A Review of Relational Machine Learning for Knowledge Graphs
暂无分享,去创建一个
Evgeniy Gabrilovich | Volker Tresp | Kevin Murphy | Maximilian Nickel | Volker Tresp | K. Murphy | Maximilian Nickel | E. Gabrilovich
[1] Leo Katz,et al. A new status index derived from sociometric analysis , 1953 .
[2] H B NEWCOMBE,et al. Automatic linkage of vital records. , 1959, Science.
[3] H. B. Newcombe,et al. Computers can be used to extract "follow-up" statistics of families from files of routine records. , 1959 .
[4] Marvin Minsky,et al. A framework for representing knowledge , 1974 .
[5] Kathryn B. Laskey,et al. Stochastic blockmodels: First steps , 1983 .
[6] Douglas B. Lenat,et al. On the thresholds of knowledge , 1987, Proceedings of the International Workshop on Artificial Intelligence for Industrial Applications.
[7] Geoffrey E. Hinton. Tensor Product Variable Binding and the Representation of Symbolic Structures in Connectionist Systems , 1991 .
[8] S. Wasserman,et al. Building stochastic blockmodels , 1992 .
[9] George A. Miller,et al. WordNet: A Lexical Database for English , 1995, HLT.
[10] David H. Wolpert,et al. Stacked generalization , 1992, Neural Networks.
[11] Peter Szolovits,et al. What Is a Knowledge Representation? , 1993, AI Mag..
[12] Douglas B. Lenat,et al. CYC: a large-scale investment in knowledge infrastructure , 1995, CACM.
[13] T. Plate. A Common Framework for Distributed Representation Schemes for Compositional Structure , 1997 .
[14] Sergey Brin,et al. The Anatomy of a Large-Scale Hypertextual Web Search Engine , 1998, Comput. Networks.
[15] S. Phillips,et al. Processing capacity defined by relational complexity: implications for comparative, developmental, and cognitive psychology. , 1998, The Behavioral and brain sciences.
[16] Albert,et al. Emergence of scaling in random networks , 1999, Science.
[17] John F. Sowa,et al. Knowledge Representation and Reasoning , 2000 .
[18] P. Bartlett,et al. Probabilities for SV Machines , 2000 .
[19] James A. Hendler,et al. The Semantic Web published as an article in Scientific American , 2001 .
[20] Erhard Rahm,et al. A survey of approaches to automatic schema matching , 2001, The VLDB Journal.
[21] M. Newman,et al. The structure of scientific collaboration networks. , 2000, Proceedings of the National Academy of Sciences of the United States of America.
[22] Craig A. Knoblock,et al. Learning object identification rules for information integration , 2001, Inf. Syst..
[23] John F. Sowa,et al. Knowledge representation: logical, philosophical, and computational foundations , 2000 .
[24] Peter D. Hoff,et al. Latent Space Approaches to Social Network Analysis , 2002 .
[25] Jennifer Neville,et al. Linkage and Autocorrelation Cause Feature Selection Bias in Relational Learning , 2002, ICML.
[26] Jon Kleinberg,et al. The link prediction problem for social networks , 2003, CIKM '03.
[27] Ben Taskar,et al. Link Prediction in Relational Data , 2003, NIPS.
[28] Lada A. Adamic,et al. Friends and neighbors on the Web , 2003, Soc. Networks.
[29] Olivier Bodenreider,et al. The Unified Medical Language System (UMLS): integrating biomedical terminology , 2004, Nucleic Acids Res..
[30] Deborah L. McGuinness,et al. OWL Web ontology language overview , 2004 .
[31] Tamara G. Kolda,et al. Higher-order Web link analysis using multilinear algebra , 2005, Fifth IEEE International Conference on Data Mining (ICDM'05).
[32] Lise Getoor,et al. Link mining: a survey , 2005, SKDD.
[33] J. Ross Quinlan,et al. Learning logical definitions from relations , 1990, Machine Learning.
[34] Andrew McCallum,et al. Joint deduplication of multiple record types in relational data , 2005, CIKM '05.
[35] Brett W. Bader,et al. The TOPHITS Model for Higher-Order Web Link Analysis∗ , 2006 .
[36] Nicola Fanizzi,et al. Reasoning by Analogy in Description Logics Through Instance-based Learning , 2006, SWAP.
[37] Pedro M. Domingos,et al. Entity Resolution with Markov Logic , 2006, Sixth International Conference on Data Mining (ICDM'06).
[38] M. Newman,et al. Vertex similarity in networks. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.
[39] Mark Goadrich,et al. The relationship between Precision-Recall and ROC curves , 2006, ICML.
[40] Pedro M. Domingos,et al. Sound and Efficient Inference with Probabilistic and Deterministic Dependencies , 2006, AAAI.
