Envisioning Semantic Web Technology Solutions for the Arts
Transcript of Envisioning Semantic Web Technology Solutions for the Arts
Information—Integration—Intelligence Solutions
“Envisioning Semantic Web Technology Solutions for the Arts”
Semantic Web and CIDOC CRM WorkshopRalph Hodgson, CTO, TopQuadrant
National Museum of the American IndianWashington, DC 20013
October 25, 2009
“What can Semantic Web Technologies do for the Arts”
StrategyNeed Solution Outcome
http://www.semuse.org/index.php?title=Semantic_Web_and_CIDOC_CRM_Workshop#Morning_Lightning_Talks
© Copyright 2009 TopQuadrant Inc. Slide 1
IntroductionsRalph Hodgson
co-founder and CTO of TopQuadrant, first US-based company specializing in semantic technologyPrior to starting TopQuadrant in 2001, held executive consulting positions at IBM Global Services as a founding member of Portal Practice and Object Technology Practice. Recent books: Adaptive Information, published by John Wiley in 2004, and Capability Cases: A Solution Envisioning Approach, published by Addison-Wesley in July 2005. In my spare time, a Pastel Artist and Sculptor
© Copyright 2009 TopQuadrant Inc. Slide 2
The universe of ‘relevant technologies’ is large and expanding fast
Search
Document Management
System
Content Management
SystemData
WarehouseBusiness
Applications
OLAP
Intelligent Agents
Semantic Web
Customer Data
Knowledge Bases
© Copyright 2009 TopQuadrant Inc. Slide 3
Key Benefits of Semantic Technology
Information IntegrationMapable terms to build consistent & extensible vocabularies.Integrate models with both structured and unstructured data
Search and AnalysisSemantic relationships between data enable powerful queries that leverage knowledge organized by people to deliver specific answers in a highly scalable fashionNon-programmers can connect , search and analyze data
Application Longevity and Flexibility Future-proof applications (30, 50 100 years) by enabling knowledge workers to participate in model-based application development
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Semantic Web Layer CakeOWL = Web Ontology Language
– A language for describing a domain of interest
– Classes, Instances and properties of things, relationships between things,
– A standard defined by the World-Wide Web Consortium (W3C)
How does it relate to XML?– OWL can be serialized in XML and N3– OWL is built on the Resource
Description Framework (RDF)– OWL constructs allow us to say
things that XML Schema does not allow
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Why OWL - the Ontology Web Language?
XML is document-based not model-basedContainer Hierarchies - weak support for relationshipsWeak support for aggregation (combining documents)Schema Limitations
UML is Object-BasedRestricted Type SystemWeak on RelationshipsWeak notion of identityMetamodel (Schema) is in a different language
OWL is Set-BasedExpressive Type SystemStrong on RelationshipsStrong notion of identityGraphs not TreesMetamodel is in the same language
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Semantic Web Key Idea # 1 –“Think Triples”: Subject Predicate Object
Museum CulturalCollection
hasCollection
CulturalCollection Artifact
hasArtifact
Artifact HistoryPeriod
fromPeriod
ArtifactdonatedBy
Artifact PartyownedBy
Party
Subject ObjectPredicate
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Semantic Web Key Idea # 2 –Identifiers not Names (“Everything has a URI”)
Collection SalishNationhasArtifactOf
Subject ObjectPredicate
Collection HopiNationhasArtifactOf
mnai:Collection mnai:hasArtifactOf nai:SalishNation
mnai:Collection mnai:hasArtifactOf nai:HopiNation
+
Statements from different sources but same URIs means more information about the same things
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Capability Cases
© Copyright 2009 TopQuadrant Inc. Slide 9
Solution Envisioning Method
.
http://www.capabilitycases.org
Solution Envisioning with Capability Cases
Innovation happens with the creative interplay of solution ideas with business challenges, possibilities and opportunities
© Copyright 2009 TopQuadrant Inc. Slide 10
“Quadrants of Meaning”
Info
rmal
Human
Form
al
Machine
Textual Descriptions
Semantic Descriptions
Semantic Executable
Models
Syntactical Consensus
Modal Logics
Taxonomy
DL
FOL OWL-2Thesaurus
UML
OWL-Lite
MDA
ER
RDFS
CG
Topic Maps OWL
XML
HTML
Code
Rules
Portals
Terminology Management
© Copyright 2009 TopQuadrant Inc. Slide 11
Semantic Technology Capability Cases
Info
rmal
Human
Form
al
Machine
Ontology Driven Information Retriever
Semantic Multi-Faceted Search
Concept-Based Search
Expert Locator
Semantic Data
Integrator
Product Design AssistantSemantic
Web Services Composer
Information Aggregator
Semantic Data Registry
Application Integrator
Recommender
Semantic WorkplaceGenerative
Documentation
Context-Aware Retriever
Semantic Portal
Semantic Web Server
Navigational Search
Answer Engine
Connection and
Pattern Explorer
© Copyright 2009 TopQuadrant Inc. Slide 12
Solution Envisioning Workshop
A Gallery of Capabilities …
… until we see what is possible.”
