The Linked Data Lifecycle

The Linked Data Life-Cycle

Jens Lehmann Lorenz Bühmann

contributors:Quan Nguyen Sören Auer Richard Cyganiak Daniel GerberSebastian Hellmann Anja Jentzsch Dimitris Kontokostas Axel NgongaClaus Stadler Christina Unger

2013-08-23

Lehmann, Bühmann (Univ. Leipzig) The Linked Data Life-Cycle 2013-08-23 1 / 252

Outline

1 Introduction to Linked Data

2 Linked Dataset Example: DBpedia

3 Linked Data Life-Cycle Overview

4 Knowledge Extraction

5 Data Integration / Linking

6 Enrichment

7 Repair

8 Knowledge Base Exploration / Querying

Interlinking/ Fusing

Classifi-cation/

Enrichment

Quality Analysis

Evolution / Repair

Search/ Browsing/

Exploration

Extraction

Storage/ Querying

Manual revision/

Authoring

Linked DataLifecycle


Outline






6 Enrichment

7 Repair



Classifi-cation/

Enrichment

Quality Analysis

Evolution / Repair

Search/ Browsing/

Exploration

Extraction

Storage/ Querying

Manual revision/

Authoring



The Linked Data Principles

The term Linked Data refers to a set of best practices for publishing andinterlinking structured data on the Web.

Linked Data principles:

1 Use URIs as names for things.

2 Use HTTP URIs, so that people can look up those names.

3 When someone looks up a URI, provide useful information, using thestandards (RDF, SPARQL).

4 Include links to other URIs, so that they can discover more things.


LOD Cloud


Linked Data Principles Detailed: 1 + 2

1 URI references to identify not just Web documents and digitalcontent, but also real world objects and abstract concepts

tangible things: people, placesabstract things: relationship type of knowing somebody

2 HTTP URIs enable re-use of Web architecture Linked Data givesemphasis to the Web in Semantic Web

Resource dereferencingRe-use of standard tools for security, load-balancing etc.


Principles Detailed: 3 Content Negotiation

Humans and machines should be able to retrieve appropiraterepresentations of resources: HTML for humans, RDF formachines

Achievable using an HTTP mechanism called content negotiation

Basic idea: HTTP client sends HTTP headers with each request toindicate what kinds of documents they prefer

Servers can inspect headers and select appropriate response

Two strategies:

303 URIsHash URIs

Both ensure that objects and the documents that describe them arenot confused + humans and machines can retrieve appropriaterepresentations


303 URIs

303 Redirect: instead of sending the object itself over the network,the server responds to the client with the HTTP response code 303See Other and the URI of a Web document which describes thereal-world object

Second step: client dereferences new URI and gets a Web documentdescribing the real-world object


Hash URIs

Hash URI strategy builds on characteristic that URIs may contain aspecial part (fragment identier) separated from their base part by ahash symbol (#)

HTTP protocol requires the fragment part to be stripped o beforerequesting the URI from the server

→ a URI that includes a hash cannot be retrieved directly andtherefore does not necessarily identify a Web document


Hash versus 303

Hash Uris

(+) Reduced number of necessary HTTP round-trips → reduces accesslatency(-) Descriptions of all resources sharing the same non-fragment URIpart are always returned to the client together → can lead to largeamounts of data being unnecessarily transmitted to the client

303 Uris

(+) Flexible because the redirection target can be conguredseparately for each resource (usually points to a single document foreach resource, but could also summarise several resources)(-) Requires two HTTP requests to retrieve a single description of areal-world object


Principles Detailed: 4 Links

If an RDF triple connects URIs in dierent namespaces/datasets, is iscalled a link (no unique syntactical denition of link exists)

Basic idea of Linked Data: apply the general hyperlink-basedarchitecture of the World Wide Web to the task of sharing structureddata on global scale

Research challenge: ecient creation of links with high precision andrecall


Why Linked Data?

Problem: Try to search for these things on the current Web:

Apartments near German-Russian bilingual childcare in Leipzig.

ERP service providers with oces in Vienna and London.

Researchers working on multimedia topics in Eastern Europe.

Information is available on the Web, but opaque to current Websearch.


Why Linked Data?

Problem: Try to search for these things on the current Web:

Apartments near German-Russian bilingual childcare in Leipzig.

ERP service providers with oces in Vienna and London.

Researchers working on multimedia topics in Eastern Europe.

Information is available on the Web, but opaque to current Websearch.Solution: complement text on Web pages with structured linked open data& intelligently combine/integrate such structured information fromdierent sources:


How to get there?


Tim Berners-Lee's 5-star plan

Tim Berners-Lee's 5-star plan for an open web of data

F Make data available on the Web under an open license

F F Make it available as structured data

F F F Use a non-proprietary format

F F F F Use URIs to identify things

F F F F F Link your data to other people's data to provide context


The 0th star

Data catalog with good metadataMake your data ndable


F Data on the Web, Open License



Open vs. Closed:

Data used to be closed by default

In the future, it may be open by default.



Publishers: sharing data to make it more visible



E-Commerce: Data sharing for increasing trac



Community: Collaboratively created databases


Good reasons against opening data

Privacy

Competitive advantage

Producing data and charging for it as business model

Can't get license from upstream


F F Structured Data

Enabling re-use:

Delivering data to end users in dierent forms

Combining data with other data

3rd party analysis of data


F F Structured Data

Formats:

Good for re-use / Structured: MS Excel, CSV, XML, JSON, Microdata

Not so good for re-use: Pure websites, MS Word

Bad for re-use: PDF

Really bad for re-use: Only charts/maps without numbers


F F F Non-Proprietary Formats

Specialist tools often have specialist formats

Few people have the toolsExpensiveDicult to re-use(Geospatial tools, statistics packages, etc.)

Non-proprietary:

CSV (dead simple)XMLJSONRDF (good for 4+5 stars)


F F F F URIs as Identiers



URI-Design: prefer stable, implementation independent URIs



Turning local identiers into URIsWhy?

Make them globally unique

Clarify auhority

Make them resolvable

Make them linkable


F F F F F Links to Other Data

Hyperlinks are the soul of the Web. The Web of Data is no dierent.


Summary

Linked Data Principles:

1 Use URIs to name things (not only documents, but also people,locations, concepts, etc.)

2 To enable agents (human users and machine agents alike) to look upthose names, use HTTP URIs

3 When someone looks up a URI, provide useful information(structured data in RDF, SPARQL).

4 Include links to other URIs allowing agents to discover more things

5-Star-Data:

Five-star plan for realising an emerging web of data, dataset bydataset

2 stars: re-usable data

3 stars: open standards

4+5 stars: connect data silos


Outline






6 Enrichment

7 Repair



Classifi-cation/

Enrichment

Quality Analysis

Evolution / Repair

Search/ Browsing/

Exploration

Extraction

Storage/ Querying

Manual revision/

Authoring



DBpedia

Community eort to extract structured information from Wikipediaand to make this information available on the Web

Allows to ask sophisticated queries against Wikipedia, and to linkother data sets on the Web to Wikipedia data

Semi-structured Wiki markup → structured information


Wikipedia Limitations

Simple Questions hard to answer with Wikipedia:

What have Innsbruck and Leipzig in common?

Who are mayors of central European towns elevated more than1000m?

Which movies are starring both Brad Pitt and Angelina Jolie?

All soccer players, who played as goalkeeper for a club that has astadium with more than 40.000 seats and who are born in a countrywith more than 10 million inhabitants


Structure in Wikipedia

Title

Abstract

Infoboxes

Geo-coordinates

Categories

Images

Links

other language versionsother Wikipedia pagesTo the WebRedirectsDisambiguation

...


DBpedia Information Extraction Framework

DBpedia Information Extraction Framework (DIEF)

Started in 2007

Hosted on Sourceforge and Github

Initially written in PHP but fully re-written Written in Scala and Java

Around 40 Contributors

See https://www.ohloh.net/p/dbpedia for detailed overview

Can potentially be adapted to other MediaWikis

Currently Wiktionary http://wiktionary.dbpedia.org


https://www.ohloh.net/p/dbpedia

http://wiktionary.dbpedia.org

DIEF - Overview


DIEF - Raw Infobox Extractor

WikiText syntaxInfobox Korean settlement|title = Busan Metropolitan City...|area_km2 = 763.46|pop = 3635389|region = [[Yeongnam]]RDF serializationdbp:Busan dbp:title "Busan Metropolitan City"dbp:Busan dbp:area_km2 "763.46"^xsd:oatdbp:Busan dbp:pop "3635389"^xsd:int

dbp:Busan dbp:region dbp:Yeongnam


DIEF - Raw Infobox Extractor/Diversity


DIEF - Raw Infobox extractor/Diversity


DIEF - Mapping-Based Infobox Extractor

Cleaner data:

Combine what belongs together (birth_place, birthplace)

Separate what is dierent (bornIn, birthplace)

Correct handling of datatypes

Mappings Wiki:

http://mappings.dbpedia.org

Everybody can contribute to new mappings or improve existing ones

≈ 170 editors


http://mappings.dbpedia.org

DIEF - Mapping-Based Infobox Extractor


URI/IRI schemes

http://lang.dbpedia.org is the main domain

For every article there exists a DBpedia resource in the form:http://lang.dbpedia.org/resource/ArticleName

Properties from the raw infobox extractor use thehttp://lang.dbpedia.org/property/namespace

Ontology is global for all languages and underhttp://dbpedia.org/ontology/namespace

