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CKR - Contextualized Knowledge Repository

Overview

The Contextualized Knowledge Repository (CKR) is a knowledge representation and reasoning framework that build on Semantic Web technologies to represent, store, query and reason with contextualized knowledge, i.e. knowledge that holds under specific circumstances or contexts. The CKR addresses an arising needs in the Semantic Web, where as large amounts of Linked Data are published on the Web, it is becoming apparent that the validity of published knowledge is not absolute, but often depends on time, location, topic, and other contextual attributes.

CKR features

  • intuitive context representation based on the context-as-a-box principle: contexts are treated as small and inter-related knowledge bases qualified along contextual dimensions such as time, space and topic;
  • context structuring along a broader-narrower hierarchy, automatically induced by the qualification of contexts along dimensions and by the structuring of dimensional values;
  • cross-context statements to relate knowledge in different contexts, based on 'qualified symbols';
  • contextual reasoning based on OWL 2 RL local reasoning inside contexts and knowledge propagation rules across contexts, with support of 'CKR closure' to precompute and materialize all inferrable stetaments;
  • SPARQL-based contextual queries, supporting the constraining and the extraction of knowledge from multiple contexts by extending SPARQL with a CONTEXT keyword;
  • well defined semantics rooted in established AI principia for contextualized knowledge representation;
  • standard-friendly as rooted in Semantic Web standards as RDF, OWL 2 and Named Graphs.

Implementations

CKR on SPRINGLES

Implementation of CKR defined as SPARQL-based forward rules over multiple RDF named graphs. The framework is implemented over an extension of Sesame called SPRINGLES (SParql-based Rule Inference over Named Graphs Layer Extending Sesame), which supports the specification and execution of inference over Sesame RDF repositories.

Download and demo (DL2013): https://dkm.fbk.eu/ckr-tourismdemo

Reference paper: DL2013

Related publications: DeRiVe15, EKAW14, ARCOE14

CKRew - CKR datalog rewriter

CKRew provides a datalog translation of OWL2-RL based CKR and supports reasoning with global defeasible axioms (or justifiable exceptions). CKRew is implemented as an extension of the DL to datalog rewriter DReW. and provides a command line utility for the translation of CKRs represented as RDF/TRIG files.

Webpage: http://ckrew.fbk.eu/

Reference paper: DL2014

Related publications: CILC14, ARCOE13

CKR Prototype

First implementation of the framework, developed in the context of the LiveMemories project. The prototype implements CKR for OWL2-RL/RDFS(S) data on top of the Sesame and OWLIM frameworks, and supports storing, reasoning and querying with contextual knowledge.

Prototype and evaluation page: https://dkm.fbk.eu/technologies/ckr-prototype 

Reference papers: K-CAP13JWS 2012ESWC12ESWC10

Related publications: CONTEXT13DL2012, DL2011, WoMO11, CIAO10SWAP10

Publications

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Credits

Projects:

  • Initial development of the CKR was supported by the LiveMemories project (Active Digital Memories of Collective Life) aimed at developing methods for automatically interpreting the content of unrelated digital fragments and multimedia data of different nature and provenance, and transforming them into "active memories".
  • CKR has been lately applied in the Toolisse project for the development of a unified service platform for digital tourism.
  • CKR research has been applied and was supported by the PlanetData European Network of Excellence.

CKR Team:

DKMers currently involved: Luciano SerafiniLoris Bozzato, Gaetano Calabrese

Past contributors: Mathew JosephMartin HomolaAndrei Tamilin

Copyright

Copyright © 2016 the Data & Knowledge Management (DKM) Unit @ Fondazione Bruno Kessler (FBK)

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