“We never know exactly what we want …
Shared vision Shared understanding Shared memory
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From a Gallery of Ontology-enabled Capability Cases (1)
Context Aware Retriever Answer Engine
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From the Gallery of Ontology-enabled Capability Cases (2)
Personalized Newsletter Concept Based Search
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Some Application Areas of Semantic Technology
Content managementPersonalized InformationRepurposingNews feedsMarkup
Knowledge managementConcept-Based SearchContext-Aware RetrievalExpert LocatorsCollaboration
Semantic InteroperabilityData IntegrationInformation InferencingWeb Services Discovery and Composition
AdvisorsDesign AssistantsMatchmakersRecommendersMediators
1
2
3
4
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The “Meaning Quadrants” –Mapping Capability Cases
Info
rmal
Human
Form
al
Machine
Ontology Driven Information Retriever
Semantic Multi-Faceted Search
Concept-Based Search
Expert Locator
Semantic Data
Integrator
Product Design AssistantSemantic
Web Services Composer
Information Aggregator
Semantic Data Registry
Application Integrator
Recommender
Semantic WorkplaceGenerative
Documentation
Context-Aware Retriever
Semantic Portal
Semantic Web Server
Navigational Search
Answer Engine
Connection and
Pattern Explorer
TopQuadrant
© Copyright 2001 -2005 TopQuadrant Inc., “Solution Envisioning for Knowledge Enablement”, slide 17
Capability Case: Recommender
CapabilityCase:Recommender
http://del.icio.us/CapabilityCases/Recommender
Info
rmal
Human
Form
al
Machine
Textual Descriptions
Semantic Descriptions
Semantic Executable
Models
Syntactical Consensus
Personal TV Recommender
A slide from a past presentation
TopQuadrant
© Copyright 2001 -2005 TopQuadrant Inc., “Solution Envisioning for Knowledge Enablement”, slide 18
Capability Case: RecommenderSolutionStory: Personalized TV (PTV)
Filter information for people needing to monitor and assess large volumes of data for relevance, volatility or required response. The volume of targeted information is reduced based on its relevance according to a role or interest of the end user. Sensitive information is filtered according to the "need to know".
Personal TV Advisor uses a combination of model-based case based reasoning (CBR) and collaborative filtering technology to identify relevant information. Users set up their initial profiles and preferences based on the categories in a model of the entertainment domain. System continuously improves and refines its program recommendations learning from the feedback of the individual users (thumbs up/thumbs down function), as well as others who have similar tastes. Content can be delivered through a portal or via wireless interface.
“Personalized News and TV Program Guide”
A slide from a past presentation
TopQuadrant
© Copyright 2001 -2005 TopQuadrant Inc., “Solution Envisioning for Knowledge Enablement”, slide 19
Recommender Systems
Current ApproachesCollaborative FilteringCase-Based Reasoning
Future ApproachesSemantic Web TechnologiesInferencingRecommendation Engines
www.cri.haifa.ac.il/index.html?http://www.cri.haifa.ac.il/events/2005/recommender.htm
A slide from a past presentation
TopQuadrant
© Copyright 2001 -2005 TopQuadrant Inc., “Solution Envisioning for Knowledge Enablement”, slide 20
Capability Case: Semantic Multi-Faceted Search
CapabilityCase:Semantic Multi-Faceted Searchhttp://del.icio.us/CapabilityCases/SemanticMultiFacetedSearch
Info
rmal
Human
Form
al
Machine
Textual Descriptions
Semantic Descriptions
Semantic Executable
Models
Syntactical Consensus
Museum Finland Indiana’s Learning Resource Clearinghouse
A slide from a past presentation
TopQuadrant
© Copyright 2001 -2005 TopQuadrant Inc., “Solution Envisioning for Knowledge Enablement”, slide 21
CapabilityCase: Semantic Multi-Faceted SearchSolutionStory: MuseumFinland
A semantic portal for Finnish museums to publish their collections together on the Semantic WebA “Semantic Web” research project – web publishing
From 3/2002 to 3/2004Public pilot version from March 8, 2004:http://museosuomi.cs.helsinki.fi/
The Vision:Global View to Distributed Collections
One seamless national collection (virtually)”Museums in Finland” -> ”Museum of Finland”
Intelligent Services to End-UsersSearch: Concept-Based Information RetrievalBrowsing: Semantically Linked Contents
