2015 AReviewofRelationalMachineLearn

From GM-RKB
Jump to: navigation, search

Subject Headings: Google Knowledge Vault.

Notes

  • Pre-published as arXiv:1503.00759 Journal.

Cited By

Quotes

Abstract

Relational machine learning studies methods for the statistical analysis of relational, or graph-structured, data. In this paper, we provide a review of how such statistical models can be "trained" on large knowledge graphs, and then used to predict new facts about the world (which is equivalent to predicting new edges in the graph). In particular, we discuss two different kinds of statistical relational models, both of which can scale to massive datasets. The first is based on tensor factorization methods and related latent variable models. The second is based on mining observable patterns in the graph. We also show how to combine these latent and observable models to get improved modeling power at decreased computational cost. Finally, we discuss how such statistical models of graphs can be combined with text-based information extraction methods for automatically constructing knowledge graphs from the Web. In particular, we discuss Google's Knowledge Vault project.

1. Introduction

Machine learning typically works with a data matrix, where each row represents an object characterized by a feature vector of attributes (which might be numeric or categorical), and where the main tasks are to learn a mapping from this feature vector to an output prediction of some form, or to perform unsupervised learning like clustering or factor analysis. In Statistical Relational Learning (SRL), the representation of an object can contain its relationships to other objects. Thus the data is in the form of a graph, consisting of nodes (entities) and labelled edges (relationships between entities). The main goals of SRL include prediction of missing edges, prediction of properties of the nodes, and clustering the nodes based on their connectivity patterns. These tasks arise in many settings such as analysisof social networks and biological pathways. For further information on SRL see [1, 2, 3].

In this article, we review a variety of techniques from the SRL community and explain how they can be applied to large-scale knowledge graphs (KGs), i.e, graph structured knowledge bases (KBs) which store factual information in form of relationships between entities. Recently, a large number of knowledge graphs have been created, including YAGO [4], DBpedia [5], NELL [6], Freebase [ 7], and the Google Knowledge Graph [8]. As we discuss in Section II, these graphs contain millions of nodes and billions of edges. This causes us to focus on scalable SRL techniques, which take time that is linear in the size of the graph.

In addition to typical applications of statistical relational learning, we will also discuss how SRL can aid information extraction methods to “grow” a KG automatically. In particular, we will show how SRL can be used to train a “prior” model based on an existing KG, and then combine its predictions with “noisy” facts that are automatically extracted from the web using machine reading methods (see e.g., [9, 10]). This is the approach adopted in Google’s Knowledge Vault project, as we explain in Section VIII.

The remainder of this paper is structured as follows. In Section II we introduce knowledge graphs and some of their properties. Section III discusses SRL and how it can be applied to knowledge graphs. There are two main classes of SRL techniques: those that capture the correlation between the nodes / edges using latent variables, and those that capture the correlation directly using statistical models based on the observable properties of the graph. We discuss these two families in Section IV and Section V, respectively. Section VI describes approaches for combining these two approaches, to get the best of both worlds. In Section VII we discuss relational learning using Markov Random Fields. In Section VIII we describe how SRL can be used in automated knowledge base construction projects. In Section IX we discuss extensions of the presented methods, and Section X presents our conclusions.

II. KNOWLEDGE GRAPHS=

In this section, we discuss knowledge graphs: how they are represented, how they are created, and how they are used.

A. Knowledge representation

Relational knowledge representation as used in KGs has a long history in logic and artificial intelligence [11]. More recently, it has been used in the Semantic Web to represent information in machine-readable form, in order to enable intelligent agents operating on a “web of data” [12]. While the original vision of the Semantic Web remains to be fully realized, parts of it have been achieved. In particular, the concept of linked data [13, 14] has gained traction, as it facilitates publishing and interlinking data on the Web in relational form using the W3C Resource Description Framework (RDF) [15, 16].

