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Too Much Information: Can AI Cope with Modern Knowledge Graphs?

  • Markus KrötzschEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11511)

Abstract

Knowledge graphs play an important role in artificial intelligence (AI) applications – especially in personal assistants, question answering, and semantic search – and public knowledge bases like Wikidata are widely used in industry and research. However, modern AI includes many different techniques, including machine learning, data mining, natural language processing, which are often not able to use knowledge graphs in their full size and complexity. Feature engineering, sampling, and simplification are needed, and commonly achieved with custom preprocessing code. In this position paper, we argue that a more principled integrated approach to this task is possible using declarative methods from knowledge representation and reasoning. In particular, we suggest that modern rule-based systems are a promising platform for computing customised views on knowledge graphs, and for integrating the results of other AI methods back into the overall knowledge model.

Notes

Acknowledgements

This work is partly supported by Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) in project number 389792660 (TRR 248, Center for Perspicuous Systems), CRC 912 (Highly Adaptive Energy-Efficient Computing, HAEC), and Emmy Noether grant KR 4381/1-1.

References

  1. 1.
    Aberger, C.R., Tu, S., Olukotun, K., Ré, C.: EmptyHeaded: a relational engine for graph processing. In: Özcan, F., Koutrika, G., Madden, S. (eds.) Proceedings of the 2016 ACM SIGMOD International Conference on Management of Data, pp. 431–446. ACM (2016)Google Scholar
  2. 2.
    Abiteboul, S., Hull, R., Vianu, V.: Foundations of Databases. Addison Wesley, Reading (1994)Google Scholar
  3. 3.
    Aref, M., et al.: Design and implementation of the LogicBlox system. In: Sellis, T.K., Davidson, S.B., Ives, Z.G. (eds.) Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, pp. 1371–1382. ACM (2015)Google Scholar
  4. 4.
    Baget, J.-F., Leclère, M., Mugnier, M.-L., Rocher, S., Sipieter, C.: Graal: a toolkit for query answering with existential rules. In: Bassiliades, N., Gottlob, G., Sadri, F., Paschke, A., Roman, D. (eds.) RuleML 2015. LNCS, vol. 9202, pp. 328–344. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-21542-6_21CrossRefGoogle Scholar
  5. 5.
    Belleau, F., Nolin, M., Tourigny, N., Rigault, P., Morissette, J.: Bio2RDF: towards a mashup to build bioinformatics knowledge systems. J. Biomed. Inform. 41(5), 706–716 (2008)CrossRefGoogle Scholar
  6. 6.
    Bellomarini, L., Sallinger, E., Gottlob, G.: The Vadalog system: datalog-based reasoning for knowledge graphs. Proc. VLDB Endowment 11(9), 975–987 (2018)CrossRefGoogle Scholar
  7. 7.
    Benedikt, M., et al.: Benchmarking the chase. In: Sallinger, E., den Bussche, J.V., Geerts, F. (eds.) Proceedings of the 36th Symposium on Principles of Database Systems (PODS 2017), pp. 37–52. ACM (2017)Google Scholar
  8. 8.
    Benedikt, M., Leblay, J., Tsamoura, E.: PDQ: proof-driven query answering over web-based data. Proc. VLDB Endowment 7(13), 1553–1556 (2014)CrossRefGoogle Scholar
  9. 9.
    Bonifati, A., Ileana, I., Linardi, M.: Functional dependencies unleashed for scalable data exchange. In: Baumann, P., et al. (eds.) Proceedings of the 28th International Conference on Scientific and Statistical Database Management (SSDBM 2016), pp. 2:1–2:12. ACM (2016)Google Scholar
  10. 10.