Note: that for English language no language code is used

http://dbpedia.org as main domain

http://dbpedia.org/resource/title for articles

http://dbpedia.org/property/title for properties


Linked Data Publication via 303 Redirects

http://dbpedia.org/resource/Dresden - URI of the city ofDresdenhttp://dbpedia.org/page/Dresden - information resourcedescribing the city of Dresden in HTML formathttp://dbpedia.org/data/Dresden - information resourcedescribing the city of Dresden in RDF/XML formatfurther formats supported,e.g. http://dbpedia.org/data/Dresden.n3 for N3


http://dbpedia.org/resource/Dresden

http://dbpedia.org/page/Dresden

http://dbpedia.org/data/Dresden

http://dbpedia.org/data/Dresden.n3

DBpedia Links

Data set Predicate Count Tool

Amsterdam Museum owl:sameAs 627 SBBC Wildlife Finder owl:sameAs 444 SBook Mashup rdf:type 9 100

owl:sameAsBricklink dc:publisher 10 100CORDIS owl:sameAs 314 SDailymed owl:sameAs 894 SDBLP Bibliography owl:sameAs 196 SDBTune owl:sameAs 838 SDiseasome owl:sameAs 2 300 SDrugbank owl:sameAs 4 800 SEUNIS owl:sameAs 3 100 SEurostat (Linked Stats) owl:sameAs 253 SEurostat (WBSG) owl:sameAs 137CIA World Factbook owl:sameAs 545 S


DBpedia Links


ickr wrappr dbp:hasPhoto- 3 800 000 CCollection

Freebase owl:sameAs 3 600 000 CGADM owl:sameAs 1 900GeoNames owl:sameAs 86 500 SGeoSpecies owl:sameAs 16 000 SGHO owl:sameAs 196 LProject Gutenberg owl:sameAs 2 500 SItalian Public Schools owl:sameAs 5 800 SLinkedGeoData owl:sameAs 103 600 SLinkedMDB owl:sameAs 13 800 SMusicBrainz owl:sameAs 23 000New York Times owl:sameAs 9 700OpenCyc owl:sameAs 27 100 COpenEI (Open Energy) owl:sameAs 678 S


DBpedia Links


Revyu owl:sameAs 6Sider owl:sameAs 2 000 STCMGeneDIT owl:sameAs 904UMBEL rdf:type 896 400US Census owl:sameAs 12 600WikiCompany owl:sameAs 8 300WordNet dbp:wordnet_type 467 100YAGO2 rdf:type 18 100 000

Sum 27 211 732

(S: Silk, L: LIMES, C: custom script, missing: no regeneration)


DBpedia Links - Query Example

Compare funding per year (from FTS) and country with the gross domesticproduct of a country (from DBpedia)

SELECT ∗ SELECT ? f t s y e a r ? dbpcount ry (SUM(? amount ) AS ? fund i ng )

?com r d f : t ype f t s−o : Commitment .?com f t s−o : y ea r ? y ea r .? y ea r r d f s : l a b e l ? f t s y e a r .? b e n e f i t f t s−o : deta i lAmount ?amount .? b e n e f i t f t s−o : b e n e f i c i a r y ? b e n e f i c i a r y .? b e n e f i c i a r y f t s−o : coun t r y ? f t s c o u n t r y .? f t s c o u n t r y owl : sameAs ? dbpcount ry .

SELECT ? dbpcount ry ? gdpyear ? gdpnominal ? dbpcount ry r d f : t ype dbo : Country .? dbpcount ry dbp : gdpNominal ? gdpnominal .? dbpcount ry dbp : gdpNominalYear ? gdpyear .

FILTER ( ( ? f t s y e a r = s t r (? gdpyear ) )


Infrastructure

DBpedia has two extraction modes:

Wikipedia-database-dump-based extraction

DBpedia Live synchronisation (more later)

DBpedia Dumps:

The DBpedia Dump archive is located in:http://downloads.dbpedia.org/

Latest downloads is described in: http://dbpedia.org/Downloads

Ocial Endpoint (by OpenLink): http://dbpedia.org/sparql


http://downloads.dbpedia.org/

http://dbpedia.org/Downloads

http://dbpedia.org/sparql

Query Answering

Back to our Wikipedia questions:

What have Innsbruck and Leipzig in common?

Who are mayors of central European towns elevated more than1000m?

Which movies are starring both Brad Pitt and Angelina Jolie?

All soccer players, who played as goalkeeper for a club that has astadium with more than 40.000 seats and who are born in a countrywith more than 10 million inhabitants

Using the data extracted from Wikipedia and the public SPARQL endpointDBpedia can answer these questions.


DBpedia Live

DBpedia dumps are generated on a bi-annual basis

Wikipedia has around 100,000 150,000 page edits per day

DBpedia Live pulls page updates in real-time and extraction resultsupdate the triple store

In practice, a 5 minute update delay increases performance by 15%

Links

SPARQL Endpoint: http://live.dbpedia.org/sparql

Documentation: http://wiki.dbpedia.org/DBpediaLive

Statistics: http://live.dbpedia.org/LiveStats/


http://live.dbpedia.org/sparql

http://wiki.dbpedia.org/DBpediaLive

http://live.dbpedia.org/LiveStats/

DBpedia Live - Overview


DBpedia Internationalization (I18n)

DBpedia Internationalization Committee founded:

http://wiki.dbpedia.org/Internationalization

Available DBpedia language editions in:

Korean, Greek, German, Polish, Russian, Dutch, Portuguese, Spanish,Italian, Japanese, FrenchUse the corresponding Wikipedia language edition for input

Mappings available for 23 languages


http://wiki.dbpedia.org/Internationalization

DBpedia I18n - Overview


Applications: Disambiguation

Named entity recognition and disambiguation Tools such as: DBpediaSpotlight, AlchemyAPI, Semantic API, Open Calais, Zemanta and ApacheStanbol


Applications: Question Answering

DBpedia is the primary target for several QA systems in the QuestionAnswering over Linked Data (QALD) workshop series

IBM Watson relied also on DBpedia


Applications: Faceted Browsing

Neofonie Browser

gFacet

OpenLink faceted browser (fct)


Applications: Search and Querying

Query Builder

RelFinder

SemLens


Applications: Digital Libraries & Archives

Virtual International Authority Files (VIAF) project as Linked Data

VIAF added a total of 250,000 reciprocal authority links to Wikipedia.

DBpedia can also provide:

Context information for bibliographic and archive records (e.g. anauthor's demographics, a lm's homepage, an image etc.)Stable and curated identiers for linking.The broad range of Wikipedia topics can form the basis for a thesaurusfor subject indexing.


Applications: DBpedia Mobile

DBpedia Mobile is a location-centric DBpedia client application for mobiledevices consisting of a map view, the Marbles Linked Data Browser and aGPS-enabled launcher application.


Applications: DBpedia Wiktionary

Wiktionary is a Wikimedia project: http://wiktionary.org

171 languages, 3M words for English.

Extracted Using the DBpedia Information Extraction Framework

Easily congurable for every Wiktionary language edition

Pre-congured for German, Greek, English, Russian and French.http://Wiktionary.dbpedia.org100 milion triplesLemon model


Other Applications

See http://wiki.dbpedia.org/Applications for a more complete list


http://wiki.dbpedia.org/Applications

Outline






6 Enrichment

7 Repair



Classifi-cation/

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Quality Analysis

Evolution / Repair

Search/ Browsing/

Exploration

Extraction

Storage/ Querying

Manual revision/

Authoring



Linked Data - Achievements and Challenges

Achievements:

1 Extension of the Web with a datacommons (50B facts)

2 vibrant, global RTD community

3 Industrial uptake begins (e.g.BBC, Thomson Reuters, Eli Lilly,NY Times, Facebook, Google,Yahoo)

4 Governmental adoption in sight

5 Establishing Linked Data as adeployment path for the SemanticWeb.

Challenges:

1 Coherence: Relatively few,expensively maintained links

2 Quality: partly low quality dataand inconsistencies

3 Performance: Still substantialpenalties compared to relational

4 Data consumption: large-scaleprocessing, schema mapping anddata fusion still in its infancy

5 Usability: Missing direct end-usertools and network eect.

These issues are closely related andshould ultimately lead to anecosystem of interlinked knowledge!



Classifi-cation/

Enrichment

Quality Analysis

Evolution / Repair

Search/ Browsing/

Exploration

Extraction

Storage/ Querying

Manual revision/

Authoring



ExtractionLehmann, Bühmann (Univ. Leipzig) The Linked Data Life-Cycle 2013-08-23 67 / 252

Extraction

From unstructured sources

Formats: plain textMethods: NLP, text mining, ontology learning

From semi-structured sources

Formats: wiki markup, tagsTools: DBpedia framework (Wikipedia, Wictionary)

From structured sources

Formats: databases, spreadsheets, XMLRDB2RDF tools: Sparqlify, D2R, TriplifyCSV converters: RDF extension of Google Rene


Extraction Challenges


Improve F-Measure of existing NLP approaches (OpenCalais, OntosAPI)

Develop standardized, LOD enabled interfaces between NLP tools(NLP2RDF)

From semi-structured sources

Ecient bi-directional synchronization

From structured sources

Declarative syntax and semantics of data model transformations (W3CWG RDB2RDF)

Orthogonal challenges

Using LOD as background knowledge

Provenance


ABCDE


RDF Data Management


SPARQL RDF access still by a factor 2-10 slower than relational datamanagement

Performance increases steadily

Comprehensive, well-supported open-soure and commercialimplementations are available:

OpenLink's Virtuoso (os+commercial)OWLIM-Lite (free), OWLIM-SE, OWLIM-EnterpriseTalis (hosted)Bigdata (distributed)Allegrograph (commercial)Mulgara (os)


Storage and Querying Challenges

Reduce the performance gap between relational and RDF datamanagement

SPARQL Query extensions: Spatial/semantic/temporal datamanagement

View maintenance / adaptive reorganization based on common accesspatterns

More realistic benchmarks


Authoring


Authoring

Integrated in Existing Environments: Tiki

Data oriented: RDFauthor, rdfEditor

Schema oriented: Protégé, TopBraid Composer, NeOn Toolkit,Swoop, Neologism, Knoodl


