Doing Clever Things With the Semantic Web

Mathieu d’AquinKnowledge Media Institute, the Open University

The Semantic Web

Using the Web to publish, share and exploit information/knowledge

From machines to machines

Using graph-based data modeling, knowledge representation (ontologies) and reasoning

Linked Data

As set of principles and technologies for a Web of Data– Putting the “raw” data

online in a standard representation (RDF)

– Make the data Web addressable (URIs)

– Link to other Data

http://lucero-project.info/lb/what-is-linked-data/

http://linkeddata.org

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<rdf:RDF> <owl:Ontology rdf:about=""> <owl:imports rdf:resource="http://usefulinc.com/ns/doap#"/> </owl:Ontology> <j.1:Organization rdf:ID="KMi"> <rdfs:comment rdf:datatype="http://www.w3.org/2001/XMLSchema#string" >The Knoledge Media Institute of the Open University, Milton Keynes UK</rdfs:comment> </j.1:Organization> <j.1:Document rdf:ID="KMiWebSite"> …

FOAF

DCRSS TAP

WORDNET

NCI Galen

Music

…… …

…

…

UoD

The Semantic Web

Semantic Web Applications

Doing Clever Things With the Semantic Web

Intelligent AgentSmart FeaturesClever Things…

Knowledge/Problem Solving

Methods

What I Want to Talk About

Using the Semantic Web as A Knowledge Base– KMi Watson and Finding Ontologies

• Doing More with Links– Exploring the Web of Data

• Back to the Future– AI + Linked Data = Semantic Web?

Using the Semantic Web

Dynamically retrieving, exploiting and combining relevant semantic resources from the Semantic Web

Need for a Gateway to the Semantic Web

KMi Watson

Architecture

Interface

http://watson.kmi.open.ac.ukWatson: More than a Semantic Web Search Engine, Semantic Web Journal

Watson as a Service

Providing Web accessible APIs to a collection of online ontologies and semantic data sources

Example Application: Ontology Construction

Chose an entity to search

Get entities from online ontologies

Integrate statements Into the edited ontology

Reusing Knowledge from the Semanrtic Web with the Watson Plugin, Demo at ISWC 2008

Concept Relation Discovery

ka2.rdf

Researcher AcademicStaff

Sem

anti

c W

eb

Researcher

AcademicStaff

ISWC SWRCHam SeaFood

Sem

anti

c W

eb

HamSeaFood

Meat

Meat

SeaFood

Agrovoc NALT

pizza-to-go

wine.owl

NALT

Exploring the Semantic Web as Background Knowledge for Ontology Matching, Journal of Data Semantics

PowerAqua: Question Answering

From Semantic Web Research to Linked Data Applications

Watson as a platform to research applications and techniques on top of semantic web resources

But how can the Semantic Web be exploited and used in real-world application?

Starting from what we know best…

Applying Linked Data

The Open University is the largest University in the UK, where all the courses are realized at a distance

Creating the first University Linked Data platform: data.open.ac.ukDemonstrate the value of the technology and push the research through real-world scenarios

data.open.ac.uk

Applications

Resource Discovery

Mobile and Personal Semantics

ResearchExploration

Social

Example application: Finding relevant resources

Zablith et al, LinkedLearning 2011

Data as Web resources, accessible everywhere

See also: Zablith et al., COLD 2011

http://people.kmi.open.ac.uk/mathieu/about/discobro-discovering-linked-datat-resources-while-browsing/http://discovery.ac.uk/developers/competition/

Supporting Researchers: The Reading Experience Database

http://www.open.ac.uk/Arts/reading/

40,000 accounts of somebody reading something at some time in some place

Used by researchers in literature and history to explore research hypotheses

Experience

Person

Document

EventLocation

City Countrydate: Date

subClassOf

subClassOf

locatedIn

readerInvolved

textInvolved givesBackgroundTo

title: Stringdescription: Stringpublished: Date

creator/editor

providesExcerptFor

occupation

religion

originCountry

gender

LinkedEvent Ontology

CITO Citation Ontology

Dublin Core

FOAF

DBPedia

Back to the Future

The Semantic Web is both a vision and a reality

Making the Web more than a network of documents: the biggest, most distributed knowledge base ever

What could AI do with such a knowledge base?