In this article, we will loosely follow the RDF standard and represent facts in the form of binary relationships, in particular (subject, predicate, object) (SPO) triples, where subject and object are entities and predicate is the type of a relation. (We discuss how to represent higher-order relations in Section IX-A.) The existence of a particular SPO triple indicates an existing fact, i.e., that the respective entities are in a relationship of the respective type. For instance, the information Leonard Nimoy was an actor who played the character Spock in the science-fiction movie Star Trek can be expressed via the following set of SPO triples:

subject predicate object 
(LeonardNimoy, profession, Actor) 
(LeonardNimoy, starredIn, StarTrek) 
(LeonardNimoy, played, Spock) 
(Spock, characterIn, StarTrek) 
(StarTrek, genre, ScienceFiction) 

We can combine all the SPO triples together to form a multigraph, where nodes represent entities (all subjects and objects), and directed edges represent relationships. The direction of an edge indicates whether entities occur as subjects or objects, i.e., an edge points from the subject to the object. The different relation types are represented via different types of edges (also called edge labels). This construction is called a knowledge graph (KG), or sometimes a heterogeneous information network [17].) See Figure 1 for an example.

In addition to a collection of facts, knowledge graphs often provide type hierarchies (Leonard Nimoy is an actor, which is a person, which is a living thing) and type constraints (e.g., a person can only marry another person, not a thing).

B. Open vs closed world assumption

While existing triples always encode true relationships (facts), there are different paradigms for the interpretation of nonexisting triples:

For example, the fact that in Figure 1 there is no starredIn edge from Leonard

...