Authoring: Semantic Wikis

1 Semantic (Text) Wikis

Authoring of semantically annotatedtextsSemantic MediaWiki, KiWi,(Wikipedia+DBpedia)

2 Semantic Data Wikis

Direct authoring of structuredinformation (i.e. RDF, RDF-Schema,OWL)OntoWiki


ABCDDBEFCCF


Interlinking

Data Web is an uncontrolled environment proliferation of equivalentor similar entities need for links / merging

Currently only few RDF triples are links

Manual Link Discovery:

Sindice Integration, LODStats, Semantic Pingback

Tool supported / Semi-Automatic:

SILK, LIMES, COMA, RDF-AIUsually via mapping specications / heuristics

Machine Learning / Automatic:

RAVEN, EAGLE, SILK GP


Interlinking Challenges

Apply work in the de-duplication/record linkage literature

Consider the open world nature of Linked Data

Use LOD background knowledge

Zero-conguration linking

Explore active learning approaches, which integrate users in a feedbackloop

Maintain a 24/7 linking service: Linked Open Data Around-The-Clockproject (http://latc-project.eu/)


http://latc-project.eu/

Enrichment

Currently, lack of knowledge bases with sophisticated schemainformation and instance data adhering to this schema

Goal: powerful reasoning, consistency checking and querying

Manual:

Via ontology editors, DBpedia mappings

(Semi-)Automatic:

DL-Learner, Statistical Schema Induction


Enrichment: Example

Given: knowledge base with property birthPlace (i.e. triples using thatproperty) but no information on the semantics of birthPlacePossibly enrichment:

ObjectProperty: birthPlace

Characteristics: Functional

Domain: Person

Range: Place

SubPropertyOf: hasBeenAt

Benets:

axioms serve as documentation for purpose and correct usage ofschema elements

additional implicit information can be inferred

improve the applicability of schema debugging techniques


Repair

Ontology Debugging: OWL reasoning to detect inconsistencies andsatisable classes + detect the most likely sources for the problems

basic task: provide feedback to user for resolving undesired entailments

justication J ⊆ O of an entailment is a minimal set of axioms fromwhich the entailment can be drawn


AA


Linked Data Quality Analysis

Quality on the Data Web is varying a lot

Hand crafted or expensively curated knowledge base (e.g. DBLP,UMLS) vs. extracted from text or Web 2.0 sources (DBpedia)

Quality = Fitness for use

Often not necessary to x all problems, but to know about them

30+ quality dimensions dened in recent survey

Research Challenge

Establish measures for assessing the authority, provenance, reliability ofData Web resources


Evolution © CC-BY-SA by alasis on flickr)


KB Evolution

Tasks:

Performing knowledge base changes / refactoring

Ensuring consistency of related knowledge

Managing changes, e.g. undo operations

Update materialized inferred data upon changes

Update materialised links to other data upon changes

Tools:

Protégé - PROMPT and change management plugins

EvoPat - easily re-usable and sharable evolution patterns dened viaSPARQL

PatOMat - ontology transformation framework


A


Exploration

RDF data can be complex (as discussed by Pascal Hitzler)

Exploration phase aims to make data accessible to non-experts

Options:

Faceted BrowsingQuestion AnsweringQuery BuildersVisualisation of statistical or geospatial data. . .


Catalogus Professorum Lipsiensis


Visual Query Builder


Relationship Finder in CPL



Classifi-cation/

Enrichment

Quality Analysis

Evolution / Repair

Search/ Browsing/

Exploration

Extraction

Storage/ Querying

Manual revision/

Authoring



Make the Web a Linked Data Washing Machine


Tool Support for Life-Cycle?

Many SW tools support one or more life-cycle stages

Linked Data Stack (http://stack.linkeddata.org) provides aconsolidated repository of such tools

Each tool is a Debian package

Lightweight integration between tools via common vocabularies andSPARQL

Demonstrator interfaces for showing tools in combination

Developed by LOD2 and GeoKnow EU projects

GeoKnow


http://stack.linkeddata.org

Outline






6 Enrichment

7 Repair



Classifi-cation/

Enrichment

Quality Analysis

Evolution / Repair

Search/ Browsing/

Exploration

Extraction

Storage/ Querying

Manual revision/

Authoring



Knowledge Extraction

Knowledge Extraction is the creation of knowledge from structured(relational databases, XML) and unstructured (text, documents, images)sources.

Resulting knowledge needs to be in a machine-readable and

machine-interpretable format and facilitate inferencing

Similar to Information Extraction (NLP) and ETL (Data Warehouse),but main dierence: extraction result goes beyond the creation ofstructured information or the transformation into a relational schema

Requires re-use of existing formal knowledge (reusing ontologies) orthe generation of a schema based on the source data


Categorisation of Approaches

Source - Examples: plain text, relational databases, XML, CSV

Exposition - How is the extracted knowledge made explicit? How canyou query and perform inference?

Synchronization - Is the knowledge extraction process executed onceto produce a dump or is the result synchronized with the source? Arechanges to the result written back (Bi-directional)?

Reuse of Vocabularies - Can popular ontologies (Good Relations,FOAF, . . . ) be re-used to simplify global data integration?

Automatisation - manual, semi-automatic, automatic

Domain Ontology Required - Does the approach require apre-dened ontology or can it create a schema from the source(e.g. ontology learning)?


Extraction from Structured Sources to RDF

Simple mappings from RDB tables/views to RDF

Direct mapping of the model of relational databases to RDFTable 7→ OWL classRow 7→ Instance s of this classCell with value o in column p 7→ Triple (s,p,o)Details: http://www.w3.org/TR/rdb-direct-mapping/

Complex mappings of relational databases to RDFAdditional renements can be employed to 1:1 mapping to improve theusefulness of RDF output

Extract or learn an OWL schema from the given database schemaMap the schema and its contents to a pre-existing domain ontology

Powerful mapping languages: R2RML, SML

XML

XML tree structure can be directly converted to RDF graph structureComplex mappings possible, e.g. via XSLT processors


http://www.w3.org/TR/rdb-direct-mapping/

Extraction from Natural Language Sources

80% of the information in business documents is in unstructurednatural language1

(-) Increased complexity and decreased quality of extraction

(+) Potential for a massive acquisition of extracted knowledge

Traditional Information Extraction (IE)

Recognize and categorise elements in textTechniques: Named Entity Recognition (NER), Coreference Resolution(CO), . . .

Ontology Learning (OL) from Text

Learn whole ontologies from natural language textUsually (semi-)automatic extracted

1Wimalasuriya, Dou. "Ontology-based information extraction: [. . . ]" Journal of Information Science


LinkedGeoData + Sparqlify

Example: LinkedGeoData Knowledge Extraction Project using Sparqlify

Structure

Motivation

OpenStreetMap

LGD Architecture

Mapping

Access (How LinkedGeoData is published)

Use Cases


Motivation

Ease information integration tasks that require spatial knowledge,such as

Oerings of bakeries next doorMap of distributed branches of a companyHistorical sights along a bicycle track

LOD cloud contains data sets with spatial features

e.g. Geonames, DBpedia, US census, EuroStatBut: they are restricted to popular or large entities like countries,famous places etc. or specic regions

Therefore they lack buildings, roads, mailboxes, etc.


OpenStreetMap - Datamodel

Basic entities are:

Nodes Latitude, Longitude.Ways Sequence of nodes.Relations Associations between any number of nodes, ways andrelations. Every member in a relation plays a certain role.

Each entity may be described with tags (= key-value pairs)

A way is closed if the ID of the last referenced node equals that of therst one.

Whether a closed way denotes a linear ring or a polygon (i.e. whetherthe enclosed area is part of the respective OSM entity) depends on thetags.


Example: Leipzig's Zoo


Comparison: Leipzig's Zoo (OpenStreetMap)


Comparison: Leipzig's Zoo (GoogleMaps)


LGD Architecture


Tag Mappings

Key-value pairs will be assigned toRDF ressources

Each pair (k , v) can be annotated withdatatypes, language tags, classes

Mappings are themselves tables

Example table:lgd_map_literal

k property lang

name rdfs:labelname:en rdfs:label enalt_label skos:altLabelnote rdfs:comment. . . . . . . . .


View Denition

RDF mapping of the data from aPostgreSQL database

Create View lgd_nodes As

Construct

?n a lgdm:Node .

?n geom:geometry ?g .

?g ogc:asWKT ?o .

With

?n = uri(lgd:node, ?id)

?g = uri(lgd-geom:node, ?id)

?o = typedLiteral(?geom, ogc:wktLiteral)

From

nodes


Sparqlify

SPARQL-SQL Rewriter

Rewrites SPARQL Queries accordingto the view denitionPlatform module oers SPARQLEndpoint and Linked Data interface

https:

//github.com/AKSW/Sparqlify


https://github.com/AKSW/Sparqlify

https://github.com/AKSW/Sparqlify

Rest-API

Oers REST methods for frequentqueries

Based on SPARQL (Virtuoso) endpoint


Downloads

RDF dataset for download

Generated usingConstruct ?s ?p ?o

http:

//downloads.linkedgeodata.org


http://downloads.linkedgeodata.org


Ontology

Enriched classes and properties with multilingual labels fromTranslateWiki

http://translatewiki.net

Imported icons for 90 classes from the freely available iconcollection from the SJJB Management

http://www.sjjb.co.uk/mapicons/


http://translatewiki.net

http://www.sjjb.co.uk/mapicons/

SML Mapping Examples

The following slides demonstrate how to map relational data to RDFwith the Sparqlication Mapping Language (SML).

