LOD2 Plenary Vienna 2012: WP3 - Knowledge Base Creation, Enrichment and Repair

LOD2 Plenary Vienna – 2012/03/21 – http://lod2.euCreating Knowledge out of Interlinked Data

LOD2 Presentation . 02.09.2010 . Page http://lod2.euAKSW, Universität Leipzig

Jens Lehmann

WP3: Knowledge Base Creation, Enrichment and Repair

Plenary Vienna – State-of-Play

LOD2 Plenary Vienna – 2012/03/21 – http://lod2.eu

WP3 High Level Objectives

Mutual Refinement Cycle (with optional Extraction phase)

Repair

InconsistencyModelling Problems

Refactoring

Enrichment

Definitions

Property-Axioms

Data Summary

Extraction

Structured Semi-structured

Un-structured

8 Tasks, 9 Partners, 14 Deliverables, 20+ tools→ lightweight integration via LOD2 stack


WP3 Task 3.1

● Provenance-Aware Extraction of Linked Data from Existing Structured Formats

● Partners: FUB, ULEI, OpenLink, Exalead

● Development and Support of RDB2RDF mapping standards (R2RML)

● Re-Use of existing tools/frameworks:● D2R (FUB)

● Triplify (ULEI)

● Virtuoso Sponger and RDF Views (OpenLink)

● New Tool: Sparqlify

● Deliverables: State-of-the Art Report (M6), D2R release (M20), Triplify release (M20)


✔ D3.1.1: state of the art in knowledge extraction from structured sources● 200+ tools collected at http://data.lod2.eu/2011/tools/

● http://en.wikipedia.org/wiki/Knowledge_extraction (2000 views/month)

✔ D3.1.2: D2R Server MetaData Extension (allows adding licencing and provenance output to D2R server)

● D3.1.3: Sparqlify:● 1-1 SPARQL-to-SQL-Rewriting

● DB-Planner has Full Control

● Easy to Configure

● Tested on LinkedGeoData

● Release in 1-2 months

WP3 Task 3.1 – Progress / Planned

D2R Architecture

http://data.lod2.eu/2011/tools/

http://en.wikipedia.org/wiki/Knowledge_extraction


• Provenance-Aware Extraction of Linked Data from Unstructured and Semi-Structured Sources (plain text, HTML, wikis, blogs)

• Partners: FUB, ULEI, OpenLink, Exalead, Zemanta, KAIST, UEP

• NLP techniques / text understanding

• Draws on existing tools:

• Stanford Parser, ASV toolkit, Ontos API (all external), Zemanta

• DBpedia (FUB, ULEI, OpenLink)

• Deliverables: NLP2RDF release (M8), DBpedia Live (M8), DBpedia Framework Extension (M27)

• Other: DBpedia Spotlight Release, DBpedia I18n committee founded

WP3 Task 3.2


WP3 Task 3.2 – NLP2RDF + NIF

• NLP Interchange Format (NIF) is an RDF/OWL-based format to combine and chain NLP tools

• NLP2RDF (http://nlp2rdf.org) is a project providing:

• Documentation and tutorials

• Reference implementations of NIF

• Collaboration and mailing lists

• Roadmap of NIF in LOD2:

• Integration of Zemanta API (Task 3.7)

• BoA – tool for automated hypernym discovery and entity classification to ad hoc classes, using Wikipedia and Wordnet

• Ex – tool for information extraction from heterogeneous web resources

• MultiLingual Extraction (Task 3.6)

http://nlp2rdf.org/


WP3 Task 3.2 – NLP2RDF + NIF


WP3 Task 3.2 – DBpedia Live Motivation

• Wikipedia 7th most popular website (according to alexa.com)

• Covers a variety of disciplines

• DBpedia (from FUB, ULEI, OpenLink):

☺ Extracts structured data from Wikipedia

☺ Interlinks with other knowledge bases

☺ Can answer complex queries

☺ Is used in many applications / companies

Θ Requires manual effort to create a release

Θ Data is often several months old

DBpedia Live Synchronisation with Wikipedia


WP3 Task 3.2 – DBpedia Live Architecture

• Works on live stream of updates provided by Wikipedia

• Handles live changes of ontology and mappings (explained later)

• Provides public endpoint at http://live.dbpedia.org/sparql and mirrors

http://live.dbpedia.org/sparql


• Knowledge Base Schema Enrichment

• Partners: ULEI

• Suggests OWL Schema Axioms to Knowlege Base Maintainers (Definitions, Super Classes, Disjointness, Domain, Range, …)

• Extends DL-Learner (ULEI) machine learning framework

• Tight coupling of Tasks 3.3 (Enrichment) and 3.4 (Repair):

• Both will be integrated in the ORE tool

• Iteration of Repair and Enrichment to improve quality

• Adapts existing approaches to work with very large Linked Data knowledge bases (incl. SPARQL support)

