Knowledge Graphs and Enterprise AI: The Promise of an Enabling Technology

Adopting a mature AI strategy is fundamental for modern knowledge companies to govern the proliferation of smart AI-driven applications and to coordinate them within coherent knowledge workflows. We propose knowledge graphs as the reference technology for the enterprise AI context, i.e., the complex of entities, properties and relationships that shape a business domain and constitute a common backbone for all AI-driven applications. We contribute and discuss principles to design software architectures for AI-driven applications based on knowledge graphs. We focus on the Vadalog system, a successful knowledge graph middleware from the University of Oxford and show knowledge graphs in action in a number of use cases from the financial domain.

[1]  Georg Gottlob,et al.  Complexity and expressive power of logic programming , 1997, Proceedings of Computational Complexity. Twelfth Annual IEEE Conference.

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

[3]  Shan Shan Huang,et al.  Datalog and emerging applications: an interactive tutorial , 2011, SIGMOD '11.

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

[5]  Craig Larman,et al.  Applying UML and Patterns: An Introduction to Object-Oriented Analysis and Design and Iterative Development (3rd Edition) , 1997 .

[6]  Emanuel Sallinger,et al.  Reasoning about Schema Mappings , 2013, Data Exchange, Information, and Streams.

[7]  Andrea Calì,et al.  Datalog+/-: A Family of Logical Knowledge Representation and Query Languages for New Applications , 2010, 2010 25th Annual IEEE Symposium on Logic in Computer Science.

[8]  Andrea Calì,et al.  A general Datalog-based framework for tractable query answering over ontologies , 2012, J. Web Semant..

[9]  Letizia Tanca,et al.  Logic Programming and Databases , 1990, Surveys in Computer Science.

[10]  Wolfram Wöß,et al.  Towards a Definition of Knowledge Graphs , 2016, SEMANTiCS.

[11]  C. Hölscher Editorial , 2014, Alzheimer's & Dementia.

[12]  Luigi Bellomarini,et al.  Swift Logic for Big Data and Knowledge Graphs - Overview of Requirements, Language, and System , 2017, SOFSEM.

[13]  Lise Getoor,et al.  Knowledge Graph Identification , 2013, SEMWEB.

[14]  Petr Hájek,et al.  Metamathematics of Fuzzy Logic , 1998, Trends in Logic.

[15]  Achim Rettinger,et al.  Linked data quality of DBpedia, Freebase, OpenCyc, Wikidata, and YAGO , 2017, Semantic Web.

[16]  Andrea Calì,et al.  Towards more expressive ontology languages: The query answering problem , 2012, Artif. Intell..

[17]  Matthew Richardson,et al.  Markov logic networks , 2006, Machine Learning.

[18]  Carlo Zaniolo,et al.  Big Data Analytics with Datalog Queries on Spark , 2016, SIGMOD Conference.

[19]  Heiko Paulheim,et al.  Knowledge graph refinement: A survey of approaches and evaluation methods , 2016, Semantic Web.

[20]  Emanuel Sallinger,et al.  Winner Determination in Huge Elections with MapReduce , 2017, AAAI.

[21]  Sebastian Rudolph,et al.  Walking the Complexity Lines for Generalized Guarded Existential Rules , 2011, IJCAI.

[22]  Tim Furche,et al.  Data Wrangling for Big Data: Towards a Lingua Franca for Data Wrangling , 2016, AMW.

[23]  Andrea Calì,et al.  A general datalog-based framework for tractable query answering over ontologies , 2009, SEBD.

[24]  Georg Gottlob,et al.  Expressive Languages for Querying the Semantic Web , 2018, TODS.

[25]  Andrea Calì,et al.  Taming the Infinite Chase: Query Answering under Expressive Relational Constraints , 2008, Description Logics.

[26]  Dan Suciu,et al.  Data on the Web: From Relations to Semistructured Data and XML , 1999 .

[27]  Luigi Bellomarini,et al.  Meta-Mappings for Schema Mapping Reuse , 2019, Proc. VLDB Endow..

[28]  Jean-François Baget,et al.  Walking the Decidability Line for Rules with Existential Variables , 2010, KR.

[29]  Divesh Srivastava,et al.  Answering Range Queries Under Local Differential Privacy , 2018, Proc. VLDB Endow..

[30]  Bernd Neumayr,et al.  The VADA Architecture for Cost-Effective Data Wrangling , 2017, SIGMOD Conference.

[31]  Georg Gottlob,et al.  Beyond SPARQL under OWL 2 QL Entailment Regime: Rules to the Rescue , 2015, IJCAI.

[32]  Christopher Ré,et al.  Probabilistic databases , 2011, SIGA.

[33]  Ruslan R. Fayzrakhmanov,et al.  OXPath-Based Data Acquisition for dblp , 2017, 2017 ACM/IEEE Joint Conference on Digital Libraries (JCDL).

[34]  Ronald Fagin,et al.  Data exchange: semantics and query answering , 2003, Theor. Comput. Sci..

[35]  Jennifer Widom,et al.  Database Systems: The Complete Book , 2001 .

[36]  Letizia Tanca,et al.  What you Always Wanted to Know About Datalog (And Never Dared to Ask) , 1989, IEEE Trans. Knowl. Data Eng..

[37]  Peter Sommerlad,et al.  Pattern-Oriented Software Architecture Volume 1: A System of Patterns , 1996 .

[38]  Emir Pasalic,et al.  Design and Implementation of the LogicBlox System , 2015, SIGMOD Conference.

[39]  Yi Zhang,et al.  Dataset Relationship Management , 2019, CIDR.