Abstract
Recent efforts towards digitization of cultural heritage artifacts have resulted in a surge of information around these artifacts. However, the organization of these artifacts falls short with respect to accessing the facts across these entities. In this paper, we present a method to harvest the knowledge and form a knowledge graph from the digitized artifacts in the Museums of India repository via distant supervision to enable better accessibility of the facts and ability to extract new insights around the artifacts. Triples extracted from an open information extractor are first canonicalized to a standard taxonomy based on a metric-based scoring. Since a standard taxonomy is insufficient to capture all the relationships, we propose a sequential clustering based approach to add artifact specific relationships to the taxonomy (and to the knowledge graph). The graph is enriched by inferring missing facts based on a probabilistic soft logic approach seeded from a frequent item set framework. Human evaluation of the final knowledge graph showed an accuracy of \(75\%\) on par with knowledge bases like DBpedia.
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
References
Agirre, E., Aletras, N., Clough, P.D., Fernando, S., Goodale, P., Hall, M.M., Soroa, A., Stevenson, M.: Paths: a system for accessing cultural heritage collections. In: Conference System Demonstrations, pp. 151–156. ACL (2013)
Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., Ives, Z.: DBpedia: a nucleus for a web of open data. In: Aberer, K., et al. (eds.) ASWC/ISWC 2007. LNCS, vol. 4825, pp. 722–735. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-76298-0_52
Bach, S.H., Broecheler, M., Huang, B., Getoor, L.: Hinge-loss Markov random fields and probabilistic soft logic. arXiv preprint arXiv:1505.04406 (2015)
Banko, M., Cafarella, M.J., Soderland, S., Broadhead, M., Etzioni, O.: Open information extraction from the web. In: IJCAI, vol. 7, pp. 2670–2676 (2007)
Bollacker, K., Evans, C., Paritosh, P., Sturge, T., Taylor, J.: Freebase: a collaboratively created graph database for structuring human knowledge. In: Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, pp. 1247–1250. ACM (2008)
British Museum: http://www.britishmuseum.org/
Carlson, A., Betteridge, J., Kisiel, B., Settles, B., Hruschka Jr., E.R., Mitchell, T.M.: Toward an architecture for never-ending language learning. In: AAAI, vol. 5, p. 3 (2010)
Ester, M., Kriegel, H.P., Sander, J., Xu, X., et al.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: KDD (1996)
Europeana Museums: https://www.europeana.eu/portal/en
Fader, A., Soderland, S., Etzioni, O.: Identifying relations for open information extraction. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 1535–1545. Association for Computational Linguistics (2011)
Fernando, S., Stevenson, M.: Adapting wikification to cultural heritage. In: Proceedings of the 6th Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities, pp. 101–106. Association for Computational Linguistics (2012)
Finkel, J.R., Grenager, T., Manning, C.: Incorporating non-local information into information extraction systems by Gibbs sampling. In: Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics, pp. 363–370. Association for Computational Linguistics (2005)
Galárraga, L.A., Teflioudi, C., Hose, K., Suchanek, F.: AMIE: association rule mining under incomplete evidence in ontological knowledge bases. In: Proceedings of the 22nd International Conference on World Wide Web, pp. 413–422. ACM (2013)
Museums of India. http://museumsofindia.gov.in
Pujara, J., Sameer Singh, B.D.: Knowledge graph construction from text. In: AAAI Tutorial (2017)
Kobren, A., Logan, T., Sampangi, S., McCallum, A.: Domain specific knowledge base construction via crowdsourcing. In: Neural Information Processing Systems Workshop on Automated Knowledge Base Construction, AKBC, Montreal, Canada (2014)
Lee, H., Peirsman, Y., Chang, A., Chambers, N., Surdeanu, M., Jurafsky, D.: Stanford’s multi-pass sieve coreference resolution system at the CONLL 2011 shared task. In: Proceedings of the Fifteenth Conference on Computational Natural Language Learning: Shared Task, pp. 28–34. Association for Computational Linguistics (2011)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)
Miller, G.A.: Wordnet: a lexical database for english. Commun. ACM 38(11), 39–41 (1995)
Nakashole, N., Weikum, G., Suchanek, F.: PATTY: a taxonomy of relational patterns with semantic types. In: Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pp. 1135–1145. Association for Computational Linguistics (2012)
Pujara, J., Miao, H., Getoor, L., Cohen, W.: Knowledge graph identification. In: Alani, H., et al. (eds.) ISWC 2013. LNCS, vol. 8218, pp. 542–557. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-41335-3_34
Pujara, J., Miao, H., Getoor, L., Cohen, W.W.: Using semantics and statistics to turn data into knowledge. AI Mag. 36(1), 65–74 (2015)
Suchanek, F.M., Kasneci, G., Weikum, G.: YAGO: a core of semantic knowledge. In: Proceedings of the 16th International Conference on World Wide Web, pp. 697–706. ACM (2007)
Zaveri, A., Kontokostas, D., Sherif, M.A., Bühmann, L., Morsey, M., Auer, S., Lehmann, J.: User-driven quality evaluation of DBpedia. In: Proceedings of the 9th International Conference on Semantic Systems, pp. 97–104. ACM (2013)
Zhao, X., Xing, Z., Kabir, M.A., Sawada, N., Li, J., Lin, S.W.: HDSKG: harvesting domain specific knowledge graph from content of webpages. In: 2017 IEEE 24th International Conference on Software Analysis, Evolution and Reengineering (SANER), pp. 56–67. IEEE (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Sancheti, A., Maheshwari, P., Chaturvedi, R., Monsy, A.V., Goyal, T., Srinivasan, B.V. (2018). Harvesting Knowledge from Cultural Heritage Artifacts in Museums of India. In: Phung, D., Tseng, V., Webb, G., Ho, B., Ganji, M., Rashidi, L. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2018. Lecture Notes in Computer Science(), vol 10938. Springer, Cham. https://doi.org/10.1007/978-3-319-93037-4_25
Download citation
DOI: https://doi.org/10.1007/978-3-319-93037-4_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-93036-7
Online ISBN: 978-3-319-93037-4
eBook Packages: Computer ScienceComputer Science (R0)