    Borchmann, D.: Towards an error-tolerant construction of \(\cal{EL}^\bot \)-ontologies from data using formal concept analysis. In: Cellier, P., Distel, F., Ganter, B. (eds.) ICFCA 2013. LNCS (LNAI), vol. 7880, pp. 60–75. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-38317-5_4CrossRefzbMATHGoogle Scholar
  11. 11.
    Bordes, A., Usunier, N., García-Durán, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: Burges, C.J.C., Bottou, L., Ghahramani, Z., Weinberger, K.Q. (eds.) Proceedings of the 27th Annual Conference on Neural Information Processing Systems (NIPS 2013), pp. 2787–2795 (2013)Google Scholar
  12. 12.
    Burgstaller-Muehlbacher, S., et al.: Wikidata as a semantic framework for the Gene Wiki initiative. Database 2016, baw015 (2016)CrossRefGoogle Scholar
  13. 13.
    Calì, A., Gottlob, G., Lukasiewicz, T.: A general datalog-based framework for tractable query answering over ontologies. In: Paredaens, J., Su, J. (eds.) Proceedings of the 28th Symposium on Principles of Database Systems (PODS 2009), pp. 77–86. ACM (2009)Google Scholar
  14. 14.
    Calvanese, D., Giacomo, G.D., Lembo, D., Lenzerini, M., Rosati, R.: Tractable reasoning and efficient query answering in description logics: the DL-Lite family. J. Autom. Reasoning 39(3), 385–429 (2007)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Carral, D., Dragoste, I., Krötzsch, M.: Restricted chase (non)termination for existential rules with disjunctions. In: Sierra, C. (ed.) Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI 2017), pp. 922–928 (2017). ijcai.orgGoogle Scholar
  16. 16.
    Cuenca Grau, B., et al.: Acyclicity notions for existential rules and their application to query answering in ontologies. J. Artif. Intell. Res. 47, 741–808 (2013)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Cyganiak, R., Wood, D., Lanthaler, M. (eds.): RDF 1.1 Concepts and Abstract Syntax. W3C Recommendation, 25 February 2014. http://www.w3.org/TR/rdf11-concepts/
  18. 18.
    Deutsch, A., Nash, A., Remmel, J.B.: The chase revisited. In: Lenzerini, M., Lembo, D. (eds.) Proceedings of the 27th Symposium on Principles of Database Systems (PODS 2008), pp. 149–158. ACM (2008)Google Scholar
  19. 19.
    Eiter, T., Ianni, G., Schindlauer, R., Tompits, H.: A uniform integration of higher-order reasoning and external evaluations in answer-set programming. In: Kaelbling, L., Saffiotti, A. (eds.) Proceeding 19th Internation Joint Conference on Artificial Intelligence (IJCAI 2005), pp. 90–96. Professional Book Center (2005)Google Scholar
  20. 20.
    Fagin, R., Kolaitis, P.G., Miller, R.J., Popa, L.: Data exchange: semantics and query answering. Theoret. Comput. Sci. 336(1), 89–124 (2005)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Ferrucci, D.A., et al.: Building watson: an overview of the DeepQA project. AI Mag. 31(3), 59–79 (2010)CrossRefGoogle Scholar
  22. 22.
    Ganter, B., Wille, R.: Formal Concept Analysis: Mathematical Foundations. Springer, Heidelberg (1997)zbMATHGoogle Scholar
  23. 23.
    Geerts, F., Mecca, G., Papotti, P., Santoro, D.: That’s all folks! LLUNATIC goes open source. PVLDB 7(13), 1565–1568 (2014)Google Scholar
  24. 24.
    González, L., Hogan, A.: Modelling dynamics in semantic web knowledge graphs with formal concept analysis. In: Champin, P., Gandon, F.L., Lalmas, M., Ipeirotis, P.G. (eds.) Proceedings of the 2018 World Wide Web Conference (WWW 2018), pp. 1175–1184. ACM (2018)Google Scholar
  25. 25.
    Hanika, T., Marx, M., Stumme, G.: Discovering implicational knowledge in Wikidata. In: Cristea, D., et al. (eds.) ICFCA 2019, LNAI 11511, pp. 315–323. Springer, Cham (2019)Google Scholar
  26. 26.