[41] Thomas L. Griffiths,et al. Learning Systems of Concepts with an Infinite Relational Model , 2006, AAAI.
[42] Matthew Richardson,et al. Markov logic networks , 2006, Machine Learning.
[43] Hans-Peter Kriegel,et al. Infinite Hidden Relational Models , 2006, UAI.
[44] Gerhard Weikum,et al. WWW 2007 / Track: Semantic Web Session: Ontologies ABSTRACT YAGO: A Core of Semantic Knowledge , 2022 .
[45] Jens Lehmann,et al. DBpedia: A Nucleus for a Web of Open Data , 2007, ISWC/ASWC.
[46] Tommi S. Jaakkola,et al. Fixing Max-Product: Convergent Message Passing Algorithms for MAP LP-Relaxations , 2007, NIPS.
[47] Jennifer Neville,et al. Relational Dependency Networks , 2007, J. Mach. Learn. Res..
[48] Andrew McCallum,et al. Introduction to Statistical Relational Learning , 2007 .
[49] Lise Getoor,et al. Collective entity resolution in relational data , 2007, TKDD.
[50] Peter D. Hoff,et al. Modeling homophily and stochastic equivalence in symmetric relational data , 2007, NIPS.
[51] Jon M. Kleinberg,et al. The link-prediction problem for social networks , 2007, J. Assoc. Inf. Sci. Technol..
[52] Pedro M. Domingos,et al. Statistical predicate invention , 2007, ICML '07.
[53] Robert Tibshirani,et al. Margin Trees for High-dimensional Classification , 2007, J. Mach. Learn. Res..
[54] Praveen Paritosh,et al. Freebase: a collaboratively created graph database for structuring human knowledge , 2008, SIGMOD Conference.
[55] Daisy Zhe Wang,et al. BayesStore: managing large, uncertain data repositories with probabilistic graphical models , 2008, Proc. VLDB Endow..
[56] Luc De Raedt,et al. Logical and relational learning , 2008, Cognitive Technologies.
[57] Nicole Tourigny,et al. Bio2RDF: Towards a mashup to build bioinformatics knowledge systems , 2008, J. Biomed. Informatics.
[58] Yehuda Koren,et al. Factorization meets the neighborhood: a multifaceted collaborative filtering model , 2008, KDD.
[59] Steffen Staab,et al. TripleRank: Ranking Semantic Web Data by Tensor Decomposition , 2009, SEMWEB.
[60] Dan Suciu,et al. Probabilistic databases , 2011, SIGA.
[61] Stephen Muggleton,et al. Inverse entailment and progol , 1995, New Generation Computing.
[62] Joshua B. Tenenbaum,et al. Modelling Relational Data using Bayesian Clustered Tensor Factorization , 2009, NIPS.
[63] Alan Ruttenberg,et al. Life sciences on the Semantic Web: the Neurocommons and beyond , 2009, Briefings Bioinform..
[64] Tamara G. Kolda,et al. Tensor Decompositions and Applications , 2009, SIAM Rev..
[65] Santo Fortunato,et al. Community detection in graphs , 2009, ArXiv.
[66] Tim Berners-Lee,et al. Linked Data - The Story So Far , 2009, Int. J. Semantic Web Inf. Syst..
[67] Jens Lehmann,et al. DL-Learner: Learning Concepts in Description Logics , 2009, J. Mach. Learn. Res..
[68] Nir Friedman,et al. Probabilistic Graphical Models - Principles and Techniques , 2009 .
[69] Yehuda Koren,et al. Matrix Factorization Techniques for Recommender Systems , 2009, Computer.
[70] Ed H. Chi,et al. The singularity is not near: slowing growth of Wikipedia , 2009, Int. Sym. Wikis.
[71] Elena Console,et al. Data Fusion , 2009, Encyclopedia of Database Systems.
[72] Achim Rettinger,et al. Materializing and Querying Learned Knowledge , 2009 .
[73] Ni Lao,et al. Relational retrieval using a combination of path-constrained random walks , 2010, Machine Learning.
[74] Francesca A. Lisi,et al. Inductive Logic Programming in Databases: From Datalog to $\mathcal{DL}+log}^{\neg\vee}$ , 2010, Theory and Practice of Logic Programming.
[75] Jennifer Chu-Carroll,et al. Building Watson: An Overview of the DeepQA Project , 2010, AI Mag..
[76] Deborah L. McGuinness,et al. When owl: sameAs Isn't the Same: An Analysis of Identity in Linked Data , 2010, SEMWEB.
[77] Linyuan Lu,et al. Link prediction based on local random walk , 2010, 1001.2467.