Thereby, these prexes are used:Prexes

prex IRI

rdfs http://www.w3.org/2000/01/rdf-schema#

ogc http://www.opengis.net/ont/geosparql#

geom http://geovocab.org/geometry#

lgd http://linkedgeodata.org/triplify/

lgd-geom http://linkedgeodata.org/geometry/


http://www.w3.org/2000/01/rdf-schema#

http://www.opengis.net/ont/geosparql#

http://geovocab.org/geometry#

http://linkedgeodata.org/triplify/

http://linkedgeodata.org/geometry/

SML - Mapping Example I: The Goal (1/4)

Input Table

nodesid geom

1 POINT(0 0)2 POINT(1 1)

How to map tables to RDF?

How to introduce thecommonly useddistinction in GIS betweenfeature and geometry?

Aimed for RDF Output

@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .

...

lgd:node1 geom:geometry lgd-geom:node1 .


lgd-geom:node1 ogc:asWKT "POINT(0 0)"^^ogc:wktLiteral .



SML - Mapping Example I: SML Syntax Outline (2/4)

Input Table

nodesid geom


Create View myNodesView As

Construct

...

With

...

From

...



...






SML - Mapping Example I: Construct and From (3/4)

Input Table

nodesid geom



Construct


?g ogc:asWKT ?o

With

...

From

nodes



...






SML - Mapping Example I: Complete! (4/4)

Input Table

nodesid geom



Construct


?g ogc:asWKT ?o

With

?n = uri(lgd:node, ?id)

?g = uri(lgd-geom:node, ?id)

?o = typedLiteral(?geom,

ogc:wktLiteral)

From

nodes



...






SML Mapping Examples

A more complex example, which demonstrates the use of an SQLmapping table and an SQL helper view.


SML - Mapping Example II: The Goal (1/8)

Input Table

node_tagsid k v

1 name Universitaet Leipzig1 name:en University of Leipzig1 amenity university1 addr:street Augustusplatz1 addr:city Leipzig



@prefix lgd: <http://linkedgeodata.org/triplify/> .

lgd:node1 rdfs:label "Universitaet Leipzig" .

lgd:node1 rdfs:label "University of Leipzig"@en .


SML - Mapping Example II: Source Data (2/8)

OSM Table

node_tagsid k v



SML - Mapping Example II: Mapping Table (3/8)

OSM Table RDF Mapping Table

node_tagsid k v


lgd_map_literalk property lang



SML - Mapping Example II: Helper View (4/8)

OSM Table RDF Mapping Table

node_tagsid k v


lgd_map_literalk property lang


Helper View

lgd_node_tags_literalid property v lang

1 rdfs:label Universitaet Leipzig1 rdfs:label University of Leipzig en. . . . . . . . . . . .

SELECT id, property, v, lang FROM node_tags, lgd_map_literal

WHERE node_tags.k = lgd_map_literal.k


SML - Mapping Example II: SML View (5/8)

Logical Table SML View


1 rdfs:label Univ. L.1 rdfs:label Univ. of L. en. . . . . . . . . . . .

Create View lgd_node_tags_text As

Construct







Construct

?s ?p ?o .

With

...

From

lgd_node_tags_literal







Construct

?s ?p ?o .

With

?s = uri(lgd:node, ?id)

?p = uri(?property)

?o = plainLiteral(?v, ?lang)

From





+lgd_node_tags_literal

id property v lang



Construct

?s ?p ?o .

With

?s = uri(lgd:node, ?id)

?p = uri(?property)

?o = plainLiteral(?v, ?lang)

From


Resulting RDF


@prefix lgd: <http://linkedgeodata.org/triplify/> .

lgd:node1 rdfs:label "Universitaet Leipzig" .

lgd:node1 rdfs:label "University of Leipzig"@en .


Further Tag Mappings

lgd_map_dataypek datatype

seats integerunisex boolean

lgd_map_propertyk property

website foaf:homepage

lgd_map_resource_kk property object

highway rdf:type lgdo:HighwayThing

lgd_map_resource_kvk v property object

waterway river rdf:type lgdo:River


LGD Edit Tool

Multi User Tag Mapping WebApp


Resources

Sparqlifyhttp://sparqlify.org

LinkedGeoDatahttp://linkedgeodata.org

Tag Mappingshttps://github.com/GeoKnow/LinkedGeoData/blob/master/linkedgeodata-core/src/main/resources/org/aksw/linkedgeodata/sql/Mappings.sql

SML View Denitionshttps://github.com/GeoKnow/LinkedGeoData/blob/master/linkedgeodata-core/src/main/resources/org/aksw/linkedgeodata/sml/LinkedGeoData-Triplify-IndividualViews.sml


http://sparqlify.org

http://linkedgeodata.org

https://github.com/GeoKnow/LinkedGeoData/blob/master/linkedgeodata-core/src/main/resources/org/aksw/linkedgeodata/sql/Mappings.sql

https://github.com/GeoKnow/LinkedGeoData/blob/master/linkedgeodata-core/src/main/resources/org/aksw/linkedgeodata/sql/Mappings.sql

https://github.com/GeoKnow/LinkedGeoData/blob/master/linkedgeodata-core/src/main/resources/org/aksw/linkedgeodata/sml/LinkedGeoData-Triplify-IndividualViews.sml

https://github.com/GeoKnow/LinkedGeoData/blob/master/linkedgeodata-core/src/main/resources/org/aksw/linkedgeodata/sml/LinkedGeoData-Triplify-IndividualViews.sml

Statistics (15 August 2013)

Complete OSM planet le corresponds to ∼ 20.000.000.000 triples

Virtual access via Sparqlify

Downloads limited to selected classes.292.780.188 Triples

153.613.243 triples of Nodes139.166.945 triples of WaysRelations not yet available for download

Among them

532.812 PlaceOfWorship82.788 RailwayStation72.091 Toilets71.613 Town19.937 City


Access

Materialized Sparql Endpoint (based on Virtuoso DB, downloaddatasets loaded)

http://linkedgeodata.org/sparql

http://linkedgeodata.org/snorql

Virtual Sparql Endpoint (based on Sparqlify, access to 20B triples,limited SPARQL 1.0 support)

http://linkedgeodata.org/vsparql

http://linkedgeodata.org/vsnorql

Rest Interface (based on the Virtual Sparql Endpoint)

Supports limited queries (e.g. circular/rectangular area, ltering bylabels)

Downloads


Monthly updates on the above datasets envisioned


http://linkedgeodata.org/sparql

http://linkedgeodata.org/snorql

http://linkedgeodata.org/vsparql

http://linkedgeodata.org/vsnorql


Use Cases Augmented Reality


Use Cases Generic Browsing


Outline






6 Enrichment

7 Repair



Classifi-cation/

Enrichment

Quality Analysis

Evolution / Repair

Search/ Browsing/

Exploration

Extraction

Storage/ Querying

Manual revision/

Authoring



Why Link Discovery?

1 Fourth Linked Dataprinciple

2 Links are central for

Cross-ontology QAData IntegrationReasoningFederated Queries...

3 2011 topology of theLOD Cloud:

31+ billion triples≈ 0.5 billion linksowl:sameAs in mostcases


Why is it dicult?

1 Time complexity

Large number of triplesQuadratic a-priori runtime69 days for mapping cities fromDBpedia to Geonames (1ms percomparison)decades for linking DBpedia and LGD. . .

Denition (Link Discovery)

Given sets S and T of resources and relation RTask: Find M = (s, t) ∈ S × T : R(s, t)Common approaches:

Find M ′ = (s, t) ∈ S × T : σ(s, t) ≥ θFind M ′ = (s, t) ∈ S × T : δ(s, t) ≤ θ


Why is it dicult?

2 Complexity of specications

Combination of several attributes required for high precisionTedious discovery of most adequate mappingDataset-dependent similarity functions


LIMES Framework


Runtime Optimization

Reduce the number of comparisons C (A) ≥ |M ′| (assuming we needall σ/θ values for links)

Maximize reduction ratio:

RR(A) = 1− C (A)

|S ||T |

Question

Can we devise lossless approaches with guaranteed RR?

Advantages

Space managementRuntime predictionResource scheduling


RR Guarantee

Best achievable reduction ratio: RRmax = 1− |M′||S||T |

Approach H(α) fullls RR guarantee criterion, i:

∀r < RRmax,∃α : RR(H(α)) ≥ r

Here, we use relative reduction ratio (RRR):

RRR(A) =RRmax

RR(A)


RR Guarantee

Best achievable reduction ratio: RRmax = 1− |M′||S||T |

Approach H(α) fullls RR guarantee criterion, i:

∀r < RRmax, ∃α : RR(H(α)) ≥ r

Here, we use relative reduction ratio (RRR):

RRR(A) =RRmax

RR(A)


Goal

Formal Goal

Devise H(α) : ∀r > 1, ∃α : RRR(H(α)) ≤ r


Restrictions

Minkowski Distance

δ(s, t) = p

√√√√ n∑i=1

|si − ti |p, p ≥ 2


Space Tiling

HYPPO

δ(s, t) ≤ θ describes a hypersphere

Approximate hypersphere by using a hypercube

Easy to computeNo loss of recall (blocking)


Space Tiling

Set width of single hypercube to ∆ = θ/α

Tile Ω = S ∪ T into the adjacent cubes CCoordinates: (c1, . . . , cn) ∈ Nn

Contains points ω ∈ Ω : ∀i ∈ 1 . . . n, ci∆ ≤ ωi < (ci + 1)∆


Space Tiling

Set width of single hypercube to ∆ = θ/αTile Ω = S ∪ T into the adjacent cubes C

Coordinates: (c1, . . . , cn) ∈ Nn

Contains points ω ∈ Ω : ∀i ∈ 1 . . . n, ci∆ ≤ ωi < (ci + 1)∆


HYPPO

Combine (2α + 1)n hypercubes around C (ω) to approximatehypersphere

RRR(HYPPO(α)) = (2α+1)n

αnS(n)

limα→∞

RRR(HYPPO(α)) = 2n

S(n)