Linked Data MiningFinding unexpected patterns in the use of the distributed data graph

Linked Data Mining: ExampleUsing Formal Concept Analysis + Reasoning to build a hierarchy of questions a linked dataset can answer

Use statistical metrics to identify the ones that are most likely to be interesting

http://lucero-project.info/lb/2011/06/what-to-ask-linked-data/

Extracting Relevant Questions to an RDF Dataset Using Formal Concept Amalysis at KCAP 2011

ReasoningTo analyze and understand raw data in relation with online resources

Example: Online personal information management

HTTP Ontology

Web Site Information

Location Information

Online Activities Ontology

Parameters and Website

info.

Personal Information

Trust Model

Enriched with linked data

www.youtube.com

Google Services

Entertainment Websites

Video Hosting

google

subsediaryOf

Company

type

Video sharing

subject/category

parent

www.google-analytics.com

Web Analytics

developer

subject/category

www.google.com

owner

Internet Search Engine

Search Engine

Web Search Engine

DBpedia freebase

Basic processing/analysis

Requests by User Agents

Requests by time of day

Interests

Trust

http://uciad.info

http://uciad.info

Understanding knowledge representation and data modeling

The Semantic Web also represents a very large, collaborative base of formally represented knowledge

This can also be mined, to discover things about knowledge representation and data modeling

Ontologies on the Semantic Web

Number of entities

Domain covered

Underlying description logic

Relationships between ontologies

DOOR: Towards a Formalization of Ontology Relations at KEOD 2009

Detecting versions of ontologiesWhen published on the Web, the information about the evolution of ontologies is lost

Using URI patterns to find candidate versions of ontologieshttp://loki.cae.drexel.edu/wbs/ontology/2003/10/iso-metadata http://loki.cae.drexel.edu/wbs/ontology/2004/01/iso-metadata

Applying machine learning algorithms (SVM, Naïve Bayes and Decision tree to recognize chains of versions of ontologies

Obtained 90% Precision (SVM)

Collected thousands of ontology version sequences to be analysed

For example, distribution of similarity in version and non-version ontologies (right)

Allocca at ESWC 2011

Agreement/Disagreement between ontologies

Ontologies are knowledge artifacts, they express opinions and beliefs and contradict each othersAssessing (dis)agreement in ontologies is very useful to understand how to combine knowledge from different sources

Assessing Statements related to SeaFood

Nb1: #ontologies in which the statement appears.Nb2: #ontologies containing entities matching the subject and object of the statement.

a: global agreement, d: global disagreement, cs: consensus, ct: controversy

21 different ontologies with a SeaFood concept

Agreement

Disagreement

Formally Measuring Agreement and Disagreement in Ontologies at K-CAP 2009

http://uciad.info

SeaFood disjointWith Meat

SeaFood subClassOf Meat

Vegan subClass Vegetarian

Using consensus to assess an ontology(a new NeOn toolkit plugin

AKT PortalThe brighter the blue the higher the positive consensus (higher agreement)

The brighter the red the lower the negative consensus (higher disagreement)

Dark = controversy: no clear cut between disagreement and agreement

Example: The statements attached to the class Employee are controversial: some ontologies agree, others disagree (often due to alternative representations of roles)

Visualizing Consensus with Online Ontologies to Support Quality in Ontology Development at ONTOQUAL@EKAW 2010

So my point is…The Semantic Web is a fantastic open field for AI

It is going to become omnipresent, hidden, personal

Exploring, Exploiting and Excavating the Semantic Web for

Research in technology (creating it and studying it)

Research in other areas

Everyday tasks

Still, after 10 years of research, represent new directions for many fields of the AI community, with their own issues, challenges and applications

Thank You!

More at:

http://people.kmi.open.ac.uk/mathieu

m.daquin@open.ac.uk

@mdaquin