[78] Aditya Kalyanpur,et al. PRISMATIC: Inducing Knowledge from a Large Scale Lexicalized Relation Resource , 2010, HLT-NAACL 2010.
[79] Linyuan Lu,et al. Link Prediction in Complex Networks: A Survey , 2010, ArXiv.
[80] Andreas Harth,et al. Weaving the Pedantic Web , 2010, LDOW.
[81] Lars Schmidt-Thieme,et al. Pairwise interaction tensor factorization for personalized tag recommendation , 2010, WSDM '10.
[82] Gerhard Weikum,et al. From information to knowledge: harvesting entities and relationships from web sources , 2010, PODS '10.
[83] Estevam R. Hruschka,et al. Toward an Architecture for Never-Ending Language Learning , 2010, AAAI.
[84] Léon Bottou,et al. Large-Scale Machine Learning with Stochastic Gradient Descent , 2010, COMPSTAT.
[85] Pauli Miettinen,et al. Boolean Tensor Factorizations , 2011, 2011 IEEE 11th International Conference on Data Mining.
[86] Jason Weston,et al. Learning Structured Embeddings of Knowledge Bases , 2011, AAAI.
[87] Tom M. Mitchell,et al. Random Walk Inference and Learning in A Large Scale Knowledge Base , 2011, EMNLP.
[88] Oren Etzioni,et al. Open Information Extraction: The Second Generation , 2011, IJCAI.
[89] Gerhard Weikum,et al. Scalable knowledge harvesting with high precision and high recall , 2011, WSDM '11.
[90] Maximilian Nickel. Learning Taxonomies from Multi-Relational Data via Hierarchical Link-Based Clustering , 2011 .
[91] Oren Etzioni,et al. Identifying Relations for Open Information Extraction , 2011, EMNLP.
[92] Hans-Peter Kriegel,et al. A Three-Way Model for Collective Learning on Multi-Relational Data , 2011, ICML.
[93] Huajun Chen,et al. The Semantic Web , 2011, Lecture Notes in Computer Science.
[94] Yizhou Sun,et al. Mining Heterogeneous Information Networks: Principles and Methodologies , 2012, Mining Heterogeneous Information Networks: Principles and Methodologies.
[95] Kevin P. Murphy,et al. Machine learning - a probabilistic perspective , 2012, Adaptive computation and machine learning series.
[96] Lars Schmidt-Thieme,et al. Predicting RDF triples in incomplete knowledge bases with tensor factorization , 2012, SAC '12.
[97] Divesh Srivastava,et al. Truth Finding on the Deep Web: Is the Problem Solved? , 2012, Proc. VLDB Endow..
[98] Nicolas Le Roux,et al. A latent factor model for highly multi-relational data , 2012, NIPS.
[99] Hans-Peter Kriegel,et al. Factorizing YAGO: scalable machine learning for linked data , 2012, WWW.
[100] Lise Getoor,et al. A short introduction to probabilistic soft logic , 2012, NIPS 2012.
[101] Yizhou Sun,et al. Mining heterogeneous information networks , 2012 .
[102] Oren Etzioni,et al. Open Language Learning for Information Extraction , 2012, EMNLP.
[103] Dejing Dou,et al. Learning to Refine an Automatically Extracted Knowledge Base Using Markov Logic , 2012, 2012 IEEE 12th International Conference on Data Mining.
[104] Gerhard Weikum,et al. PATTY: A Taxonomy of Relational Patterns with Semantic Types , 2012, EMNLP.
[105] Achim Rettinger,et al. Mining the Semantic Web , 2012, Data Mining and Knowledge Discovery.
[106] Volker Tresp,et al. Mining the Semantic Web Statistical Learning for Next Generation Knowledge Bases , 2012 .
[107] Xueyan Jiang,et al. Link Prediction in Multi-relational Graphs using Additive Models , 2012, SeRSy.
[108] Steffen Rendle,et al. Factorization Machines with libFM , 2012, TIST.
[109] Hector Garcia-Molina,et al. Joint Entity Resolution , 2012, 2012 IEEE 28th International Conference on Data Engineering.
[110] Christopher Ré,et al. Elementary: Large-Scale Knowledge-Base Construction via Machine Learning and Statistical Inference , 2012, Int. J. Semantic Web Inf. Syst..
[111] Stephan Bloehdorn,et al. Graph Kernels for RDF Data , 2012, ESWC.
[112] Heng Ji,et al. Tackling representation, annotation and classification challenges for temporal knowledge base population , 2014, Knowledge and Information Systems.
[113] Trevor Hastie,et al. An Introduction to Statistical Learning , 2013, Springer Texts in Statistics.
[114] Jason Weston,et al. Translating Embeddings for Modeling Multi-relational Data , 2013, NIPS.
