Statistical Relational Learning with Soft Quantifiers

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• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9575
• 期：1
• 页码：60-75
• 全文大小：714 KB
• 参考文献：1.Bach, S.H., Broecheler, M., Huang, B., Getoor, L.: Hinge-loss markov random fields and probabilistic soft logic. [cs.LG] (2015). arXiv:​1505.​04406
  2.Bach, S.H., Huang, B., London, B., Getoor, L.: Hinge-loss Markov random fields: convex inference for structured prediction. In: Proceedings of the Uncertainty in Artificial Intelligence (UAI) (2013)
  3.Beltagy, I., Erk, K.: On the proper treatment of quantifiers in probabilistic logic semantics. In: Proceedings of the 11th International Conference on Computational Semantics (IWCS), p. 140 (2015)
  4.Beltagy, I., Erk, K., Mooney, R.J.: Probabilistic soft logic for semantic textual similarity. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (ACL), pp. 1210–1219 (2014)
  5.Bobillo, F., Straccia, U.: fuzzyDL: an expressive fuzzy description logic reasoner. In: Proceedings of the IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 923–930 (2008)
  6.Cao, T.H., Rossiter, J.M., Martin, T.P., Baldwin, J.F.: On the implementation of fril++ for object-oriented logic programming with uncertainty and fuzziness. In: Bouchon-Meunier, B., Gutiérrez-Ríos, J., Magdalena, L., Yager, R.R. (eds.) Technologies for Constructing Intelligent Systems 2. Studies in Fuzziness and Soft Computing, vol. 90, pp. 393–406. Springer, Heidelberg (2002)CrossRef
  7.Collins, M.: Discriminative training methods for hidden Markov models: theory and experiments with perceptron algorithms. In: Proceedings of the International Conference on Empirical methods in Natural Language Processing (ACL), pp. 1–8 (2002)
  8.Delgado, M., Sánchez, D., Vila, M.A.: Fuzzy cardinality based evaluation of quantified sentences. Int. J. Approximate Reason. 1, 23–66 (2000)MathSciNet CrossRef MATH
  9.Fakhraei, S., Huang, B., Raschid, L., Getoor, L.: Network-based drug-target interaction prediction with probabilistic soft logic. IEEE/ACM Trans. Comput. Biol. Bioinform. 11(5), 775–787 (2014)CrossRef
  10.Farnadi, G., Bach, S.H., Moens, M.F., Getoor, L., De Cock, M.: Extending psl with fuzzy quantifiers. In: Proceedings of the Fourth International Workshop on Statistical Relational, AI at AAAI (StarAI) (2014)
  11.Getoor, L., Taskar, B.: Introduction to Statistical Relational Learning. MIT press, Cambridge (2007)MATH
  12.Heider, F.: The Psychology of Interpersonal Relations. Wiley, New York (1958)CrossRef
  13.Huang, B., Kimmig, A., Getoor, L., Golbeck, J.: A flexible framework for probabilistic models of social trust. In: Greenberg, A.M., Kennedy, W.G., Bos, N.D. (eds.) SBP 2013. LNCS, vol. 7812, pp. 265–273. Springer, Heidelberg (2013)CrossRef
  14.Isbell, J.R., Marlow, W.H.: Attrition games. Naval Res. Logistics Q. 3(1–2), 71–94 (1956)MathSciNet CrossRef MATH
  15.Jain, D., Barthels, A., Beetz, M.: Adaptive Markov logic networks: learning statistical relational models with dynamic parameters. In: Proceedings of the European Conference on Artificial Intelligence (ECAI), pp. 937–942 (2010)
  16.Kazemi, S.M., Buchman, D., Kersting, K., Natarajan, S., Poole, D.: Relational logistic regression. In: Proceedings of the International Conference on Principles of Knowledge Representation and Reasoning (KR) (2014)
  17.Klir, G., Yuan, B.: Fuzzy Sets and Fuzzy Logic. Prentice Hall, New Jersey (1995)MATH
  18.Leskovec, J., Huttenlocher, D., Kleinberg, J.: Signed networks in social media. In: Proceedings of the 28th ACM Conference on Human Factors in Computing Systems (CHI) (2010)
  19.Lowd, D., Domingos, P.: Recursive random fields. In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), pp. 950–955 (2007)
  20.Milch, B., Zettlemoyer, L.S., Kersting, K., Haimes, M., Kaelbling, L.P.: Lifted probabilistic inference with counting formulas. In: Proceedings of the International Conference on Artificial Intelligence (AAAi) 8, 1062–1068 (2008)
  21.Muggleton, S., De Raedt, L.: Inductive Logic Programming: Theory and Methods. J. Logic Program. 19(20), 629–679 (1994)MathSciNet CrossRef MATH
  22.Poole, D., Buchman, D., Kazemi, S.M., Kersting, K., Natarajan, S.: Population size extrapolation in relational probabilistic modelling. In: Straccia, U., Calì, A. (eds.) SUM 2014. LNCS, vol. 8720, pp. 292–305. Springer, Heidelberg (2014)
  23.Poole, D., Buchman, D., Natarajan, S., Kersting, K.: Aggregation and population growth: the relational logistic regression and Markov logic cases. In: Proceedings of the International Workshop on Statistical Relational AI at UAI (StarAI) (2012)
  24.Prade, H., Richard, G., Serrurier, M.: Learning first order fuzzy logic rules. In: De Baets, B., Kaynak, O., Bilgiç, T. (eds.) IFSA 2003. LNCS, vol. 2715. Springer, Heidelberg (2003)
  25.Pujara, J., Miao, H., Getoor, L., Cohen, W.: Knowledge graph identification. In: Alani, H., Kagal, L., Fokoue, A., Groth, P., Biemann, C., Parreira, J.X., Aroyo, L., Noy, N., Welty, C., Janowicz, K. (eds.) ISWC 2013, Part I. LNCS, vol. 8218, pp. 542–557. Springer, Heidelberg (2013)CrossRef
  26.Richardson, M., Domingos, P.: Markov logic networks. Mach. Learn. 62(1–2), 107–136 (2006)CrossRef
  27.Van den Broeck, G., Meert, W., Darwiche, A.: Skolemization for weighted first-order model counting (2013). arXiv:​1312.​5378
  28.Victor, P., Cornelis, C., De Cock, M.: Trust and recommendations. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 645–675. Springer, Heidelberg (2011)CrossRef
  29.West, R., Paskov, H.S., Leskovec, J., Potts, C.: Exploiting social network structure for person-to-person sentiment analysis. Trans. Assoc. Comput. Linguist. (TACL) 2, 297–310 (2014)
  30.Yager, R.R.: On ordered weighted averaging aggregation operators in multicriteria decision making. IEEE Trans. Syst. Man Cybern. (IEEE SMC) 1, 183–190 (1988)MathSciNet CrossRef MATH
  31.Zadeh, L.A.: A computational approach to fuzzy quantifiers in natural languages. Comput. Math. Appl. 1, 149–184 (1983)MathSciNet CrossRef MATH
  