References

  • [1] L. Getoor and B. Taskar, Eds., Introduction to statistical relational learning. MIT Press, 2007.
  • [2] S. Dzeroski and N. Lavra?c, Relational Data Mining. Springer Science & Business Media, 2001.
  • [3] L. De Raedt, Logical and relational learning. Springer, 2008.
  • [4] F. M. Suchanek, G. Kasneci, and G. Weikum, “Yago: A Core of Semantic Knowledge,” in Proceedings of the 16th International Conference on World Wide Web. New York, NY, USA: ACM, 2007, pp. 697–706.
  • [5] S. Auer, C. Bizer, G. Kobilarov, J. Lehmann, R. Cyganiak, and Z. Ives, “DBpedia: A Nucleus for a Web of Open Data,” in The Semantic Web. Springer Berlin Heidelberg, 2007, vol. 4825, pp. 722–735.
  • [6] A. Carlson, J. Betteridge, B. Kisiel, B. Settles, E. R. H. Jr, and T. M. Mitchell, “Toward an Architecture for Never-Ending Language Learning,” in Proceedings of the Twenty-Fourth Conference on Artificial Intelligence (AAAI 2010). AAAI Press, 2010, pp. 1306–1313.
  • [7] K. Bollacker, C. Evans, P. Paritosh, T. Sturge, and J. Taylor, “Freebase: a collaboratively created graph database for structuring human knowledge,” in Proceedings of the 2008 ACM SIGMOD International Conference on Management of data. ACM, 2008, pp. 1247–1250.
  • [8] A. Singhal, “Introducing the Knowledge Graph: things, not strings,” May 2012. [Online]. Available: http://googleblog.blogspot.com/2012/05/introducing-knowledge-graph-things-not.html
  • [9] G. Weikum and M. Theobald, “From information to knowledge: harvesting entities and relationships from web sources,” in Proceedings of the twenty-ninth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems. ACM, 2010, pp. 65–76.
  • [10] J. Fan, R. Hoffman, A. A. Kalyanpur, S. Riedel, F. Suchanek, and P. P. Talukdar, “AKBC-WEKEX 2012: The Knowledge Extraction Workshop at NAACLHLT,” 2012. [Online]. Available: https://akbcwekex2012.wordpress.com/
  • [11] R. Davis, H. Shrobe, and P. Szolovits, “What is a knowledge representation?” AI Magazine, vol. 14, no. 1, pp. 17–33, 1993.
  • [12] T. Berners-Lee, J. Hendler, and O. Lassila, “The Semantic Web,” 2001. [Online]. Available: http://www. scientificamerican.com/article/the-semantic-web/
  • [13] T. Berners-Lee, “Linked Data - Design Issues,” Jul. 2006. [Online]. Available: http://www.w3.org/DesignIssues/LinkedData.html
  • [14] C. Bizer, T. Heath, and T. Berners-Lee, “Linked data - the story so far,” International Journal on Semantic Web and Information Systems, vol. 5, no. 3, pp. 1–22, 2009.
  • [15] G. Klyne and J. J. Carroll, “Resource Description Framework (RDF): Concepts and Abstract Syntax,” Feb. 2004. [Online]. Available: http://www.w3.org/TR/2004/REC-rdf-concepts-20040210/
  • [16] R. Cyganiak, D. Wood, and M. Lanthaler, “RDF 1.1 Concepts and Abstract Syntax,” Feb. 2014. [Online]. Available: http://www.w3.org/TR/2014/REC-rdf11-concepts-20140225/
  • [17] Y. Sun and J. Han, “Mining Heterogeneous Information Networks: Principles and Methodologies,” Synthesis Lectures on Data Mining and Knowledge Discovery, vol. 3, no. 2, pp. 1–159, 2012.
  • [18] R. West, E. Gabrilovich, K. Murphy, S. Sun, R. Gupta, and D. Lin, “Knowledge Base Completion via Search- Based Question Answering,” in Proceedings of the 23rd International Conference on World Wide Web, 2014, pp. 515–526.
  • [19] D. B. Lenat, “CYC: A Large-scale Investment in Knowledge Infrastructure,” Commun. ACM, vol. 38, no. 11, pp. 33–38, Nov. 1995.
  • [20] G. A. Miller, “WordNet: A Lexical Database for English,” Commun. ACM, vol. 38, no. 11, pp. 39–41, Nov. 1995.
  • [21] O. Bodenreider, “The Unified Medical Language System (UMLS): integrating biomedical terminology,” Nucleic Acids Research, vol. 32, no. Database issue, pp. D267– 270, Jan. 2004.
  • [22] D. Vrande?ci´c and M. Krötzsch, “Wikidata: a free collaborative knowledgebase,” Communications of the ACM, vol. 57, no. 10, pp. 78–85, 2014.