HYPPO

RRR(HYPPO) for p = 2, n = 2, 3, 4 and 2 ≤ α ≤ 50

limα→∞

RRR(HYPPO(α)) = 4π ≈ 1.27 (n = 2)

limα→∞

RRR(HYPPO(α)) = 6π ≈ 1.91 (n = 3)

limα→∞

RRR(HYPPO(α)) = 32π2≈ 3.24 (n = 4)


HR3: Idea

index(C , ω) =

0 if ∃i : |ci − c(ω)i | ≤ 1, 1 ≤ i ≤ n,n∑

i=1(|ci − c(ω)i | − 1)p else,


HR3: IdeaCompare C (ω) with C i index(C , ω) ≤ αpα = 4, p = 2


HR3: Idea

Lemma

∀s ∈ S : index(C , s) > αp implies that all t ∈ C are non-matches

Claims

No loss of recallXlimα→∞

RRR(HR3(α)) = 1


HR3: Lemma 3

Lemma

∀α > 1 RRR(HR3(2α)) < RRR(HR3(α))

p = 2, α = 4


HR3: Proof

Lemma

∀α > 1 RRR(HR3(2α)) < RRR(HR3(α))

p = 2, α = 8


HR3: Proof

Lemma

∀α > 1 RRR(HR3(2α)) < RRR(HR3(α))

p = 2, α = 25


HR3: Proof

Lemma

∀α > 1 RRR(HR3(2α)) < RRR(HR3(α))

p = 2, α = 50


HR3: Idea

Theorem

limα→∞

RRR(HR3(α)) = 1

Claims

No loss of recallXlimα→∞

RRR(HR3(α)) = 1X


HR3: Experiments

Compare HR3 with LIMES 0.5's HYPPO and SILK 2.5.1

Experimental Setup:

Deduplicating DBpedia places by minimum elevation, elevation andmaximum elevation (θ = 49m, 99m).Geonames and LinkedGeoData by longitude and latitude (θ = 1, 9)

64-bit computer with a 2.8GHz i7 processor with 8GB RAM.


HR3: Experiments (Comparisons)

Experiment 2: Deduplicating DBpedia places, θ = 99m0.64× 106 less comparisons


HR3: Experiments (Comparisons)

Experiment 4: Linking Geonames and LinkedGeoData, θ = 9

4.3× 106 less comparisons


HR3: Experiments (Runtime)

Experiment 1, 2: DBpedia, θ = 49, 99mExperiment 3, 4: Geonames and LGD, θ = 1, 9

Exp. 1 Exp. 2 Exp. 3 Exp. 4100

101

102

103

104

Run

time

(s)

HR3HYPPOSILK


HR3: Summary

Mission

New category of algorithms for link discovery

Presented HR3

Link discovery in ane spaces with Minkowski measuresOutperforms the state of the art (runtime, comparisons)Optimal reduction ratioIntegrated in LIMES


Learning Complex Specications

Supervised (mostly active, e.g., RAVEN, EAGLE, SILK)

Unsupervised (e.g., KnoFuss, EUCLID, EAGLE)



Supervised (mostly active, e.g., RAVEN, EAGLE, SILK)

Unsupervised (e.g., KnoFuss, EUCLID, EAGLE)



Insight

Choice of right example is key for learning

So far, only use of informativeness

Question

Can we do better by using more information?

Higher F-measure

Often slower


Basic Idea

Use similarity of link candidates when selecting most informativeexamples (intra + inter class similarity)


Similarity of Candidates

Link candidate x = (s, t) can be regarded as vector(σ1(x), . . . , σn(x)) ∈ [0, 1]n.

Similarity of link candidates x and y :

sim(x , y) =1

1 +

√n∑

i=1(σi (x)− σi (y))2

. (1)

Allows exploiting both intra- and inter-class similarity


Graph Clustering

Rationale: Use intra-class similarity

Approach

Cluster elements of S+ and S− independentlyChoose one element per cluster as representativePresent oracle with most informative representatives

0.8

0.9

0.8

S+

S-

0.8

0.9

0.8

0.25

0.25

0.9

0.80.8

0.8

0.25a

b

c

d

e

d

f g

hi

k

l


BorderFlow

G = (V ,E , ω) with V = S+ or V = S−

ω(x , y) = sim(x , y)

Keep best ec edges for each x ∈ V


BorderFlow

Seed-based algorithm

Goal: Maximize borderow ratio bf (X ) = Ω(b(X ),X )Ω(b(X ),n(X ))

http://sourceforge.net/projects/cugar-framework/



BorderFlow



http://sourceforge.net/projects/cugar-framework/Lehmann, Bühmann (Univ. Leipzig) The Linked Data Life-Cycle 2013-08-23 168 / 252


BorderFlow






BorderFlow



http://sourceforge.net/projects/cugar-framework/Lehmann, Bühmann (Univ. Leipzig) The Linked Data Life-Cycle 2013-08-23 169 / 252


Conclusion

Can be combined with arbitrary active learning ML algorithms

Was experimentally combined with EAGLE (genetic programming) andRAVEN (linear classier) and shown to outperform the plaininformativeness function in terms of F-measure

Choice of example important to minimise user eort

Contact me for detailed experimental results

Longer runtimes (up to 2×)


Summary

Linking crucial task in the web of dataTow key problems

1 Ecient execution of link specications2 Creation of link specication

Presented HR3 to handle the rst problemPresented COALA as building block for the second problem


Outline






6 Enrichment

7 Repair



Classifi-cation/

Enrichment

Quality Analysis

Evolution / Repair

Search/ Browsing/

Exploration

Extraction

Storage/ Querying

Manual revision/

Authoring



Motivation

rise in the availability and usage of knowledge bases

still a lack of knowledge bases that consist of sophisticated schemainformation and instance data adhering to this schema

e.g. in the life sciences several knowledge bases

only consist of schema informationto a large extent, a collection of facts without a clear structure(e.g. information extracted from databases)

combination of sophisticated schema and instance data would allowpowerful reasoning, consistency checking, and improved querying

→ create schemata based on existing data


Example

dbr : Brad_Pitt : b i r t h P l a c e dbr : Shawnee , _Oklahoma ;a : Person .

dbr : Angela_Merkel : b i r t h P l a c e dbr : Hamburg ;a : Person .

dbr : A l b e r t_E i n s t e i n : b i r t h P l a c e dbr : Ulm ;a : Person .

dbr : Shawnee , _Oklahoma a : P lace .dbr : Ulm a : P lace .dbr : Hamburg a : P lace .

Suggestions: birthPlace

Ob j e c tP rope r t y : b i r t h P l a c eC h a r a c t e r i s t i c s : F un c t i o n a lDomain : PersonRange : P laceSubPropertyOf : hasBeenAt


Benets of an expressive schema

Axioms serve as documentation for the purpose and correct usage ofschema elements

Additional implicit information can be inferred

Improve querying optimisations

Improve/allow the application of schema debugging techniques


Each person was only born at one place?!


birthPlace birthPlace

6=

birthPlace is functional

SELECT ? s WHERE ? s dbo : b i r t hP l a c e ?o1 .? s dbo : b i r t hP l a c e ?o2 .FILTER (? o1 != ?o2 )


birthPlace birthPlace

6=

birthPlace is functional

SELECT ? s WHERE ? s dbo : b i r t h P l a c e ?o1 .? s dbo : b i r t h P l a c e ?o2 .FILTER (? o1 != ?o2 )


Where was Julia Nannie Wallace born?


Julia Nannie Wallace was born in Lacrosse?


No, Julia Nannie Wallace was born in La Crosse!


birthPlace

Sport

rdf:type

birthPlace range Place

Placerdf:type

Place disjointWith Sport

6=

City

v

SELECT ? s ? p l a c e WHERE ? s dbo : b i r t h P l a c e ? p l a c e .? p l a c e r d f : t ype / r d f s : subC la s sOf ∗ ? type1 .? type2 r d f s : subC la s sOf ∗ dbo : P lace .? type1 owl : d i s j o i n tW i t h ? type2 .


birthPlace

Sportrdf:type


Placerdf:type


6=

City

v



birthPlace

Sport

rdf:type


Placerdf:type


6=

City

v



3 Steps to get a schema

SPARQLEndpoint

Input: Entity URI, Axiom Type, Knowledge Base (SPARQL Endpoint)

3-Phase EnrichmentLearning Approach:



1. obtain schema information

SPARQLEndpoint


Background Knowledge


(onl

y ex

ecu

ted

once

per

know

ledg

e ba

se)




Reasoner

SPARQLEndpoint



BackgroundKnowledge+ Relevant Instance Data

(opt

ion

alin

voca

tion

)

2. obtain axiom type and entity specific data


(onl

y ex

ecu

ted

once

per

know

ledg

e ba

se)

(sam

ple

dat

aif

nece

ssar

y)




Reasoner

SPARQLEndpoint

EnrichmentOntology




List of Axiom Suggestions+ Metadata

(opt

ion

alin

voca

tion

)


3. run machine learning algorithm


(onl

y ex

ecu

ted

once

per

know

ledg

e ba

se)

(sam

ple

dat

aif

nece

ssar

y)

Learner

DL-Learner




Reasoner

SPARQLEndpoint

EnrichmentOntology




List of Axiom Suggestions+ Metadata

(opt

ion

alin

voca

tion

)


3. run machine learning algorithm


(onl

y ex

ecu

ted

once

per

know

ledg

e ba

se)

iterate over all axiom typesand schema entities for fullenrichment

(sam

ple

dat

aif

nece

ssar

y)

Learner

DL-Learner


Starting Point

SPARQL endpoint: http://dbpedia.org/sparql

Entity URI: http://dbpedia.org/ontology/author

Axiom Type: Object Property Domain


http://dbpedia.org/sparql

http://dbpedia.org/ontology/author

Step 1 - Obtaining Schema Information

CONSTRUCT WHERE ? sub r d f s : subC la s sOf ? sup .

