HAPE: A programmable big knowledge graph platform

Abstract Heaven Ape (HAPE) is an integrated big knowledge graph platform supporting the construction, management, and operation of large to massive scale knowledge graphs. Its current version described in this paper is a prototype, which consists of three parts: a big knowledge graph knowledge base, a knowledge graph browser on the client side, and a knowledge graph operating system on the server side. The platform is programmed in two high level scripting languages: JavaScript for programming the client side functions and Python for the server side functions. For making the programming more suitable for big knowledge processing and more friendly to knowledge programmers, we have developed two versions of knowledge scripting languages, namely K-script-c and K-script-s, for performing very high level knowledge programming of client resp. server side functions. HAPE borrows ideas from some well-known knowledge graph processing techniques and also invents some new ones as our creation. As an experiment, we transformed a major part of the DBpedia knowledge base and reconstructed it as a big knowledge graph. It works well in some application tests and provides acceptable efficiency.

[1]  Joan Feigenbaum,et al.  Secure Multiparty Computation of Approximations , 2001, ICALP.

[2]  Yuanzhuo Wang,et al.  OpenKN: An open knowledge computational engine for network big data , 2014, 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014).

[3]  M. White,et al.  Digital workplaces , 2012 .

[4]  Kevin Murphy.  From big data to big knowledge , 2013, CIKM.

[5]  Peng Zhang,et al.  XLore: A Large-scale English-Chinese Bilingual Knowledge Graph , 2013, SEMWEB.

[6]  Yuzhong Qu,et al.  Efficient Algorithms for Association Finding and Frequent Association Pattern Mining , 2016, SEMWEB.

[7]  David Ellsworth,et al.  Managing Big Data for Scientific Visualization , 2015 .

[8]  David Flanagan,et al.  JavaScript: The Definitive Guide , 1996 .

[9]  Andriy Nikolov,et al.  metaphactory: A platform for knowledge graph management , 2019, Semantic Web.

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

[11]  John Gantz,et al.  The Digital Universe in 2020: Big Data, Bigger Digital Shadows, and Biggest Growth in the Far East , 2012 .

[12]  Xindong Wu,et al.  Knowledge Engineering with Big Data , 2015, IEEE Intell. Syst..

[13]  Maria-Esther Vidal,et al.  Enterprise Knowledge Graphs: A Semantic Approach for Knowledge Management in the Next Generation of Enterprise Information Systems , 2017, ICEIS.

[14]  Wei Zhang,et al.  Knowledge vault: a web-scale approach to probabilistic knowledge fusion , 2014, KDD.

[15]  Marko A. Rodriguez,et al.  General-Purpose Computing on a Semantic Network Substrate , 2007, ArXiv.

[16]  Travis E. Oliphant,et al.  Python for Scientific Computing , 2007, Computing in Science & Engineering.

[17]  Sören Auer,et al.  Enterprise Knowledge Graphs: A Backbone of Linked Enterprise Data , 2016, 2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI).

[18]  David R. Karger,et al.  Haystack: A Platform for Authoring End User Semantic Web Applications , 2003, WWW.

[19]  Meikang Qiu,et al.  A Study on Big Knowledge and Its Engineering Issues , 2019, IEEE Transactions on Knowledge and Data Engineering.

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

[21]  Sholom M. Weiss,et al.  Predictive data mining - a practical guide , 1997 .

[22]  Jason Weston,et al.  Translating Embeddings for Modeling Multi-relational Data , 2013, NIPS.

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

[24]  Ramanathan V. Guha,et al.  Building large knowledge-based systems , 1989 .

[25]  Haixun Wang,et al.  Probase: a probabilistic taxonomy for text understanding , 2012, SIGMOD Conference.

[26]  David Makinson.  Review: Brian F. Chellas, Modal Logic. An Introduction , 1981 .