  • [23] J. Hoffart, F. M. Suchanek, K. Berberich, and G. Weikum, “YAGO2: a spatially and temporally enhanced knowledge base from Wikipedia,” Artificial Intelligence, vol. 194, pp. 28–61, 2013.
  • [24] X. Dong, E. Gabrilovich, G. Heitz, W. Horn, N. Lao, K. Murphy, T. Strohmann, S. Sun, and W. Zhang, “Knowledge Vault: A Web-scale Approach to Probabilistic Knowledge Fusion,” in Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2014, pp. 601–610.
  • [25] N. Nakashole, G. Weikum, and F. Suchanek, “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, 2012, pp. 1135–1145.
  • [26] F. Niu, C. Zhang, C. Ré, and J. Shavlik, “Elementary: Large-scale knowledge-base construction via machine learning and statistical inference,” International Journal on Semantic Web and Information Systems (IJSWIS), vol. 8, no. 3, pp. 42–73, 2012.
  • [27] N. Nakashole, M. Theobald, and G. Weikum, “Scalable knowledge harvesting with high precision and high recall,” in Proceedings of the fourth ACM International Conference on Web search and data mining. ACM, 2011, pp. 227–236.
  • [28] A. Fader, S. Soderland, and O. Etzioni, “Identifying relations for open information extraction,” in Proceedings of the Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA, USA: Association for Computational Linguistics, 2011, pp. 1535–1545.
  • [29] M. Schmitz, R. Bart, S. Soderland, O. Etzioni, and others, “Open language learning for information extraction,” in Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. Association for Computational Linguistics, 2012, pp. 523–534.
  • [30] J. Fan, D. Ferrucci, D. Gondek, and A. Kalyanpur, “Prismatic: Inducing knowledge from a large scale lexicalized relation resource,” in Proceedings of the NAACL HLT 2010 First International Workshop on Formalisms and Methodology for Learning by Reading. Association for Computational Linguistics, 2010, pp. 122–127.
  • [31] B. Suh, G. Convertino, E. H. Chi, and P. Pirolli, “The Singularity is Not Near: Slowing Growth of Wikipedia,” in Proceedings of the 5th International Symposium on Wikis and Open Collaboration. New York, NY, USA: ACM, 2009, pp. 8:1–8:10.
  • [32] O. Etzioni, A. Fader, J. Christensen, S. Soderland, and M. Mausam, “Open Information Extraction: The Second Generation,” in Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence - Volume Volume One. Barcelona, Catalonia, Spain: AAAI Press, 2011, pp. 3–10.
  • [33] D. B. Lenat and E. A. Feigenbaum, “On the thresholds of knowledge,” Artificial intelligence, vol. 47, no. 1, pp. 185–250, 1991.
  • [34] R. Qian, “Understand Your World with Bing, bing search blog,” Mar. 2013. [Online]. Available: http://blogs.bing.com/search/2013/03/21/understand-your-world-with-bing/
  • [35] D. Ferrucci, E. Brown, J. Chu-Carroll, J. Fan, D. Gondek, A. A. Kalyanpur, A. Lally, J. W. Murdock, E. Nyberg, J. Prager, and others, “Building Watson: An overview of the DeepQA project,” AI magazine, vol. 31, no. 3, pp. 59–79, 2010.
  • [36] F. Belleau, M.-A. Nolin, N. Tourigny, P. Rigault, and J. Morissette, “Bio2rdf: towards a mashup to build bioinformatics knowledge systems,” Journal of Biomedical Informatics, vol. 41, no. 5, pp. 706–716, 2008.
  • [37] A. Ruttenberg, J. A. Rees, M. Samwald, and M. S. Marshall, “Life sciences on the Semantic Web: the Neurocommons and beyond,” Briefings in Bioinformatics, vol. 10, no. 2, pp. 193–204, Mar. 2009.
  • [38] V. Momtchev, D. Peychev, T. Primov, and G. Georgiev, “Expanding the pathway and interaction knowledge in linked life data,” Proceedings of of International Semantic Web Challenge, 2009.
  • [39] G. Angeli and C. Manning, “Philosophers are Mortal: Inferring the Truth of Unseen Facts,” in Proceedings of the Seventeenth Conference on Computational Natural Language Learning. Sofia, Bulgaria: Association for Computational Linguistics, Aug. 2013, pp. 133–142.
  • [40] B. Taskar, M.-F. Wong, P. Abbeel, and D. Koller, “Link Prediction in Relational Data,” in Advances in Neural Information Processing Systems, S. Thrun, L. Saul, and B. Schölkopf, Eds., vol. 16. Cambridge, MA: MIT Press, 2004.