ORDER BY DESC(? sub ) LIMIT 1000 OFFSET 1000

dbo : D i s e a s e r d f s : subC la s sOf owl : Thing .dbo : Book r d f s : subC la s sOf dbo : WrittenWork .dbo : WrittenWork r d f s : subC la s sOf dbo :Work .dbo :Work r d f s : subC la s sOf owl : Thing .dbo : Ph i l o s o ph e r r d f s : subC la s sOf dbo : Person .dbo : Person r d f s : subC la s sOf dbo : Agent .dbo : Agent r d f s : subC la s sOf owl : Thing .dbo : Spor t r d f s : subC la s sOf dbo : A c t i v i t y .dbo : A c t i v i t y r d f s : subC la s sOf owl : Thing .dbo : F i s h r d f s : subC la s sOf dbo : Animal .


Step 2 - Obtain axiom type and entity specic data

SELECT ? type (COUNT(DISTINCT ? s ) AS ? cnt ) WHERE ? s dbo : au tho r ?o .? s a ? type .

GROUP BY ? type ORDER BY DESC(? cnt )

type cnt

owl:Thing 30284dbo:Work 30284schema:CreativeWork 30284dbo:WrittenWork 25730dbo:Book 24673schema:Book 24673dbo:TelevisionShow 2567dbo:Play 1057...

...

CONSTRUCT WHERE ? ind dbo : au tho r ?o .? i nd a ? type .

ORDER BY DESC(? ind ) LIMIT 1000 OFFSET 2000

...dbped ia : The_Adventures_of_Tom_Sawyerdbo : au tho r dbped ia : Mark_Twain ;r d f : t ype dbo : Book .dbped ia : The_Zombie_Survival_Guidedbo : au tho r dbped ia : Max_Brooks ;r d f : t ype dbo : WrittenWork .dbped ia : Web_Therapydbo : au tho r dbped ia : Lisa_Kudrow ;r d f : t ype dbo : Book ....


Step 3 - Scoring

dbped ia : The_Adventures_of_Tom_Sawyerdbo : au tho r dbped ia : Mark_Twain ;r d f : t ype dbo : Book .

dbped ia : The_Zombie_Survival_Guidedbo : au tho r dbped ia : Max_Brooks ;r d f : t ype dbo : WrittenWork .

dbped ia : Web_Therapydbo : au tho r dbped ia : Lisa_Kudrow ;r d f : t ype dbo : Book .

Score(Domain(dbo:author, dbo:Book))= 23 ≈ 66.7%

Score(Domain(dbo:author, dbo:WrittenWork))= 13 ≈ 33.3%

dbo : Book r d f s : subC la s sOf dbo : WrittenWork .

Score(Domain(dbo:author, dbo:WrittenWork))= 33 = 100%


Step 3 - Scoring(2)

Problem:

support for axiom in KB not taken into account→ no dierence between 3 out of 3 and 100 out of 100

Solution:

Average of 95% condence interval (Wald method)

p′ = s+2

m+4

s −#successm −#total

min(1, p′ + 1.96 ·√

p′·(1−p′)m+4

) max(0, p′ − 1.96 ·√

p′·(1−p′)m+4

)

In 95% of the intervals the true value is between ... and ...

Score(Domain(dbo:author, dbo:Book))≈ 57.3%Score(Domain(dbo:author, dbo:WrittenWork))≈ 69.1%


More Complex Axioms

"Pattern Based Knowledge Base Enrichment", ISWC 2013


Outlook and Summary

Schema in the Linked Data Web often shallow → tools needed tosupport knowledge engineers

Showed some techniques for learning OWL axioms on large knowledgebases available as SPARQL endpoints

More complex aioms require:

OWL-SPARQL rewriting orFragment extraction

Small- and medium sized knowledge bases can be handled viatechniques from Inductive Logic Programming

All algorithms implemented in DL-Learner framework(http://dl-learner.org)


http://dl-learner.org

Outline






6 Enrichment

7 Repair



Classifi-cation/

Enrichment

Quality Analysis

Evolution / Repair

Search/ Browsing/

Exploration

Extraction

Storage/ Querying

Manual revision/

Authoring



Motivation

increasing number of knowledge bases in theSemantic Web (see e.g. LOD cloud)

maintenance of knowledge bases withexpressive semantics is challenging


(Automatically) Detectable Ontology Problems

Common problems:

Syntactic Problems

Structural Problems

Semantic Problems (focus of talk)

Task Based Problems:

Reasoning Related Problems

Linked Data Related Problems


Syntactic Problems

Syntactic errors are mainly violations of conventions of the language inwhich the ontology is modelled.

Example (Validity of XML)

<?xml v e r s i o n=" 1 .0 "?><rdf:RDF xm l n s : r d f=" h t t p : //www.w3 . org /1999/02/22− rd f−

syntax−ns#" xmlns :dc=" h t t p : // p u r l . o rg /dc/ e l ement s/1 .1/ "><r d f : D e s c r i p t i o n r d f : a b o u t=" h t t p : //www.w3 . org /">

<d c : t i t l e>World Wide Web Consort ium</ d c : t i t l e></ rdf :RDF>

FatalError: The element type rdf:Description must be terminated by thematching end-tag </rdf:Description>.[Line = 7, Column = 3]


Structural Problems

Problems in the taxonomy

Example (Circularities)

A v B,B v C ,C v A


Reasoning Related Problems

Problems which negatively aect the performance of reasoning overexpressive knowledge bases

Example (A named concept is equivalent to an AllValues restriction)

A ≡ ∀r .CReasoning complexity:

Universal restriction does not require to have a property value but onlyrestricts the values for existing property values

Any concept B for which instances cannot have r -llers satises therestriction, i.e. B v ∀r .C , and becomes a subclass of A

Typically leads to unintended inferences and additional inferences mayeventually slow down reasoning performance

Can be checked via Pellint (part of Pellet)


Linked Data Related Problems

Problems which are the specic to publishing RDF using the Linked Dataprinciples

Incorrect implementation of content negotiation

Mixing up information and non-information resources


Semantic Problems

Logical contradictions in the underlying knowledge base

Example (Unsatisable classes)

O = A v B u C ,C v ¬B |= A v ⊥

Example (Inconsistent ontology)

O = A v B u C ,C v ¬B,A(x) |= > v ⊥

Usually handled by Ontology Debugging


Ontology Debugging

Problem: We have undesirable entailments

Solution: Repair (Delete/Modify) responsible axiomsQuestion: Which axioms?


Ontology Debugging

Problem: We have undesirable entailmentsSolution: Repair (Delete/Modify) responsible axioms

Question: Which axioms?


Ontology Debugging

Problem: We have undesirable entailmentsSolution: Repair (Delete/Modify) responsible axiomsQuestion: Which axioms?


Justication

Justication

For an ontology O and an entailment η where O |= η, a set of axioms J isa justication for η in O if J ⊆ O,J |= η and if J ′ ⊂ J then J ′ 6|= η.

Minimal subsets of an ontology that are sucient for a givenentailment to hold

Synonyms: MUPS (Minimal Unsatisability Preserving Sub-TBoxes),MinAs (Minimal Axiom sets), Kernels

Observations:

there can be multiple justications for a single entailment

an axiom can be part of multiple justications


Justication - Example

O =

B v ∃r .D (1)

B v ∀r .¬D (2)

A v B u C (3)

B v ¬C (4)

A v E (5)

A v ¬E u F (6)

|= A v ⊥

J1 = 1, 2, 3

J2 = 5, 6

J3 = 3, 4


Justication Based Repair

For a repair, at least one axiom from every justication needs to beremoved.

For a repair plan, all justications are needed.


Justication Algorithms

Single justication:

Glass Box: Modifying underlying reasoning algorithm (tableau tracing)

Black-Box: Using reasoner as oracle

All justications:

Reiter's Hitting Set Tree Algorithm (HST)


Black-Box

Expansion-Contraction Strategy

Expansion: Add axioms to empty set until entailment holds

Contraction: Remove axioms from set such that set becomes minimaland entailment still can be derived.

CHAPTER 3. COMPUTING JUSTIFICATIONS 54

Expansion Contraction

AxiomAxiom in justificationSelected axiom

Key:

Figure 3.1: A Depiction of a Black-Box Expand-Contract Strategy

3.2 Black-Box Algorithms for Computing Sin-

gle Justifications

The basic idea behind a black-box justification finding algorithm is to systemat-

ically test different subsets of an ontology in order to find one that corresponds

to a justification. As depicted in Figure 3.1, subsets of an ontology are typically

explored using an “expand-contract” strategy. In order to compute a justification

for O |= η, an initial, small, subset S of O (represented by circles with thick black

borders in Figure 3.1) is selected. The axioms in S are typically the axioms whose

signature has a non-empty intersection with the signature of η, or axioms that

“define”2 terms in the signature of η. A reasoner is then used to check if S |= η,

and if not, S is expanded by adding a few more axioms from O. This incremental

expansion phase continues until S is large enough so that it entails η. When this

happens, either S, or some subset of S, is guaranteed to be a justification for η.

At this point S is gradually contracted until it is a minimal set of axioms that

entails η i.e. a justification for η in O.

