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2018 | OriginalPaper | Chapter

Data Science with Vadalog: Bridging Machine Learning and Reasoning

Authors : Luigi Bellomarini, Ruslan R. Fayzrakhmanov, Georg Gottlob, Andrey Kravchenko, Eleonora Laurenza, Yavor Nenov, Stéphane Reissfelder, Emanuel Sallinger, Evgeny Sherkhonov, Lianlong Wu

Published in: Model and Data Engineering

Publisher: Springer International Publishing

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Abstract

Following the recent successful examples of large technology companies, many modern enterprises seek to build knowledge graphs to provide a unified view of corporate knowledge and to draw deep insights using machine learning and logical reasoning. There is currently a perceived disconnect between the traditional approaches for data science, typically based on machine learning and statistical modelling, and systems for reasoning with domain knowledge. In this paper we present a state-of-the-art Knowledge Graph Management System, Vadalog, which delivers highly expressive and efficient logical reasoning and provides seamless integration with modern data science toolkits, such as the Jupyter platform. We demonstrate how to use Vadalog to perform traditional data wrangling tasks, as well as complex logical and probabilistic reasoning. We argue that this is a significant step forward towards combining machine learning and reasoning in data science.

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Footnotes
Literature
1.
Abiteboul, S., Hull, R., Vianu, V.: Foundations of Databases. Addison-Wesley, Boston (1995)MATH
2.
Albagli, S., Ben-Eliyahu-Zohary, R., Shimony, S.E.: Markov network based ontology matching. J. Comput. Syst. Sci. 78(1), 105–118 (2012)MathSciNetCrossRef
3.
Arenas, M., Bertossi, L.E., Chomicki, J.: Consistent query answers in inconsistent databases. In: PODS, pp. 68–79. ACM Press (1999)
4.
Arenas, M., Gottlob, G., Pieris, A.: Expressive languages for querying the semantic web. In: PODS, pp. 14–26 (2014)
5.
Arming, S., Pichler, R., Sallinger, E.: Complexity of repair checking and consistent query answering. In: ICDT, LIPIcs, SD-LZI, vol. 48 (2016)
6.
Bach, S.H., Broecheler, M., Huang, B., Getoor, L.: Hinge-loss Markov random fields and probabilistic soft logic. J. Mach. Learn. Res. (JMLR) 18(109), 1–67 (2017)MathSciNetMATH
7.
Bellomarini, L., Gottlob, G., Pieris, A., Sallinger, E.: Swift logic for big data and knowledge graphs. In: IJCAI, pp. 2–10 (2017)
8.
Bellomarini, L., Gottlob, G., Pieris, A., Sallinger, E.: Swift logic for big data and knowledge graphs. In: Tjoa, A.M., Bellatreche, L., Biffl, S., van Leeuwen, J., Wiedermann, J. (eds.) SOFSEM 2018. LNCS, vol. 10706, pp. 3–16. Springer, Cham (2018)CrossRef
9.
Bellomarini, L., Gottlob, G., Pieris, A., Sallinger, E.: Swift logic for big data and enterprise knowledge graphs. In: AMW, The Vadalog System (2018)
10.
Bellomarini, L., Sallinger, E., Gottlob, G.: The Vadalog system: datalog-based reasoning for knowledge graphs. PVLDB 11(9), 975–987 (2018)
11.
Bizer, C., et al.: Dbpedia - a crystallization point for the web of data. J. Web Sem. 7(3), 154–165 (2009)CrossRef
12.
Box, G.E.P., Hunter, J.S., Hunter, W.G.: Statistics for Experimenters: Design, Innovation, and Discovery, 2nd edn. Wiley, Hoboken (2005)MATH
13.
Buneman, P., Khanna, S., Tan, W.C.: On propagation of deletions and annotations through views. In: PODS, pp. 150–158. ACM (2002)
14.
Calì, A., Gottlob, G., Kifer, M.: Taming the infinite chase: query answering under expressive relational constraints. J. Artif. Intell. Res. 48, 115–174 (2013)MathSciNetCrossRef
15.
Calì, A., Gottlob, G., Lukasiewicz, T.: A general datalog-based framework for tractable query answering over ontologies. J. Web Sem. 14, 57–83 (2012)CrossRef
16.
Calì, A., Gottlob, G., Lukasiewicz, T., Marnette, B., Pieris, A.: Datalog+/-: a family of logical knowledge representation and query languages for new applications. In: LICS, pp. 228–242 (2010)
17.
Calì, A., Gottlob, G., Pieris, A.: Towards more expressive ontology languages: the query answering problem. Artif. Intell. 193, 87–128 (2012)MathSciNetCrossRef