[115] Jeffrey Dean,et al. Efficient Estimation of Word Representations in Vector Space , 2013, ICLR.
[116] V. S. Costa,et al. Inductive Logic Programming , 2014, Lecture Notes in Computer Science.
[117] Volker Tresp,et al. Tensor Factorization for Multi-relational Learning , 2013, ECML/PKDD.
[118] Gerhard Weikum,et al. YAGO2: A Spatially and Temporally Enhanced Knowledge Base from Wikipedia: Extended Abstract , 2013, IJCAI.
[119] Danqi Chen,et al. Reasoning With Neural Tensor Networks for Knowledge Base Completion , 2013, NIPS.
[120] Andrew McCallum,et al. Relation Extraction with Matrix Factorization and Universal Schemas , 2013, NAACL.
[121] Christopher D. Manning,et al. Philosophers are Mortal: Inferring the Truth of Unseen Facts , 2013, CoNLL.
[122] Maximilian Nickel,et al. Tensor factorization for relational learning , 2013 .
[123] Christopher Ré,et al. Towards high-throughput gibbs sampling at scale: a study across storage managers , 2013, SIGMOD '13.
[124] Fabian M. Suchanek,et al. AMIE: association rule mining under incomplete evidence in ontological knowledge bases , 2013, WWW.
[125] Volker Tresp,et al. Logistic Tensor Factorization for Multi-Relational Data , 2013, ArXiv.
[126] Pauli Miettinen,et al. Discovering facts with boolean tensor tucker decomposition , 2013, CIKM.
[127] Lise Getoor,et al. Knowledge Graph Identification , 2013, SEMWEB.
[128] Steffen Rendle. Scaling Factorization Machines to Relational Data , 2013, Proc. VLDB Endow..
[129] Fabian M. Suchanek,et al. Inside YAGO2s: a transparent information extraction architecture , 2013, WWW '13 Companion.
[130] Kai-Wei Chang,et al. Typed Tensor Decomposition of Knowledge Bases for Relation Extraction , 2014, EMNLP.
[131] Volker Tresp,et al. Large-scale factorization of type-constrained multi-relational data , 2014, 2014 International Conference on Data Science and Advanced Analytics (DSAA).
[132] Volker Tresp,et al. Querying Factorized Probabilistic Triple Databases , 2014, SEMWEB.
[133] Nicola Fanizzi,et al. Learning to Propagate Knowledge in Web Ontologies , 2014, URSW.
[134] Hans-Peter Kriegel,et al. A scalable approach for statistical learning in semantic graphs , 2014, Semantic Web.
[135] Wei Zhang,et al. From Data Fusion to Knowledge Fusion , 2014, Proc. VLDB Endow..
[136] Xueyan Jiang,et al. Reducing the Rank in Relational Factorization Models by Including Observable Patterns , 2014, NIPS.
[137] John A. Barnden,et al. Semantic Networks , 1998, Encyclopedia of Social Network Analysis and Mining.
[138] Xueyan Jiang,et al. Probabilistic Latent-Factor Database Models , 2014, LD4KD.
[139] Wei Zhang,et al. Knowledge vault: a web-scale approach to probabilistic knowledge fusion , 2014, KDD.
[140] Rahul Gupta,et al. Knowledge base completion via search-based question answering , 2014, WWW.
[141] Markus Krötzsch,et al. Wikidata , 2014, Commun. ACM.
[142] Wei Zhang,et al. Knowledge-Based Trust: Estimating the Trustworthiness of Web Sources , 2015, Proc. VLDB Endow..
[143] Fabian M. Suchanek,et al. Fast rule mining in ontological knowledge bases with AMIE+\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$+$$\end{docu , 2015, The VLDB Journal.
[144] Jianfeng Gao,et al. Embedding Entities and Relations for Learning and Inference in Knowledge Bases , 2014, ICLR.
[145] Daniel M. Roy,et al. Bayesian Models of Graphs, Arrays and Other Exchangeable Random Structures , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[146] Danqi Chen,et al. Observed versus latent features for knowledge base and text inference , 2015, CVSC.
[147] Lise Getoor,et al. Using Semantics and Statistics to Turn Data into Knowledge , 2015, AI Mag..
[148] Hans Uszkoreit,et al. Improvement of n-ary Relation Extraction by Adding Lexical Semantics to Distant-Supervision Rule Learning , 2015, ICAART.
[149] Stephen H. Bach. Hinge-Loss Markov Random Fields and Probabilistic Soft Logic: A Scalable Approach to Structured Prediction , 2015, J. Mach. Learn. Res..
[150] Nada Lavrač,et al. Relational Data Mining , 2018, Encyclopedia of Social Network Analysis and Mining. 2nd Ed..