin, Texas, USA, 25–30 January 2015, pp. 2432–2439 (2015)

    Google Scholar 

  18. Rada, R., Mili, H., Bicknell, E., Blettner, M.: Development and application of a metric on semantic nets. IEEE Trans. Syst. Man Cybern. 19(1), 17–30 (1989)

    Article  Google Scholar 

  19. Sandulescu, V., Ester, M.: Detecting singleton review spammers using semantic similarity. In: WWW, Florence, Italy, 18–22 May 2015, Companion Volume, pp. 971–976 (2015)

    Google Scholar 

  20. Strube, M., Ponzetto, S.P.: Wikirelate! computing semantic relatedness using Wikipedia. In: AAAI, Boston, Massachusetts, USA, 16–20 July 2006, pp. 1419–1424 (2006)

    Google Scholar 

  21. Wu, L.Y., Fisch, A., Chopra, S., Adams, K., Bordes, A., Weston, J.: StarSpace: embed all the things! In: AAAI, New Orleans, Louisiana, USA, 2–7 February 2018, pp. 5569–5577 (2018)

    Google Scholar 

  22. Wu, Z., Giles, C.L.: Sense-aware semantic analysis: a multi-prototype word representation model using Wikipedia. In: AAAI, Austin, Texas, USA, 25–30 January 2015, pp. 2188–2194 (2015)

    Google Scholar 

  23. Yang, J., Fan, J., Wei, Z., Li, G., Liu, T., Du, X.: Cost-effective data annotation using game-based crowdsourcing. PVLDB 12(1), 57–70 (2018)

    Google Scholar 

  24. Yeh, E., Ramage, D., Manning, C.D., Agirre, E., Soroa, A.: WikiWalk: random walks on Wikipedia for semantic relatedness. In: Proceedings of the Workshop on Graph-based Methods for Natural Language Processing, Singapore, 7 August 2009, pp. 41–49 (2009)

    Google Scholar 

  25. Zesch, T., Müller, C., Gurevych, I.: Using wiktionary for computing semantic relatedness. In: AAAI, Chicago, Illinois, USA, 13–17 July 2008, pp. 861–866 (2008)

    Google Scholar 

  26. Zhang, K., Zhu, K.Q., Hwang, S.: An association network for computing semantic relatedness. In: AAAI, Austin, Texas, USA, 25–30 January 2015, pp. 593–600 (2015)

    Google Scholar 

  27. Zhang, W., Feng, W., Wang, J.: Integrating semantic relatedness and words’ intrinsic features for keyword extraction. In: IJCAI, Beijing, China, 3–9 August 2013, pp. 2225–2231 (2013)

    Google Scholar 

  28. Zhu, G., Iglesias, C.A.: Computing semantic similarity of concepts in knowledge graphs. IEEE Trans. Knowl. Data Eng. 29(1), 72–85 (2017)

    Article  Google Scholar 

Download references

Acknowledgments

This research is partially supported by National Natural Science Foundation of China (Grant No. 61572335, 61632016), the Natural Science Research Project of Jiangsu Higher Education Institution (No. 17KJA520003), and the Dongguan Innovative Research Team Program (No.2018607201008).

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Soochow University, Suzhou, China

    Jiapeng Li, Wei Chen, Binbin Gu, Junhua Fang, Zhixu Li & Lei Zhao

  2. Institute of Electronic and Information Engineering of UESTC in Guangdong, Dongguan, 523808, Guangdong, China

    Jiapeng Li

  3. IFLYTEK Research, Suzhou, China

    Zhixu Li

Authors
  1. Jiapeng Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wei Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Binbin Gu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Junhua Fang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zhixu Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Lei Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lei Zhao .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Guoliang Li

  2. Duke University, Durham, NC, USA

    Jun Yang

  3. University of Porto, Porto, Portugal

    Joao Gama

  4. Chiang Mai University, Chiang Mai, Thailand

    Juggapong Natwichai

  5. Beihang University, Beijing, China

    Yongxin Tong

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Li, J., Chen, W., Gu, B., Fang, J., Li, Z., Zhao, L. (2019). Measuring Semantic Relatedness with Knowledge Association Network. In: Li, G., Yang, J., Gama, J., Natwichai, J., Tong, Y. (eds) Database Systems for Advanced Applications. DASFAA 2019. Lecture Notes in Computer Science(), vol 11446. Springer, Cham. https://doi.org/10.1007/978-3-030-18576-3_40

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-18576-3_40

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-18575-6

  • Online ISBN: 978-3-030-18576-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation