Knowledge Graphs: Research Directions

In these lecture notes, we provide an overview of some of the high-level research directions and open questions relating to knowledge graphs. We discuss six high-level concepts relating to knowledge graphs: data models, queries, ontologies, rules, embeddings and graph neural networks. While traditionally these concepts have been explored by different communities in the context of graphs, more recent works have begun to look at how they relate to one another, and how they can be unified. In fact, at a more foundational level, we can find some surprising relations between the different concepts. The research questions we explore mostly involve combinations of these concepts.

[1]  Luciano Serafini,et al.  Contextualized knowledge repositories for the Semantic Web , 2012, J. Web Semant..

[2]  Simone Paolo Ponzetto,et al.  BabelNet: The automatic construction, evaluation and application of a wide-coverage multilingual semantic network , 2012, Artif. Intell..

[3]  Benny Kimelfeld,et al.  Flexible Caching in Trie Joins , 2016, EDBT.

[4]  Dániel Marx,et al.  Size Bounds and Query Plans for Relational Joins , 2013, SIAM J. Comput..

[5]  Jacopo Urbani,et al.  VLog: A Rule Engine for Knowledge Graphs , 2019, SEMWEB.

[6]  Marcelo Arenas,et al.  Foundations of Modern Query Languages for Graph Databases , 2016, ACM Comput. Surv..

[7]  Giorgio Orsi,et al.  Datalog and Its Extensions for Semantic Web Databases , 2012, Reasoning Web.

[8]  Bernd Neumayr,et al.  Knowledge Graph OLAP , 2020, Semantic Web.

[9]  Markus Krötzsch,et al.  Attributed Description Logics: Reasoning on Knowledge Graphs , 2018, IJCAI.

[10]  Atri Rudra,et al.  Join Processing for Graph Patterns: An Old Dog with New Tricks , 2015, GRADES@SIGMOD/PODS.

[11]  Wim Martens,et al.  An analytical study of large SPARQL query logs , 2017, VLDB 2017.

[12]  Juan L. Reutter,et al.  Recursion in SPARQL , 2015, SEMWEB.

[13]  Pablo Barceló,et al.  Querying graph databases , 2013, PODS '13.

[14]  Marcelo Arenas,et al.  Counting beyond a Yottabyte, or how SPARQL 1.1 property paths will prevent adoption of the standard , 2012, WWW.

[15]  Boris Motik,et al.  Computing Datalog Rewritings Beyond Horn Ontologies , 2013, IJCAI.

[16]  Natasha Noy,et al.  Industry-scale Knowledge Graphs: Lessons and Challenges , 2019, ACM Queue.

[17]  Ah Chung Tsoi,et al.  The Graph Neural Network Model , 2009, IEEE Transactions on Neural Networks.

[18]  Axel Polleres,et al.  Everything you always wanted to know about blank nodes , 2014, J. Web Semant..

[19]  Justin J. Miller,et al.  Graph Database Applications and Concepts with Neo4j , 2013 .

[20]  Albert Meroño-Peñuela,et al.  List.MID: A MIDI-Based Benchmark for Evaluating RDF Lists , 2019, SEMWEB.

[21]  Sebastian Rudolph,et al.  Query Answering in the Horn Fragments of the Description Logics SHOIQ and SROIQ , 2011, IJCAI.

[22]  Sören Auer,et al.  Linked SDMX Data: Path to high fidelity Statistical Linked Data , 2015, Semantic Web.

[23]  Antoine Zimmermann,et al.  The Unified Code for Units of Measure in RDF: cdt: ucum and other UCUM Datatypes , 2018, ESWC.

[24]  Daniel P. Miranker,et al.  OBDA: Query Rewriting or Materialization? In Practice, Both! , 2014, SEMWEB.

[25]  Paul T. Groth,et al.  The Semantic Web – ISWC 2014 , 2014, Lecture Notes in Computer Science.

[26]  Stefan Plantikow,et al.  Cypher: An Evolving Query Language for Property Graphs , 2018, SIGMOD Conference.

[27]  Kevin Duh,et al.  Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing , 2016, EMNLP 2016.

[28]  Marko A. Rodriguez,et al.  The Gremlin graph traversal machine and language (invited talk) , 2015, DBPL.

[29]  Kunle Olukotun,et al.  EmptyHeaded: A Relational Engine for Graph Processing , 2015, ACM Trans. Database Syst..

[30]  D. Nardi,et al.  An Introduction to Description Logic , 2017 .

[31]  Alexandra Poulovassilis,et al.  Efficient Ontological Query Answering by Rewriting into Graph Queries , 2019, FQAS.

[32]  Li Guo,et al.  Jointly Embedding Knowledge Graphs and Logical Rules , 2016, EMNLP.

[33]  Sebastian Rudolph,et al.  Schema-Agnostic Query Rewriting in SPARQL 1.1 , 2014, International Semantic Web Conference.

[34]  Thomas Demeester,et al.  Lifted Rule Injection for Relation Embeddings , 2016, EMNLP.

[35]  Pablo Barceló,et al.  Logical Expressiveness of Graph Neural Networks , 2019 .

[36]  Juan Sequeda,et al.  G-CORE: A Core for Future Graph Query Languages , 2017, SIGMOD Conference.

[37]  Diego Calvanese,et al.  The DL-Lite Family and Relations , 2009, J. Artif. Intell. Res..

[38]  Diego Calvanese,et al.  Rules and Ontology Based Data Access , 2014, RR.

[39]  Aidan Hogan,et al.  A Worst-Case Optimal Join Algorithm for SPARQL , 2019, SEMWEB.

[40]  Michel C. A. Klein,et al.  Concept drift and how to identify it , 2011, J. Web Semant..

[41]  Markus Krötzsch,et al.  Wikidata , 2014, Commun. ACM.

[42]  Carsten Lutz,et al.  Ontology-Based Data Access: A Study through Disjunctive Datalog, CSP, and MMSNP , 2014, ACM Trans. Database Syst..

[43]  Li Guo,et al.  Knowledge Base Completion Using Embeddings and Rules , 2015, IJCAI.

[44]  Boris Motik,et al.  The Complexity of Answering Conjunctive and Navigational Queries over OWL 2 EL Knowledge Bases , 2014, J. Artif. Intell. Res..

[45]  Magdalena Ortiz,et al.  Regular Path Queries in Lightweight Description Logics: Complexity and Algorithms , 2015, J. Artif. Intell. Res..

[46]  Gerhard Weikum,et al.  YAGO2: exploring and querying world knowledge in time, space, context, and many languages , 2011, WWW.

[47]  Umberto Straccia,et al.  A General Framework for Representing, Reasoning and Querying with Annotated Semantic Web Data , 2011, J. Web Semant..

[48]  Pablo Barceló Baeza Querying graph databases , 2013, PODS 2013.

[49]  Jens Lehmann,et al.  DBpedia - A large-scale, multilingual knowledge base extracted from Wikipedia , 2015, Semantic Web.

[50]  Gert Smolka,et al.  Attributive Concept Descriptions with Complements , 1991, Artif. Intell..

[51]  Steffen Staab,et al.  Knowledge graphs , 2020, Commun. ACM.

[52]  Sebastian Rudolph,et al.  Description Logic Reasoning with Decision Diagrams: Compiling SHIQ to Disjunctive Datalog , 2008, SEMWEB.

[53]  Evgeny Kharlamov,et al.  Rule Learning from Knowledge Graphs Guided by Embedding Models , 2018, SEMWEB.

[54]  Luigi Bellomarini,et al.  The Vadalog System: Datalog-based Reasoning for Knowledge Graphs , 2018, Proc. VLDB Endow..

[55]  Amit P. Sheth,et al.  Don't like RDF reification?: making statements about statements using singleton property , 2014, WWW.

[56]  Diego Calvanese,et al.  Ontology-Based Data Access: A Survey , 2018, IJCAI.

[57]  Christos H. Papadimitriou,et al.  The even-path problem for graphs and digraphs , 1984, Networks.

[58]  Sebastian Rudolph,et al.  Foundations of Description Logics , 2011, Reasoning Web.

[59]  Olaf Hartig,et al.  Foundations of RDF⋆ and SPARQL⋆ (An Alternative Approach to Statement-Level Metadata in RDF) , 2017, AMW.

[60]  Jure Leskovec,et al.  How Powerful are Graph Neural Networks? , 2018, ICLR.

[61]  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.

[62]  Peter H. Schmitt,et al.  A closer look at the semantic relationship between Datalog and description logics , 2015, Semantic Web.

[63]  Renzo Angles,et al.  The Property Graph Database Model , 2018, AMW.

[64]  Zhendong Mao,et al.  Knowledge Graph Embedding: A Survey of Approaches and Applications , 2017, IEEE Transactions on Knowledge and Data Engineering.

[65]  Peter F. Patel-Schneider,et al.  OWL 2 Web Ontology Language Primer (Second Edition) , 2012 .

[66]  Leonid Libkin Locality of Queries and Transformations , 2006, Electron. Notes Theor. Comput. Sci..

[67]  Aleksander Kuzmanovic Net Neutrality: Unexpected Solution to Blockchain Scaling , 2019, ACM Queue.

[68]  Enrico Motta,et al.  Ontology evolution: a process-centric survey , 2013, The Knowledge Engineering Review.