WP3 Task 3.3


WP3 Task 3.3: Learning Schema Axioms

Deliverables: D3.3.1 Enrichment Algorithms (M12), D3.3.2 Enrichment User Interfaces (M24), D3.3.3 Evaluation (M36)


• Knowledge Base Repair

• Partners: ULEI, NUIG

• Fix inconsistent knowledge bases, unsatisfiable classes, (some) modelling errors, (some) reasoning performance problems

• Draws on a lot of existing work in ontology debugging and extends it to knowledge bases in the LOD cloud

• Related to Task 4.3 (Linked Data Quality Assessment)

• Result: ORE tool (together with Task 3.3)

• Deliverables: Report on Modelling Errors/Problems (M6), 1st ORE Release (M28), 2nd ORE Release (M40)

WP3 Task 3.4


• ORE (ontology repair and enrichment) tool started:

• Code: http://code.google.com/p/ore

• General Information: http://ore-tool.net

• Web Prototype: http://web.ore-tool.net (preliminary)

• Included in LOD2 stack

✔ Deliverable 3.4.1 (State of the Art on Modelling Problems) completed:

• Comprehensive overview on modelling problems, syntactical and semantical errors

• One of the conclusions: many tools available but scalability still an issue

• ORE will focus on fragment extraction, incremental reasoning, high reuse of existing tools and libraries

• work on algorithms for supporting debugging SPARQL endpoints

WP3 Task 3.4 - Progress

http://code.google.com/p/ore

http://ore-tool.net/

http://web.ore-tool.net/


• Knowledge base repair/refactoring based on naming/content patterns

• Partners: UEP

• Started February 2012 as extension to T3.4 (Knowledge base repair from logical point of view)

• Long-term goal is to bring the outcomes of the state-of-the-art ontology patterns research to the LOD2 Stack

• Result: a component for ORE allowing to detect taxonomic naming → discussion in breakout session

• (Anti-)patterns and suggested repairs will be developed until M24

• long term, prominent linked data vocabularies will be analyzed and mapped on ontology (content) design patterns

• Will lead to improvement in ontology repair and enrichment (WP3) as well as in ontology matching & instance linking (WP4)

WP3 Task 3.4a


• Web Linkage Validator

• Partners: NUIG, Exalead

• companion tool for unsupervised interlinking of data on the Semantic Web

• Dataset owners or authors utilise the tool by submitting their data for internal and external linkage analysis

• analytics will be used to perform recommendations and suggestions for ways in which they may improve the linkage of their data, e.g. suggest to add further properties, more specific property values, better specify classes/properties

• Deliverables: Initial Release (M18), LOD2 Stack Component Release (M28)

WP3 Task 3.5


WP3 Task 3.5


• Multi-Lingual Provenance-Aware Linked Data Extraction

• Partners: IMP

• Information retrieval: find documents using appropriate keywords (e.g. search engines: Google, Yahoo!, Baidu, Bing, etc.)

• Functionality not supported: find documents using a natural language document instead of using keywords

• Possible applications: Patent search (patent attorneys); Case search (lawyers); Anamnesis search (physicians); Paper search (researchers)

• The corresponding NLP technique will enable:

• Processing of documents in multiple languages

• Extraction of a vocabulary of concepts (words, phrases) specific for each class of documents

• Representation of domain specific vocabularies and links to related documents (based on NIF format)

WP3 Task 3.6


• Re-Uses many LOD2 stack components

• NLP technique for the structured representation of natural language documents:

✔Representation of natural language documents in structured form (words, phrases, sentences, paragraphs, documents)

• Multi-lingual support based on UTF-8 format – ongoing activity

• Creation of domain specific vocabularies based on classified documents – not started yet

• Searching for similar documents based on domain specific concepts found in given document – not started yet

• Sorting found documents according to similarity – not started yet

• Deliverables: D3.6 Multi-Lingual Support for Linked Data Extraction (M30)

WP3 Task 3.6


• Web Scale Link and Text Mining

• Partners: ZEM

• Gathering shallow semantic data about new entities – new knowledge about popular topics (not yet curated in LOD)

• Contributes to WP3 by creating new LOD datasets

• Extraction of new entities from blogs worldwide

• Creation of lexicons for new entity types to be used in named entity extraction engines

• Integration of new LOD datasets in Zemanta recommendation engine

• Gain market advantage

• Improved recommendations for bloggers and Zemanta free API users

• Deliverables: D3.7.1 Shallow information extraction from blogs (M20), D3.7.2 Improved entity recommender engine (M36)

WP3 Task 3.7


Project: http://lod2.euOrganisation: http://uni-leipzig.de, http://aksw.org Presenter: http://jens-lehmann.org

Thanks for your attention!

http://lod2.eu/

http://uni-leipzig.de/

http://aksw.org/

http://jens-lehmann.org/

LOD2 Plenary Vienna 2012: WP3 - Knowledge Base Creation, Enrichment and Repair

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