  • [41] L. Getoor and C. P. Diehl, “Link mining: a survey,” ACM SIGKDD Explorations Newsletter, vol. 7, no. 2, pp. 3–12, 2005.
  • [42] H. B. Newcombe, J. M. Kennedy, S. J. Axford, and A. P. James, “Automatic Linkage of Vital Records Computers can be used to extract "follow-up" statistics of families from files of routine records,” Science, vol. 130, no. 3381, pp. 954–959, Oct. 1959.
  • [43] S. Tejada, C. A. Knoblock, and S. Minton, “Learning object identification rules for information integration,” Information Systems, vol. 26, no. 8, pp. 607–633, 2001.
  • [44] E. Rahm and P. A. Bernstein, “A survey of approaches to automatic schema matching,” the VLDB Journal, vol. 10, no. 4, pp. 334–350, 2001.
  • [45] A. Culotta and A. McCallum, “Joint deduplication of multiple record types in relational data,” in Proceedings of the 14th ACM International Conference on Information and knowledge management. ACM, 2005, pp. 257–258.
  • [46] P. Singla and P. Domingos, “Entity Resolution with Markov Logic,” in Data Mining, 2006. ICDM ’06. Sixth International Conference on, Dec. 2006, pp. 572–582.
  • [47] I. Bhattacharya and L. Getoor, “Collective entity resolution in relational data,” ACM Trans. Knowl. Discov. Data, vol. 1, no. 1, Mar. 2007.
  • [48] S. E. Whang and H. Garcia-Molina, “Joint Entity Resolution,” in 2012 IEEE 28th International Conference on Data Engineering. Washington, DC, USA: IEEE Computer Society, 2012, pp. 294–305.
  • [49] S. Fortunato, “Community detection in graphs,” Physics Reports, vol. 486, no. 3, pp. 75–174, 2010.
  • [50] J. C. Platt, “Probabilities for SV Machines,” in Advances in Large Margin Classifiers. MIT Press, 1999, pp. 61– 74.
  • [51] L. A. Galárraga, C. Teflioudi, K. Hose, and F. Suchanek, “AMIE: Association Rule Mining Under Incomplete Evidence in Ontological Knowledge Bases,” in Proceedings of the 22nd International Conference on World Wide Web, 2013, pp. 413–422.
  • [52] L. Bottou, “Large-Scale Machine Learning with Stochastic Gradient Descent,” in Proceedings of COMPSTAT’2010. Physica-Verlag HD, 2010, pp. 177–186.
  • [53] M. E. J. Newman, “The structure of scientific collaboration networks,” Proceedings of the National Academy of Sciences, vol. 98, no. 2, pp. 404–409, Jan. 2001, arXiv: cond-mat/0007214.
  • [54] D. Liben-Nowell and J. Kleinberg, “The link-prediction problem for social networks,” Journal of the American society for information science and technology, vol. 58, no. 7, pp. 1019–1031, 2007.
  • [55] D. Jensen and J. Neville, “Linkage and Autocorrelation Cause Feature Selection Bias in Relational Learning,” in Proceedings of the Nineteenth International Conference on Machine Learning. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc., 2002, pp. 259–266.
  • [56] P. W. Holland, K. B. Laskey, and S. Leinhardt, “Stochastic blockmodels: First steps,” Social networks, vol. 5, no. 2, pp. 109–137, 1983.
  • [57] C. J. Anderson, S. Wasserman, and K. Faust, “Building stochastic blockmodels,” Social Networks, vol. 14, no. 1–2, pp. 137–161, 1992, special Issue on Blockmodels.
  • [58] P. Hoff, “Modeling homophily and stochastic equivalence in symmetric relational data,” in Advances in Neural Information Processing Systems 20. Curran Associates, Inc., 2008, pp. 657–664.
  • [59] M. Nickel, V. Tresp, and H.-P. Kriegel, “A Three-Way Model for Collective Learning on Multi-Relational Data,” in Proceedings of the 28th International Conference on Machine Learning, 2011, pp. 809–816.
  • [60] M. Nickel, V. Tresp, and H.-P. Kriegel, “Factorizing YAGO: scalable machine learning for linked data,” in Proceedings of the 21st International Conference on World Wide Web, 2012, pp. 271–280.
  • [61] M. Nickel, “Tensor factorization for relational learning,” PhD Thesis, Ludwig-Maximilians-Universität München, Aug. 2013.