    Harris, S., Seaborne, A. (eds.): SPARQL 1.1 Query Language. W3C Recommendation, 21 March 2013. http://www.w3.org/TR/sparql11-query/
  27. 27.
    Hernández, D., Hogan, A., Krötzsch, M.: Reifying RDF: what works well with wikidata? In: Liebig, T., Fokoue, A. (eds.) Proceedings of the 11th International Workshop on Scalable Semantic Web Knowledge Base Systems. CEUR Workshop Proceedings, vol. 1457, pp. 32–47. CEUR-WS.org (2015)Google Scholar
  28. 28.
    Ho, V.T., Stepanova, D., Gad-Elrab, M.H., Kharlamov, E., Weikum, G.: Learning rules from incomplete KGs using embeddings. In: van Erp, M., Atre, M., López, V., Srinivas, K., Fortuna, C. (eds.) Posters & Demonstrations, Industry and Blue Sky Ideas Tracks of the 17th International Semantic Web Conference (ISWC 2018). CEUR Workshop Proceedings, vol. 2180. CEUR-WS.org (2018)Google Scholar
  29. 29.
    Hoffart, J., Suchanek, F.M., Berberich, K., Weikum, G.: YAGO2: a spatially and temporally enhanced knowledge base from Wikipedia. J. Artif. Intell. 194, 28–61 (2013)MathSciNetCrossRefGoogle Scholar
  30. 30.
    Kaminski, M., Grau, B.C., Kostylev, E.V., Motik, B., Horrocks, I.: Foundations of declarative data analysis using limit datalog programs. In: Sierra, C. (ed.) Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI 2017), pp. 1123–1130 (2017). ijcai.org
  31. 31.
    Khamis, M.A., Ngo, H.Q., Nguyen, X., Olteanu, D., Schleich, M.: In-database learning with sparse tensors. In: den Bussche, J.V., Arenas, M. (eds.) Proceedings of the 37th Symposium on Principles of Database Systems (PODS 2018), pp. 325–340. ACM (2018)Google Scholar
  32. 32.
    Kontchakov, R., Lutz, C., Toman, D., Wolter, F., Zakharyaschev, M.: The combined approach to ontology-based data access. In: Walsh, T. (ed.) Proceedings 22nd International Joint Conference on Artificial Intelligence (IJCAI 2011). pp. 2656–2661. AAAI Press/IJCAI (2011)Google Scholar
  33. 33.
    Krötzsch, M.: Ontologies for knowledge graphs? In: Artale, A., Glimm, B., Kontchakov, R. (eds.) Proceedings of the 30th International Workshop on Description Logics (DL 2017). CEUR Workshop Proceedings, vol. 1879. CEUR-WS.org (2017)Google Scholar
  34. 34.
    Krötzsch, M., Marx, M., Ozaki, A., Thost, V.: Attributed description logics: Reasoning on knowledge graphs. In: Lang, J. (ed.) Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI 2018), pp. 5309–5313 (2018).  https://doi.org/10.24963/ijcai.2018/743
  35. 35.
    Krötzsch, M., Thost, V.: Ontologies for knowledge graphs: breaking the rules. In: Groth, P., et al. (eds.) ISWC 2016. LNCS, vol. 9981, pp. 376–392. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46523-4_23CrossRefGoogle Scholar
  36. 36.
    Krötzsch, M., Marx, M., Rudolph, S.: The power of the terminating chase. In: Barceló, P., Calautti, M. (eds.) Proceedings of the 22nd International Conference on Database Theory (ICDT 2019). LIPIcs, vol. 127, pp. 3:1–3:17. Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik (2019)Google Scholar
  37. 37.
    Lenzerini, M.: Data integration: a theoretical perspective. In: Popa, L. (ed.) Proceedings of the 21st Symposium on Principles of Database Systems (PODS 2002), pp. 233–246. ACM (2002)Google Scholar
  38. 38.
    Leskovec, J., Rajaraman, A., Ullman, J.D.: Mining of Massive Datasets, 2nd edn. Cambridge University Press, Cambridge (2014)CrossRefGoogle Scholar
  39. 39.
    Malyshev, S., Krötzsch, M., González, L., Gonsior, J., Bielefeldt, A.: Getting the most out of wikidata: semantic technology usage in Wikipedia’s knowledge graph. In: Vrandečić, D., et al. (eds.) ISWC 2018. LNCS, vol. 11137, pp. 376–394. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-00668-6_23CrossRefGoogle Scholar
