Abstract
In the real world, a majority of facts are not static or immutable but highly ephemeral. Each fact is valid for only a limited amount of time, or it stands in temporal dependencies. In addition, facts with time information are usually accompanied by a real-valued weight which witnesses the possibility of a fact. However, most of existing Knowledge Graphs (KGs) focus on static data thus impeding the comprehensive solution for the management of uncertain and temporal facts in KGs. To fill this gap, we emphasize the characteristics of time and propose a coherent management framework ETC (Eliminate Temporal Conflicts) for temporal consistency. ETC is based on maximum weight clique to detect temporal conflicts in uncertain temporal knowledge graphs and eliminate them to achieve the most probable knowledge graph according to related constraints. Constraint graphs with detailed description have first been proposed to identify temporal constraints for the conflict detection. Also, implicit constraints and weight conversion have been propose for conflict resolution. Experiments over two different temporal knowledge graphs demonstrate the high recall rate and precision rate of our framework.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Allen, J.F.: Maintaining knowledge about temporal intervals. Commun. ACM 26(11), 832–843 (1983)
Anagnostopoulos, E., Batsakis, S., Petrakis, E.G.M.: CHRONOS: a reasoning engine for qualitative temporal information in OWL. In: KES, pp. 70–77 (2013)
Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., Ives, Z.G.: Dbpedia: a nucleus for a web of open data. In: ISWC, pp. 722–735 (2007)
Bao, J., Duan, N., Yan, Z., Zhou, M., Zhao, T.: Constraint-based question answering with knowledge graph. In: COLING, pp. 2503–2514 (2016)
Batsakis, S., Stravoskoufos, K., Petrakis, E.G.M.: Temporal reasoning for supporting temporal queries in OWL 2.0. In: KES, pp. 558–567 (2011)
Chekol, M.W., Pirrò, G., Schoenfisch, J., Stuckenschmidt, H.: Marrying uncertainty and time in knowledge graphs. In: AAAI, pp. 88–94 (2017)
Chen, Y., Wang, D.Z.: Knowledge expansion over probabilistic knowledge bases. In: SIGMOD, pp. 649–660 (2014)
Dylla, M., Sozio, M., Theobald, M.: Resolving temporal conflicts in inconsistent RDF knowledge bases. In: BTW, pp. 474–493 (2011)
Fang, Z., Li, C., Xu, K.: An exact algorithm based on maxsat reasoning for the maximum weight clique problem. J. Artif. Intell. Res. 55, 799–833 (2016)
Gutiérrez, C., Hurtado, C.A., Vaisman, A.A.: Temporal RDF. In: ESWC, pp. 93–107 (2005)
Gutierrez, C., Hurtado, C.A., Vaisman, A.A.: Introducing time into RDF. IEEE Trans. Knowl. Data Eng. 19(2), 207–218 (2007)
McCusker, J.P., Dumontier, M., Yan, R., He, S., Dordick, J.S., McGuinness, D.L.: Finding melanoma drugs through a probabilistic knowledge graph. PeerJ Comput. Sci. 3, e106 (2017)
Mitchell, T.M., Cohen, W.W., Jr., E.R.H.: Never-ending learning. In: AAAI, pp. 2302–2310 (2015)
Padia, A.: Cleaning noisy knowledge graphs. In: ISWC (2017)
Saeeda, L., Kremen, P.: Temporal knowledge extraction for dataset discovery. In: ISWC (2017)
Schlobach, S., Huang, Z., Cornet, R., van Harmelen, F.: Debugging incoherent terminologies. J. Autom. Reason. 39(3), 317–349 (2007)
Singh, S.P., Markovitch, S. (eds.): Conference on Artificial Intelligence. AAAI Press, San Francisco (2017)
Singla, P., Domingos, P.M.: Lifted first-order belief propagation. In: AAAI, pp. 1094–1099 (2008)
Sirin, E., Parsia, B., Grau, B.C., Kalyanpur, A., Katz, Y.: Pellet: a practical OWL-DL reasoner. J. Web Sem. 5(2), 51–53 (2007)
Suchanek, F.M., Kasneci, G., Weikum, G.: Yago: a core of semantic knowledge. In: WWW, pp. 697–706 (2007)
Zou, L., Özsu, M.T., Chen, L., Shen, X., Huang, R., Zhao, D.: gstore: a graph-based SPARQL query engine. VLDB J. 23(4), 565–590 (2014)
Acknowledgements
This work was supported by the National Natural Science Foundation of China under Grant Nos. 61572335, 61572336, 61472263, 61402312 and 61402313, the Natural Science Foundation of Jiangsu Province of China under Grant No. BK20151223, and Collaborative Innovation Center of Novel Software Technology and Industrialization, Jiangsu, China.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Lu, L., Fang, J., Zhao, P., Xu, J., Yin, H., Zhao, L. (2018). Eliminating Temporal Conflicts in Uncertain Temporal Knowledge Graphs. In: Hacid, H., Cellary, W., Wang, H., Paik, HY., Zhou, R. (eds) Web Information Systems Engineering – WISE 2018. WISE 2018. Lecture Notes in Computer Science(), vol 11233. Springer, Cham. https://doi.org/10.1007/978-3-030-02922-7_23
Download citation
DOI: https://doi.org/10.1007/978-3-030-02922-7_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-02921-0
Online ISBN: 978-3-030-02922-7
eBook Packages: Computer ScienceComputer Science (R0)