  • [62] Y. Koren, R. Bell, and C. Volinsky, “Matrix factorization techniques for recommender systems,” IEEE Computer, vol. 42, no. 8, pp. 30–37, 2009.
  • [63] T. G. Kolda and B. W. Bader, “Tensor Decompositions and Applications,” SIAM Review, vol. 51, no. 3, pp. 455–500, 2009.
  • [64] M. Nickel and V. Tresp, “Logistic Tensor-Factorization for Multi-Relational Data,” in Structured Learning: Inferring Graphs from Structured and Unstructured Inputs (SLG 2013). Workshop at ICML’13, 2013.
  • [65] K.-W. Chang, W.-t. Yih, B. Yang, and C. Meek, “Typed Tensor Decomposition of Knowledge Bases for Relation Extraction,” in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing. ACL – Association for Computational Linguistics, Oct. 2014.
  • [66] S. Kok and P. Domingos, “Statistical Predicate Invention,” in Proceedings of the 24th International Conference on Machine Learning. New York, NY, USA: ACM, 2007, pp. 433–440.
  • [67] Z. Xu, V. Tresp, K. Yu, and H.-P. Kriegel, “Infinite Hidden Relational Models,” in Proceedings of the 22nd International Conference on Uncertainity in Artificial Intelligence. AUAI Press, 2006, pp. 544–551.
  • [68] C. Kemp, J. B. Tenenbaum, T. L. Griffiths, T. Yamada, and N. Ueda, “Learning systems of concepts with an infinite relational model,” in Proceedings of the Twenty-First National Conference on Artificial Intelligence, vol. 3, 2006, p. 5.
  • [69] I. Sutskever, J. B. Tenenbaum, and R. R. Salakhutdinov, “Modelling Relational Data using Bayesian Clustered Tensor Factorization,” in Advances in Neural Information Processing Systems 22, 2009, pp. 1821–1828.
  • [70] D. Krompaß, M. Nickel, and V. Tresp, “Large-Scale Factorization of Type-Constrained Multi-Relational Data,” in Proceedings of the 2014 International Conference on Data Science and Advanced Analytics (DSAA’2014), 2014.
  • [71] M. Nickel and V. Tresp, “Learning Taxonomies from Multi-Relational Data via Hierarchical Link- Based Clustering,” in Learning Semantics. Workshop at NIPS’11, Granada, Spain, 2011.
  • [72] T. Kolda and B. Bader, “The TOPHITS Model for Higher-order Web Link Analysis,” in Proceedings of Link Analysis, Counterterrorism and Security 2006, 2006.
  • [73] T. Franz, A. Schultz, S. Sizov, and S. Staab, “Triplerank: Ranking semantic web data by tensor decomposition,” The Semantic Web-ISWC 2009, pp. 213–228, 2009.
  • [74] L. Drumond, S. Rendle, and L. Schmidt-Thieme, “Predicting RDF Triples in Incomplete Knowledge Bases with Tensor Factorization,” in Proceedings of the 27th Annual ACM Symposium on Applied Computing. Riva del Garda, Italy: ACM, 2012, pp. 326–331.
  • [75] S. Rendle and L. Schmidt-Thieme, “Pairwise interaction tensor factorization for personalized tag recommendation,” in Proceedings of the third ACM International Conference on Web Search and Data Mining. ACM, 2010, pp. 81–90.
  • [76] S. Rendle, “Scaling factorization machines to relational data,” in Proceedings of the 39th International Conference on Very Large Data Bases. Trento, Italy: VLDB Endowment, 2013, pp. 337–348.
  • [77] R. Jenatton, N. L. Roux, A. Bordes, and G. R. Obozinski, “A latent factor model for highly multi-relational data,” in Advances in Neural Information Processing Systems 25. Curran Associates, Inc., 2012, pp. 3167–3175.
  • [78] P. Miettinen, “Boolean Tensor Factorizations,” in 2011 IEEE 11th International Conference on Data Mining, Dec. 2011, pp. 447–456.
  • [79] D. Erdos and P. Miettinen, “Discovering Facts with Boolean Tensor Tucker Decomposition,” in Proceedings of the 22nd ACM International Conference on Conference on Information & Knowledge Management. New York, NY, USA: ACM, 2013, pp. 1569–1572.
  • [80] X. Jiang, V. Tresp, Y. Huang, and M. Nickel, “Link Prediction in Multi-relational Graphs using Additive Models.” in Proceedings of International Workshop on Semantic Technologies meet Recommender Systems & Big Data at the ISWC, M. de Gemmis, T. D. Noia, P. Lops, T. Lukasiewicz, and G. Semeraro, Eds., vol. 919. CEUR Workshop Proceedings, 2012, pp. 1–12.