  40. 40.
    Marnette, B.: Generalized schema-mappings: from termination to tractability. In: Paredaens, J., Su, J. (eds.) Proceedings of the 28th Symposium on Principles of Database Systems (PODS 2009), pp. 13–22. ACM (2009)Google Scholar
  41. 41.
    Marx, M., Krötzsch, M., Thost, V.: Logic on MARS: Ontologies for generalised property graphs. In: Sierra, C. (ed.) Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI 2017), pp. 1188–1194 (2017)Google Scholar
  42. 42.
    Nenov, Y., Piro, R., Motik, B., Horrocks, I., Wu, Z., Banerjee, J.: RDFox: a highly-scalable RDF store. In: Arenas, M., et al. (eds.) ISWC 2015. LNCS, vol. 9367, pp. 3–20. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-25010-6_1CrossRefGoogle Scholar
  43. 43.
    Nickel, M., Murphy, K., Tresp, V., Gabrilovich, E.: A review of relational machine learning for knowledge graphs. Proc. IEEE 104(1), 11–33 (2016)CrossRefGoogle Scholar
  44. 44.
    openCypher community: Cypher Query Language Reference, Version 9 (2019). http://www.opencypher.org/resources
  45. 45.
    Prud’hommeaux, E., Seaborne, A. (eds.): SPARQL Query Language for RDF. W3C Recommendation, 15 January 2008. http://www.w3.org/TR/rdf-sparql-query/
  46. 46.
    Rodriguez, M.A., Neubauer, P.: Constructions from dots and lines. Bull. Am. Soc. Inf. Sci. Technol. 36(6), 35–41 (2010)CrossRefGoogle Scholar
  47. 47.
    Rudolph, S.: Exploring relational structures via \({\cal{F\!LE}}\). In: Wolff, K.E., Pfeiffer, H.D., Delugach, H.S. (eds.) ICCS-ConceptStruct 2004. LNCS (LNAI), vol. 3127, pp. 196–212. Springer, Heidelberg (2004).  https://doi.org/10.1007/978-3-540-27769-9_13CrossRefzbMATHGoogle Scholar
  48. 48.
    Simonite, T.: Inside the Alexa-friendly world of Wikidata. WIRED Magazine 27.03 (2019). https://www.wired.com/story/inside-the-alexa-friendly-world-of-wikidata/. Accessed 16 Mar 2019
  49. 49.
    Tanon, T.P., Vrandecic, D., Schaffert, S., Steiner, T., Pintscher, L.: From freebase to wikidata: the great migration. In: Bourdeau, J., Hendler, J., Nkambou, R., Horrocks, I., Zhao, B.Y. (eds.) Proceedings of the 25th International Conference on World Wide Web (WWW 2016), pp. 1419–1428. ACM (2016)Google Scholar
  50. 50.
    Urbani, J., Jacobs, C., Krötzsch, M.: Column-oriented Datalog materialization for large knowledge graphs. In: Schuurmans, D., Wellman, M.P. (eds.) Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI 2016), pp. 258–264. AAAI Press (2016)Google Scholar
  51. 51.
    Urbani, J., Krötzsch, M., Jacobs, C., Dragoste, I., Carral, D.: Efficient model construction for horn logic with VLog. In: Galmiche, D., Schulz, S., Sebastiani, R. (eds.) IJCAR 2018. LNCS (LNAI), vol. 10900, pp. 680–688. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-94205-6_44CrossRefGoogle Scholar
  52. 52.
    Vrandečić, D., Krötzsch, M.: Wikidata: a free collaborative knowledgebase. Commun. ACM 57(10), 78–85 (2014)CrossRefGoogle Scholar
  53. 53.
    Wagner, C., Graells-Garrido, E., Garcia, D., Menczer, F.: Women through the glass ceiling: gender asymmetries in Wikipedia. EPJ Data Sci. 5(1), 5 (2016)CrossRefGoogle Scholar
  54. 54.
    Wang, Z., Zhang, J., Feng, J., Chen, Z.: Knowledge graph embedding by translating on hyperplanes. In: Brodley, C.E., Stone, P. (eds.) Proceedings of the 28th AAAI Conference on Artificial Intelligence (AAAI 2014), pp. 1112–1119. AAAI Press (2014)Google Scholar

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Authors and Affiliations

  1. 1.TU DresdenDresdenGermany

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