• 作者单位：Golnoosh Farnadi (16) (17)
  Stephen H. Bach (18)
  Marjon Blondeel (19)
  Marie-Francine Moens (17)
  Lise Getoor (20)
  Martine De Cock (16) (21)
  
  16. Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
  17. Department of Computer Science, Katholieke Universiteit Leuven, Leuven, Belgium
  18. Statistical Relational Learning Group, University of Maryland, College Park, USA
  19. Ghent University Global Campus, Incheon, South Korea
  20. Statistical Relational Learning Group, University of California, Santa Cruz, USA
  21. Center for Data Science, University of Washington, Tacoma, USA
  
• 丛书名：Inductive Logic Programming
• ISBN：978-3-319-40566-7
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：9575

文摘

Quantification in statistical relational learning (SRL) is either existential or universal, however humans might be more inclined to express knowledge using soft quantifiers, such as “most” and “a few”. In this paper, we define the syntax and semantics of PSL\(^Q\), a new SRL framework that supports reasoning with soft quantifiers, and present its most probable explanation (MPE) inference algorithm. To the best of our knowledge, PSL\(^Q\) is the first SRL framework that combines soft quantifiers with first-order logic rules for modeling uncertain relational data. Our experimental results for link prediction in social trust networks demonstrate that the use of soft quantifiers not only allows for a natural and intuitive formulation of domain knowledge, but also improves the accuracy of inferred results.