  • [81] S. Riedel, L. Yao, B. M. Marlin, and A. McCallum, “Relation Extraction with Matrix Factorization and Universal Schemas,” in Joint Human Language Technology Conference/Annual Meeting of the North American Chapter of the Association for Computational Linguistics (HLT-NAACL ’13), Jun. 2013.
  • [82] V. Tresp, Y. Huang, M. Bundschus, and A. Rettinger, “Materializing and querying learned knowledge,” Proceedings of of IRMLeS, vol. 2009, 2009.
  • [83] Y. Huang, V. Tresp, M. Nickel, A. Rettinger, and H.-P. Kriegel, “A scalable approach for statistical learning in semantic graphs,” Semantic Web journal SWj, 2013.
  • [84] P. Smolensky, “Tensor product variable binding and the representation of symbolic structures in connectionist systems,” Artificial intelligence, vol. 46, no. 1, pp. 159–216, 1990.
  • [85] G. S. Halford, W. H. Wilson, and S. Phillips, “Processing capacity defined by relational complexity: Implications for comparative, developmental, and cognitive psychology,” Behavioral and Brain Sciences, vol. 21, no. 06, pp. 803–831, 1998.
  • [86] T. Plate, “A common framework for distributed representation schemes for compositional structure,” Connectionist systems for knowledge representation and deduction, pp. 15–34, 1997.
  • [87] T. Mikolov, K. Chen, G. Corrado, and J. Dean, “Efficient Estimation of Word Representations in Vector Space,” in Proceedings of Workshop at ICLR, 2013.
  • [88] A. Bordes, J. Weston, R. Collobert, and Y. Bengio, “Learning Structured Embeddings of Knowledge Bases,” in Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence, San Francisco, USA, 2011.
  • [89] R. Socher, D. Chen, C. D. Manning, and A. Ng, “Reasoning With Neural Tensor Networks for Knowledge Base Completion,” in Advances in Neural Information Processing Systems 26. Curran Associates, Inc., 2013, pp. 926–934.
  • [90] A. Bordes, N. Usunier, A. Garcia-Duran, J. Weston, and O. Yakhnenko, “Translating Embeddings for Modeling Multi-relational Data,” in Advances in Neural Information Processing Systems 26. Curran Associates, Inc., 2013, pp. 2787–2795.
  • [91] B. Yang, W.-t. Yih, X. He, J. Gao, and L. Deng, “Embedding Entities and Relations for Learning and Inference in Knowledge Bases,” CoRR, vol. abs/1412.6575, 2014.
  • [92] P. D. Hoff, A. E. Raftery, and M. S. Handcock, “Latent space approaches to social network analysis,” Journal of the American Statistical Association, vol. 97, no. 460, pp. 1090–1098, 2002.
  • [93] L. Lü and T. Zhou, “Link prediction in complex networks: A survey,” Physica A: Statistical Mechanics and its Applications, vol. 390, no. 6, pp. 1150–1170, Mar. 2011.
  • [94] L. A. Adamic and E. Adar, “Friends and neighbors on the Web,” Social Networks, vol. 25, no. 3, pp. 211–230, 2003.
  • [95] A.-L. Barabási and R. Albert, “Emergence of Scaling in Random Networks,” Science, vol. 286, no. 5439, pp. 509–512, 1999.
  • [96] L. Katz, “A new status index derived from sociometric analysis,” Psychometrika, vol. 18, no. 1, pp. 39–43, 1953.
  • [97] E. A. Leicht, P. Holme, and M. E. Newman, “Vertex similarity in networks,” Physical Review E, vol. 73, no. 2, p. 026120, 2006.
  • [98] S. Brin and L. Page, “The anatomy of a large-scale hypertextual Web search engine,” Computer networks and ISDN systems, vol. 30, no. 1, pp. 107–117, 1998.
  • [99] W. Liu and L. Lü, “Link prediction based on local random walk,” EPL (Europhysics Letters), vol. 89, no. 5, p. 58007, 2010.
  • [100] N. Lao and W. W. Cohen, “Relational retrieval using a combination of path-constrained random walks,” Machine learning, vol. 81, no. 1, pp. 53–67, 2010.
  • [101] N. Lao, T. Mitchell, and W. W. Cohen, “Random walk inference and learning in a large scale knowledge base,” in Proceedings of the Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2011, pp. 529–539.