In some black-box algorithms the expand phase may be trivial, or “empty”,

where S is immediately expanded to all input axioms. In this situation it is the

contraction phase which “does all the work”. An example of such a strategy is

presented in Algorithm 3.1. In this algorithm a set of axioms S is initialised

(expanded) with all of the axioms in O so that S |= η. S is then pruned one

axiom at a time, so that for each α ∈ S, if S \ α |= η, then S = S \ α. This

process terminates when all axioms α ∈ S have been examined, at which point

2For example, the axiom A v B defines the class name A


Source: M. Horridge:JusticationBased Explanation in Ontologies(PhD

Thesis)

Hitting Set Tree Algorithm

from eld of Model Based Diagnosis

given a faulty system (ontology), it constructs nite tree whose

nodes are labelled with conict sets (justications), and whoseedges are labelled with components (axioms)

nds all minimal hitting sets, which represent diagnoses for theconict sets in the system

diagnosis = repair


Hitting Set Tree Algorithm - ExampleCHAPTER 3. COMPUTING JUSTIFICATIONS 63

Figure 3.2: An Example of a Hitting Set Tree

J1 = A ! B, B ! D

A ! B

A ! "R.C

B ! D

A ! "R.C

J2 = A ! "R.C,"R.# ! D

!R." # D!R." # D

J !2 = A ! "R.C,"R.# ! D

bottom right hand successor to the node labelled with J ′2 and whose successor

edge is labelled with ∃R.> v D was generated by considering O \ S where

S = B v D, ∃R.> v D (∃R.> plus the label of the path to the root) and

noting that O \ S does not contain a justification for A v D. A fresh successor

node was therefore generated and labelled with the empty set, with the successor

edge label being set as ∃R.> v D.

When no more successor nodes can be generated the hitting set tree is com-

plete. At this point, all justifications for O |= η occur as labels of nodes in the

tree. Additionally, all minimal repairs (diagnoses) for O |= η are contained as

leaf-root paths in the tree.

3.3.3 Model Based Diagnosis Optimisations

The above description of a hitting set tree and illustrative example do not take

into consideration any optimisations. In order to achieve acceptable performance

it is necessary to consider two important optimisations: (1) Early Path Termina-

tion, and (2) Justification Reuse, which are detailed below:

Early path termination

In the unoptimised version of the algorithm a node can be extended with successor

edges provided there is an axiom in its label which does not already label one of the


O = A v B

B v D

A v ∃R.C∃R.> v D |= A v D

Source: M. Horridge:JusticationBased Explanation in Ontologies(PhD

Thesis)

Justication Scenarios

A user can be faced with the following situations:

Small number of small justications

, Easy and pleasant to inspect

Small number of large justications

, Better than alternatives

Large number of justications

/ Pretty hopeless with current mechanismsIdea: Find source of unsatisability


Root Unsatisability - Denitions

A root UC is a class whose unsatisability does not depend on anotherclass, otherwise it is a derived UC.

A derived UC for which there is some justication that is not a strictsuperset of a justication for another UC is a partial derived UC.

Root Unsatisable Class

A class A is a root unsatisable class if there is no justication J |= A v ⊥such that J is a strict superset of a justication for some otherunsatisable class.


Root Unsatisability - Approaches

Approaches:

1: compute all justications for each unsatisable class and apply thedenition → computationally often too expensive

2: heuristics for structural analysis of axioms


Debugging Unsatisable Classes in OWL Ontologies, Kalyanpur, Parsia, Sirin, Hendler,

J. Web Sem, 2005.

Root Unsatisability - Example

O =

B v ∃r .D (1)

B v ∀r .¬D (2)

A v B u C (3)

B v ¬C (4)

A v E (5)

A v ¬E u F (6)

|= A v ⊥J1 = 1, 2, 3J2 = 5, 6J3 = 3, 4

|= B v ⊥ J4 = 1, 2 root

partial

(J4 ⊂ J1)



O =

B v ∃r .D (1)

B v ∀r .¬D (2)

A v B u C (3)

B v ¬C (4)

A v E (5)

A v ¬E u F (6)

|= A v ⊥

J1 = 1, 2, 3J2 = 5, 6J3 = 3, 4

|= B v ⊥ J4 = 1, 2 root

partial

(J4 ⊂ J1)



O =

B v ∃r .D (1)

B v ∀r .¬D (2)

A v B u C (3)

B v ¬C (4)

A v E (5)

A v ¬E u F (6)

|= A v ⊥J1 = 1, 2, 3J2 = 5, 6J3 = 3, 4

|= B v ⊥ J4 = 1, 2 root

partial

(J4 ⊂ J1)



O =

B v ∃r .D (1)

B v ∀r .¬D (2)

A v B u C (3)

B v ¬C (4)

A v E (5)

A v ¬E u F (6)

|= A v ⊥J1 = 1, 2, 3J2 = 5, 6J3 = 3, 4

|= B v ⊥

J4 = 1, 2 root

partial

(J4 ⊂ J1)



O =

B v ∃r .D (1)

B v ∀r .¬D (2)

A v B u C (3)

B v ¬C (4)

A v E (5)

A v ¬E u F (6)

|= A v ⊥J1 = 1, 2, 3J2 = 5, 6J3 = 3, 4

|= B v ⊥ J4 = 1, 2

root

partial

(J4 ⊂ J1)



O =

B v ∃r .D (1)

B v ∀r .¬D (2)

A v B u C (3)

B v ¬C (4)

A v E (5)

A v ¬E u F (6)

|= A v ⊥J1 = 1, 2, 3J2 = 5, 6J3 = 3, 4

|= B v ⊥ J4 = 1, 2 root

partial

(J4 ⊂ J1)


Axiom Relevance

resolving justication requires to delete or edit axioms

ranking methods highlight the most probable causes for problems

methods:

frequency

syntactic relevance

semantic relevance


Repair Consequences

after repairing process, axioms have been deleted or modied

→ desired entailments may be lost or new entailments obtained(including inconsistencies!)

→ user can decide to preserve them


SPARQL Endpoint Support

Previously mentioned approaches are implemented in the ORE tool(http://ore-tool.net)

ORE supports using SPARQL endpoints

implements an incremental load procedure

knowledge base is loaded in small chunks:

count number of axioms by typepriority based loading proceduree.g. disjointness axioms have higher priority than class assertion axioms

uses Pellet incremental reasoning


Learning of OWL Class Descriptions on Very Large Knowledge Bases,

Hellmann, Lehmann, Auer, Int. Journal Semantic Web Inf. Syst, 2009

http://ore-tool.net

SPARQL Endpoint Support II

algorithm performs sanity checks, e.g. SPARQL queries which probefor typical inconsistent axiom sets

can fetch additional Linked Data

dierent termination criteria

overall:

ORE allows to apply state-of-the-art ontology debugging methods on a

larger scale than was possible previously

aims at stronger support for the web aspect of the Semantic Weband the high popularity of Web of Data initiative


DBpedia Live Demo

Inconsistency in DBpedia Live:

Individual: dbr:Purify_(album)

Facts: dbo:artist dbr:Axis_of_Advance

Individual: dbr:Axis_of_Advance

Types: dbo:Organisation

Class: dbo:Organisation

DisjointWith dbo:Person

ObjectProperty: dbo:artist

Range: dbo:Person


DBpedia Live Demo 2

Inconsistency in DBpedia in combination with WGS84 (Linked Data):

Individual: dbr:WKWS Facts: geo:long -81.76833343505859

Types: dbo:Organisation

DataProperty: geo:long Domain: geo:SpatialThing

Class: dbo:Organisation DisjointWith: geo:SpatialThing


OpenCyc Demo

Inconsistency in OpenCyc:

Individual: 'PopulatedPlace'

Types: 'ArtifactualFeatureType', 'ExistingStuffType'

Class: 'ArtifactualFeatureType'

SubClassOf: 'ExistingObjectType'

Class: 'ExistingObjectType'

DisjointWith: 'ExistingStuffType'


ORE - Screenshot


Related Tools

Swoopcan compute justications for unsatisability of classes and oers repairmodene-grained justication computation algorithm is incompletecan also compute justications for an inconsistent ontology, but doesnot oer repair mode in this casedoes not extract locality-based modules, which leads to lowerperformance for large ontologies

RaDONplugin for the NeOn toolkitoers a number of techniques for working with inconsistent orincoherent ontologiesallows to reason with inconsistent ontologies and can handle sets ofontologies (ontology networks)no ne-grained justications, no repair impact analysis

Pellintsearches for common patterns which lead to potential reasoningperformance problemsintegration in ORE planned


Related Tools II

PION and DION

developed in the SEKT project to deal with inconsistenciesPION is an inconsistency tolerant reasoner (four-valued paraconsistentlogic)DION oers the possibility to compute justications, but no repair

Explanation Workbench

Protégé plugin for reasoner requests like class unsatisability or inferredsubsumption relationscan compute regular and laconic justicationsmotivated the ORE debugging interfacecurrent version of Explanation Workbench does not allow to removeaxioms in laconic justications

RepairTab

supports the user in nding and detecting errors in ontologiesRepairTab uses a modied tableau algorithmshows inferences which can no longer be drawn after removing anaxiom (inspired ORE)


Outline






6 Enrichment

7 Repair



Classifi-cation/

Enrichment

Quality Analysis

Evolution / Repair

Search/ Browsing/

Exploration

Extraction

Storage/ Querying

Manual revision/

Authoring



Motivation

User Query Interfaces:

Knowledge BaseSpecic Interfaces

Facet-BasedBrowsing

Visual SPARQLQuery Builders

Question Answering


Motivation

User Query Interfaces:

Knowledge BaseSpecic Interfaces

Facet-BasedBrowsing

Visual SPARQLQuery Builders

Question AnsweringWhich tools for creating (SPARQL) queries

are end user friendly?


Strengths of Weaknesses of Query Interfaces

Easy to Use

Robust

Flexible Queries

Expressive

Knowledge Base Specific Facet-Based BrowsingVisual SPARQL Query Builders Question Answering


Faceted Browsing

Simple way to browse structured information

User starts with all resources and then drills down via facets

Multiple dimensions can be supported for facets, e.g. taxonomy,existence of properties, values of properties

Can be combined with text search: previously information was browsedeither via a xed classication scheme or text search (with the latterbeing dominant) facet based browsing allows a combination of both


Facete


Facete

Generic Facet-Based Browser

RDF properties are facets (sub-facets are supported)

Each facete serves as source for columns (table rendering), points ofinterest (map rendering)

JavaScript implementation - SPARQL queries are done by the client

Each SPARQL endpoint can serve as backend, no API needs to beimplemented


Question Answering - State of the art

1 Map natural language question to triple-based representation.

2 Match triple-based representation against RDF data.

Example:

Where did Abraham Lincoln die?