18.
19.
The UniProt Consortium: UniProt: the universal protein knowledgebase. Nucleic Acids Res. 45(Database-Issue), D158–D169 (2017)
20.
Csar, T., Lackner, M., Pichler, R., Sallinger, E.: Winner determination in huge elections with MapReduce. In: AAAI, pp. 451–458. AAAI Press (2017)
21.
22.
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, vol. 96, pp. 226–231 (1996)
23.
Fierens, D., et al.: Inference and learning in probabilistic logic programs using weighted Boolean formulas. TPLP 15(3), 358–401 (2015)MathSciNetMATH
24.
Furche, T., Gottlob, G., Grasso, G., Schallhart, C., Sellers, A.J.: OXPath: a language for scalable data extraction, automation, and crawling on the deep web. VLDB J. 22(1), 47–72 (2013)CrossRef
25.
Furche, T., Gottlob, G., Neumayr, B., Sallinger, E.: Towards a lingua franca for data wrangling. In: AMW, Data Wrangling for Big Data (2016)
26.
Furche, T., Grasso, G., Kravchenko, A., Schallhart, C.: Turn the page: automated traversal of paginated websites. In: ICWE, pp. 332–346 (2012)
27.
Getoor, L., Taskar, B.: Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning). The MIT Press, Cambridge (2007)MATH
28.
Glimm, B., et al.: SPARQL 1.1 entailment regimes. W3C Recommendation, 21 March 2013
29.
Gottlob, G., Pieris, A.: Beyond SPARQL under OWL 2 QL entailment regime: rules to the rescue. In: IJCAI, pp. 2999–3007 (2015)
30.
Gribkoff, E., Suciu, D.: Slimshot: in-database probabilistic inference for knowledge bases. PVLDB 9(7), 552–563 (2016)
31.
Guagliardo, P., Pichler, R., Sallinger, E.: Enhancing the updatability of projective views. In: AMW, CEUR Workshop Proceedings, vol. 1087. CEUR-WS.org (2013)
32.
Kolaitis, P.G., Pichler, R., Sallinger, E., Savenkov, V.: Nested dependencies: structure and reasoning. In: PODS, pp. 176–187. ACM (2014)
33.
Kolaitis, P.G., Pichler, R., Sallinger, E., Savenkov, V.: Limits of schema mappings. Theory Comput. Syst. 62(4), 899–940 (2018)MathSciNetCrossRef
34.
Konstantinou, N., et al.: The VADA architecture for cost-effective data wrangling. In: SIGMOD. ACM (2017)
35.
Kravchenko, A., Fayzrakhmanov, R.R., Sallinger, E.: Web page representations and data extraction with BERyL. In: Proceedings of MATWEP 2018, p. 8 (2018, in Press)
36.
Michels, C., Fayzrakhmanov, R.R., Ley, M., Sallinger, E., Schenkel, R.: Oxpath-based data acquisition for dblp. In: JCDL, pp. 319–320. IEEE CS (2017)
37.
Niu, F., Ré, C., Doan, A.H., Shavlik, J.W.: Tuffy: scaling up statistical inference in markov logic networks using an RDBMS. PVLDB 4(6), 373–384 (2011)
38.
Pichler, R., Sallinger, E., Savenkov, V.: Relaxed notions of schema mapping equivalence revisited. Theory Comput. Syst. 52(3), 483–541 (2013)MathSciNetCrossRef
39.
Poon, H., Domingos, P.M.: Unsupervised ontology induction from text. In: ACL, pp. 296–305 (2010)
40.
41.
Richardson, M., Domingos, P.M.: Markov logic networks. Mach. Learn. 62(1–2), 107–136 (2006)CrossRef
42.
Sallinger, E.: Reasoning about schema mappings. In: Dagstuhl Follow-Ups, Data Exchange, Information, and Streams, vol. 5, pp. 97–127. SD-LZI (2013)
43.
Sarawagi, S.: Information extraction. Found. Trends Databases 1(3), 261–377 (2008)CrossRef
44.
Shkapsky, A., Yang, M., Zaniolo, C.: Optimizing recursive queries with monotonic aggregates in deals. In: ICDE, pp. 867–878 (2015)
45.
Singla, P., Domingos, P.M.: Entity resolution with Markov logic. In: ICDM, pp. 572–582 (2006)
46.
Vrandecic, D., Krötzsch, M.: Wikidata: a free collaborative knowledgebase. Commun. ACM 57(10), 78–85 (2014)CrossRef
Metadata
Title
Data Science with Vadalog: Bridging Machine Learning and Reasoning
Authors
Luigi Bellomarini
Ruslan R. Fayzrakhmanov
Georg Gottlob
Andrey Kravchenko
Eleonora Laurenza
Yavor Nenov
Stéphane Reissfelder
Emanuel Sallinger
Evgeny Sherkhonov
Lianlong Wu
Copyright Year
2018
Book
Model and Data Engineering

Print ISBN: 978-3-030-00855-0

Electronic ISBN: 978-3-030-00856-7

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-030-00856-7_1

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