Explanation for the Semantic Web

Explanation for the Semantic WebRakebul Hasan

04/09/2023Wimmics Seminar - 2

Explanation in the Semantic Web

What’s been done (existing work)

What’s ahead (perspective)


What is explanation and why do we need it?


Tim Berners-Lee was born in Londonhttp://www.academiawiki.com/



London is in Englandhttp://www.geolocationwiki.com/




Computer Scientists are Scientistshttp://www.vocabularywiki.com/





Tim Berners-Lee is a Computer Scientisthttp://www.academiawiki.com/






England is part of United Kingdomhttp://www.geolocationwiki.com/







TimBernersLee

London

England

birthPlace

isPartOf

UnitedKingdom

isPartOf

ComputerScientist

Scientistrdfs:subClassOf

rdf:type


TimBernersLee

London

England

birthPlace

isPartOf

UnitedKingdom

isPartOf

ComputerScientist

Scientist

rdfs:subClassOfrdf:type


TimBernersLee

London

England

birthPlace

isPartOf

UnitedKingdom

isPartOf

ComputerScientist

Scientist



TimBernersLee

London

England

birthPlace

isPartOf

UnitedKingdom

isPartOf

ComputerScientist

Scientist


rdf:type

isPartOf


TimBernersLee

London

England

birthPlace

isPartOf

UnitedKingdom

isPartOf

ComputerScientist

Scientist


rdf:type

isPartOf

birthPlace birthPlace


TimBernersLee

London

England

birthPlace

isPartOf

UnitedKingdom

isPartOf

ComputerScientist

Scientist


rdf:type

isPartOf

birthPlace birthPlace

Scientists born in United Kingdom?

Answer: Tim Berners-Lee


Tim Berners-Lee is a Scientist

Tim Berners-Lee was born in United Kingdom







Tim Berners-Lee was born in England


“Oh, yeah?” button to support users in assessing the

reliability of information encountered on the Web

- Tim Berners-Lee


What’s been done


What is explained?

Who are the targets?

How explanations are presented?

What is explained?


• Information manipulation steps

•Information manipulation operations

• Proof tree of derivations

• Provenance information such as How, When, Who,

Where

Target


• Human users

•Natural language explanation

•Graphical explanation

• Software agents

•Machine readable descriptions


How explanations are presented?

How explanations are represented for machines?

How human users interact with explanations?

Trust

Presentation


WIQA [Bizer, 2007]

Presentation


KiWi [Kotowski and Bry, 2010]

Presentation


Explaining entailments in OWL ontologies [Horridge et al., 2008]

Presentation


Inference Web [McGuinness et al. (a), 2003] [McGuinness et al. (b), 2006] [McGuinness et al. (c), 2008]

Presentation


EASD/KOIOS [Forcher et al., 2010]

Metadata representation


• Proof trees for answers

• Operations used to compute answers

• Different types of provenance information

• Models for how explanations should be presented to

human users

• Trust related information



• Proof Markup Language (PML) Ontology

•Proof interlingua

•Justifications: information manipulation steps and

operations

•Provenance information

•Trust information



Accountability In RDF (AIR) [Kagal et al., 2011]



• KOIOS

•KOIOS Process Language (KPL) for describing the

behavior of KOIOS problem solver

•The Mathematical Graph Language (MGL) for

transforming the process model to a graph based view.

•VGL for describing the visualization model



EXPL: WIQA describes its explanation trees (parts and

subparts of an explanation) using the Explanation

(EXPL) Vocabulary

Interaction


• Navigation

• Follow-up

Interaction


Trust


• Inference Web

•PML Trust vocabulary

•Trust explanation

Trust



What’s ahead

Infrastructure


• Accommodating common data publishing principle

•Publishing explanation metadata along with data

using linked data principles

• Addressing heterogeneous and distributed nature of the

Web promoting interoperability

•W3C PROV-DM data model as an interchange data

model

Target


• Level of user expertise should be taken into account

while providing explanation

•User profiling

What is explained?


• Semantic Web applications use distributed

interconnected data in their reasoning process

•Explaining network of data used in the reasoning

process, flow of information

• How explanations exposing problem solving methods

influence security and confidentiality?



• Blank nodes are not good

• Granularity

• Provenance

• Interoperability


r4ta:Justification

prov:Trace

prov:Account

prov:ProvenanceContainer

prov:Entity

r4ta:Assertion

r4ta:InferredAssertion

r4ta:DirectAssertion

r4ta:InferenceRule

prov:Plan

r4ta:inferredByRule

prov:hadPlan

prov:Activityr4ta:justifies

prov:traceTo

prov:qualifiedTrace

r4ta:antecedent prov:specializationOf

Presentation


• User expertise

•What’s useful and what’s overwhelming?

•Context-aware data consumption

Presentation


Summarization

Interaction


• What kind of interactions are useful need to be

understood

• How to interact established trust?

Trust


• How explanation influence trust in the Semantic Web?

• How to capture established trust and reason over it?

• Explaining trust itself

Trust


“If someone trusts a large company such as IBM, and IBM

brands other companies as ethical, then the person will

trust those companies, too”

– Tim Berners-Lee,

Weaving the Web


<geo:london> <voc:isPartOf> <geo:England>.<geo:london> <rdfs:label> “London”@en.<geo:london> <voc:population> “7825200”^^xsd:integer.<geo:london> <voc:lat> “51.50853”^^xsd:decimal.<geo:london> <voc:long> “-0.12574”^^xsd:decimal.

geo:about_london



geo:about_london

trust

endorses



geo:about_london

trust

endorses

• Explicit endorsements• Incoming links: owl:sameAs



geo:about_london

trust

endorses

• Explicit endorsements• Incoming links: owl:sameAs

• Explanation of trust and endorsement network


Please see the research report RR-7974 for the list of references:

http://hal.inria.fr/hal-00702277

Thank you

Explanation for the Semantic Web

Education

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