SELECT ?x WHERE

res:Abraham_Lincoln dbo:deathPlace ?x .

PowerAqua:

Triple representation:

〈state/place, die, Abraham Lincoln〉Ontology mappings:

〈Place, deathPlace, Abraham_Lincoln〉


Problem

Triples do not always provide a faithful representation of the semanticstructure of the question.

Thus more expressive queries cannot be answered.

Example 1:

Which cities have more than three universities?

SELECT ?y WHERE

?x rdf:type dbo:University .

?x dbo:city ?y .

HAVING (COUNT(?x) > 3)

PowerAqua (triple representation):〈cities, more than, universities three〉


Problem

Triples do not always provide a faithful representation of the semanticstructure of the question.

Thus more expressive queries cannot be answered.

Example 2:

Who produced the most lms?

SELECT ?y WHERE

?x rdf:type dbo:Film .

?x dbo:producer ?y .

ORDER BY DESC(COUNT(?x)) LIMIT 1

PowerAqua (triple representation):〈person/organization, produced, most lms〉


Goal

In order to understand a user question, we need to understand:

The wordsFind a mapping from natural language expressions to ontologyconcepts.

Abraham Lincoln → res:Abraham_Lincoln

died in → dbo:deathPlace

The semantic structureDetermine the triple structure as well as lters and aggregationfunctions (order by, count, etc.).

the most N → ODER BY DESC(COUNT(?n)) LIMIT 1

more than three N → HAVING (COUNT(?n) > 3)

Goal: an approach that combines both an analysis of the semanticstructure and a mapping of words to URIs


Templated-based question answering

1 Template generation (Understanding the semantic structure)Parse question to produce a SPARQL template that directly mirrorsthe structure of the question, including lters and aggregationoperations.

2 Template instantiation (Understanding the words)Instantiate SPARQL template by matching natural languageexpressions with ontology concepts using statistical entityidentication and predicate detection.


Example: Who produced the most lms?

1 SPARQL template:SELECT ?x WHERE

?y rdf:type ?c .

?y ?p ?x .

ORDER BY DESC(COUNT(?y)) LIMIT 1

?c CLASS [lms]?p PROPERTY [produced]

2 Instantiations:

?c = <http://dbpedia.org/ontology/Film>

?p = <http://dbpedia.org/ontology/producer>


Architecture

Natural Language Question

Semantic Representaion

SPARQL Query

Templates

Templates with URI slots

Ranked SPARQL Queries

Answer

LOD

Entity identification

Entity and Query Ranking

Query Selection

Resourcesand Classes

SPARQL Endpoint

Type Checkingand Prominence

BOA PatternLibrary

Properties

Tagged Question

Domain Independent Lexicon

Domain Dependent Lexicon

Parsing

Corpora?

!Loading

State

Process

Uses


Step 1: Template generation - Linguistic processing



1 Natural language question is taggedwith part-of-speech information.



2 Based on POS tags, lexical entriesare built on the y.

Lexical entries are pairs of:

tree structures(Lexicalized Tree Adjoining Grammar)

semantic representations(ext. Discourse Representation Structures)



3 These lexical entries, together withdomain-independent lexical entries,are used for parsing the question.



4 The resulting semanticrepresentation is translated into aSPARQL template.



domain-independent: who, the most

domain-dependent: produced/VBD, lms/NNS

SPARQL template 1:SELECT ?x WHERE

?x ?p ?y .

?y rdf:type ?c .





domain-independent: who , the most



?x ?p ?y .

?y rdf:type ?c .






domain-dependent: produced/VBD , lms/NNS


?x ?p ?y .

?y rdf:type ?c .


?c CLASS [lms]

?p PROPERTY [produced]






?x ?p ?y .


?p PROPERTY [lms]


Step 2: Template instantiation - Entity identication



1 For resources and classes:

Identify synonyms of the label using WordNet.Retrieve entities with a label similar to the slot labelbased on string similarities (trigram, Levenshtein,substring).



2 For property labels, the label isadditionally compared to naturallanguage expressions stored in theBOA pattern library.



3 The highest ranking entities arereturned as candidates for lling thequery slots.


BOA

The BOA pattern library is a repository of natural language representationsof Semantic Web predicates.Idea:

For each predicate P in a data repository (e.g. DBpedia), collect theset of entities S and O connected through P .

Search a text corpus (e.g. Wikipedia) for all sentences containing thelabels of S and O.

For all retrieved sentences, the natural language predicate is apotential pattern for P . The potential patterns are then scored by aneural network (e.g. according to frequency) and ltered.


BOA: Example

Predicate:http://dbpedia.org/ontology/subsidiary

RDF snippet:

<http://dbpedia.org/resource/Google>

<http://dbpedia.org/ontology/subsidiary>

<http://dbpedia.org/resource/YouTube> .

<http://dbpedia.org/resource/Google> rdfs:label `Google'@en .

<http://dbpedia.org/resource/YouTube> rdfs:label `Youtube'@en .

Sentences:

Google's acquisition of Youtube comes as online video is really startingto hit its stride.Youtube, a division of Google, is exploring a new way to get morehigh-quality clips on its site: nancing amateur video creators.

Patterns:

subsidiary: S's acquisition of O

subsidiary: O, a division of S


BOA

The use of BOA patterns allows us to match natural language expressionsand ontology concepts even if they are not string similar and not coveredby WordNet.Examples:

married to → http://dbpedia.org/ontology/spouse

was born in → http://dbpedia.org/ontology/birthPlace

graduated from → http://dbpedia.org/ontology/almaMater

write → http://dbpedia.org/ontology/author



Candidates for lling query slots:

?c CLASS [lms]

<http://dbpedia.org/ontology/Film>

<http://dbpedia.org/ontology/FilmFestival>

. . .

?p PROPERTY [produced]

<http://dbpedia.org/ontology/producer>

<http://dbpedia.org/property/producer>

<http://dbpedia.org/ontology/wineProduced>

. . .


Step 3: Query ranking and selection



1 Every entity receives a scoreconsidering string similarity andprominence (roughly how often itoccurs in the knowledge base).

2 The score of a query is thencomputed as the average of thescores of the entities used to ll itsslots.



3 In addition, type checks areperformed:For all triples ?x rdf:type

<class>, all query tripels ?x p e

and e p ?x are checked w.r.t.whether domain/range of p isconsistent with <class>.(If not, the query is rejected.)



4 Of the remaining queries, the onewith highest score that returns aresult is chosen to retrieve ananswer.



SELECT ?x WHERE

?x <http://dbpedia.org/ontology/producer> ?y .

?y rdf:type <http://dbpedia.org/ontology/Film> .


Score: 0.7592425075864263

SELECT ?x WHERE

?x <http://dbpedia.org/ontology/film> ?y .


Score: 0.6264001353183296

SELECT ?x WHERE

?x <http://dbpedia.org/ontology/producer> ?y .

?y rdf:type <http://dbpedia.org/ontology/FilmFestival>.


Score: 0.6012584940627768


Evaluation set-up

Question set: 39 DBpedia training questions from QALD-1

The other 11 questions rely on namespaces which were notincorporated in predicate detection (FOAF and YAGO).

POS tags were idealized, in order to exclude tagging errors.

Evaluation measures:

Precision =number of correct resources returned by system

number of resources returned by system

Recall =number of correct resources returned by systemnumber of resources in gold standard answer


Results

Of the 39 questions. . .

5 could not be parsed due to unknown syntactic constructions oruncovered domain-independent expressions

19 were answered exactly as required by the benchmark (with precisionand recall 1.0)

another 2 are answered almost correctly (with precision and recallgreater than 0.8)

Mean precision: 0.61Mean recall: 0.63F-measure: 0.62


Discussion: Main sources of error

Incorrect templatesTemplate structure does not coincide with structure of the data:

When did Germany join the EU?res:Germany dbp:accessioneudate ?x .

Predicate detection fails

inhabitants 9 dbp:population, dbp:populationTotalowns 9 dbo:keyPerson

higher 9 dbp:elevationM

Wrong query is selected

Who wrote The pillars of the Earth?res:The_Pillars_of_the_Earth_(TV_Miniseries) dbo:writer ?x .

res:The_Pillars_of_the_Earth dbo:author ?x .


Conclusion

Two-step approach:1 Build templates that capture the semantic structure of a question.

map complex expressions (quantiers, comparatives, superlatives, etc.)to aggregation functions

2 Instantiate templates mapping natural language expressions to URIs.

BOA patterns for cases where string similarity and WordNetare not sucient


Outlook

Current work: Entity identication should take into account whethercandidate entities actually have any connection in the dataset.

Future work: Make templates less rigid and determine the exact triplestructure on the basis of data exploration.

The created template structure does not always coincide with how thedata is actually modelled.

Considering all possibilities of how the data could be modelled leads toa huge amount of templates (and even more queries) for one question.


Links

Web: http://aksw.org/Projects/AutoSPARQL

Demo: http://autosparql-tbsl.dl-learner.org

BOA: http://boa.aksw.orgDaniel Gerber and Axel-Cyrille Ngonga Ngomo: Bootstrapping theLinked Data Web. In: Proceedings of the Web Scale Knowledge

Extraction Workshop (WekEx), ISWC 2011.

QALD: http://www.sc.cit-ec.uni-bielefeld.de/ild/

http://aksw.org/Projects/AutoSPARQL

http://autosparql-tbsl.dl-learner.org

http://boa.aksw.org

http://www.sc.cit-ec.uni-bielefeld.de/ild/

The End

Jens [email protected]/Uni Leipzig

GeoKnow

http://geoknow.eu


http://geoknow.eu

The Linked Data Lifecycle

Education

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