  • [102] J. R. Quinlan, “Learning logical definitions from relations,” Machine Learning, vol. 5, pp. 239–266, 1990.
  • [103] M. Nickel, X. Jiang, and V. Tresp, “Reducing the Rank in Relational Factorization Models by Including Observable Patterns,” in Advances in Neural Information Processing Systems 27. Curran Associates, Inc., 2014, pp. 1179–1187.
  • [104] Y. Koren, “Factorization Meets the Neighborhood: A Multifaceted Collaborative Filtering Model,” in Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2008, pp. 426–434.
  • [105] S. Rendle, “Factorization machines with libFM,” ACM Transactions on Intelligent Systems and Technology (TIST), vol. 3, no. 3, p. 57, 2012.
  • [106] D. H. Wolpert, “Stacked generalization,” Neural networks, vol. 5, no. 2, pp. 241–259, 1992.
  • [107] M. Richardson and P. Domingos, “Markov logic networks,” Machine Learning, vol. 62, no. 1, pp. 107–136, 2006.
  • [108] S. Jiang, D. Lowd, and D. Dou, “Learning to Refine an Automatically Extracted Knowledge Base Using Markov Logic,” 2013 IEEE 13th International Conference on Data Mining, pp. 912–917, 2012.
  • [109] C. Zhang and C. Ré, “Towards high-throughput Gibbs sampling at scale: A study across storage managers,” in Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data. ACM, 2013, pp. 397–408.
  • [110] A. Kimmig, S. H. Bach, M. Broecheler, B. Huang, and L. Getoor, “A Short Introduction to Probabilistic Soft Logic,” in NIPS Workshop on Probabilistic Programming: Foundations and Applications, 2012.
  • [111] J. Pujara, H. Miao, L. Getoor, and W. Cohen, “Knowledge graph identification,” in The Semantic Web–ISWC 2013. Springer, 2013, pp. 542–557.
  • [112] J. Neville and D. Jensen, “Relational dependency networks,” The Journal of Machine Learning Research, vol. 8, pp. 637–652, May 2007.
  • [113] D. Krompaß, X. Jiang, M. Nickel, and V. Tresp, “Probabilistic Latent-Factor Database Models,” in Proceedings of the 1st Workshop on Linked Data for Knowledge Discovery co-located with European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2014), 2014.
  • [114] H. Ji, T. Cassidy, Q. Li, and S. Tamang, “Tackling Representation, Annotation and Classification Challenges for Temporal Knowledge Base Population,” Knowledge and Information Systems, pp. 1–36, Aug. 2013.
  • [115] D. L. McGuinness, F. Van Harmelen, and others, “OWL web ontology language overview,” W3C recommendation, vol. 10, no. 10, p. 2004, 2004.
  • [116] A. Hogan, A. Harth, A. Passant, S. Decker, and A. Polleres, “Weaving the pedantic web,” in 3rd International Workshop on Linked Data on the Web (LDOW2010), in conjunction with 19th International World Wide Web Conference. Raleigh, North Carolina, USA: CEUR Workshop Proceedings, 2010.
  • [117] H. Halpin, P. Hayes, J. McCusker, D. Mcguinness, and H. Thompson, “When owl: sameAs isn’t the same: An analysis of identity in linked data,” The Semantic Web–ISWC 2010, pp. 305–320, 2010.
  • [118] D. Krompaß, M. Nickel, and V. Tresp, “Querying Factorized Probabilistic Triple Databases,” in The Semantic Web–ISWC 2014. Springer, 2014, pp. 114– 129.
  • [119] D. Suciu, D. Olteanu, C. Re, and C. Koch, Probabilistic Databases. Morgan & Claypool, 2011.
  • [120] D. Z. Wang, E. Michelakis, M. Garofalakis, and J. M. Hellerstein, “BayesStore: managing large, uncertain data repositories with probabilistic graphical models,” Proceedings of the VLDB Endowment, vol. 1, no. 1, pp. 340–351, 2008.;


 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2015 AReviewofRelationalMachineLearnMaximilian Nickel
Kevin P. Murphy
Volker Tresp
Evgeniy Gabrilovich
A Review of Relational Machine Learning for Knowledge Graphs: From Multi-Relational Link Prediction to Automated Knowledge Graph Construction2015
AuthorMaximilian Nickel +, Kevin Murphy +, Volker Tresp + and Evgeniy Gabrilovich +
titleA Review of Relational Machine Learning for Knowledge Graphs: From Multi-Relational Link Prediction to Automated Knowledge Graph Construction +
year2015 +