Easy Content Publication for Museums
Adapted from: Mirva Salminen, University of Helsinki, Helsinki Institute for Information Technology (HIIT) Semantic Computing Research Group, www.cs.helsinki.fi/group/seco/
A slide from a past presentation
TopQuadrant
© Copyright 2001 -2005 TopQuadrant Inc., “Solution Envisioning for Knowledge Enablement”, slide 22
MuseumFinland
http://www.cs.helsinki.fi/group/seco/museums/tutorial/step1.html
A slide from a past presentation
TopQuadrant
© Copyright 2001 -2005 TopQuadrant Inc., “Solution Envisioning for Knowledge Enablement”, slide 23
Capability Case: Expert Locator
CapabilityCase:Expert Locator
http://del.icio.us/CapabilityCases/ExpertLocator
Info
rmal
Human
Form
al
Machine
Textual Descriptions
Semantic Descriptions
Semantic Executable
Models
Syntactical Consensus
Boeing Expert Locator
A slide from a past presentation
TopQuadrant
© Copyright 2001 -2005 TopQuadrant Inc., “Solution Envisioning for Knowledge Enablement”, slide 24
CapabilityCase: Expert LocatorSolutionStory: Boeing Expert Locator
Provide users with convenient access to experts in a given area who can help with problems, answer questions, locate and interpret specific documents, and collaborate on specific tasks. Knowing who is an expert in what can be difficult in an organization with a large workforce of experts. Expert Locator could also identify experts across organizational barriers.
Boeing has a large workforce of experts making it hard to find the right person. This web-based system returns details on potentially appropriate experts. The Boeing technical thesaurus was harnessed to create expert profiles. Boeing Technical Libraries already had made a considerable investment to develop by hand a technical thesaurus in the form of a semantic network. It incorporates 37,000 concepts with an additional 19,000 synonym concept names, and 100,000 links including broaderTerm, narrowerTerm, and relatedTerm.
“Exploiting a Thesaurus-Based Semantic Net for Knowledge-Based Search”
A slide from a past presentation
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What is currently happening?Museum Twitter
http://museumpods.com/museums_twitter.html
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What is currently happening?Top Museums on Twitter
http://www.museummarketing.co.uk/?p=132
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Musing the possibilities of Semantic Web for the Art World
If we had ontologies ofGalleries, Museums, Cultural Collections, Private CollectionsExhibitions and EventsArtists and their Art WorksRSS and Tweet feeds
We could knowWhat is being shown where and whenWhere is this specific Art Work nowWho owns this Art WorkWhat influenced this Art WorkWhere was it painted – show me that on the WebDo I need to go to this exhibitionCan see the same some where nearer
© Copyright 2009 TopQuadrant Inc. Slide 28
Arts World Capability Cases (1 of 3)Artwork Recommender - semantics-driven
recommendations based on user profiles, feedback and collaborative filtering
EmergingArtistLocator – provides access to news, galleries and personal places/blogs of artists that are up and coming
MuseumTweetsAggregator– aggregates information from tweets making sense of what is happening, where it is happening, and what is of potential interest on a personal basis.
ArtTimeMachine– explores the life of an individual artist, connecting artworks to where they are now, who owns them and where they were created.
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Arts World Capability Cases (2 of 3)
Virtual Docent- personalized virtual museum tours based on user profiles, feedback and collaborative filtering.
Artist-SpecificVirtual Docent- personalized virtual museum tours of a specific artist across multiple museums and galleries, based on user profiles, feedback and collaborative filtering.
LostArtWorkMinder – provides a place for potential recovery of art works by allowing news, events and other information to be interpreted and aggregated.
© Copyright 2009 TopQuadrant Inc. Slide 30
Arts World Capability Cases (3 of 3)
iArt – an iTunes-like application for provisioning art on the web
ArtOnDemand – personalized web-based provisioning of Art to homea, work-places, and public places.
© Copyright 2009 TopQuadrant Inc. Slide 31
“Semantic Web” is happening in the Arts: The CHIP Project (1 of 2)
The goals of the CHIP project are to demonstrate how novel Semantic Web technologies can be deployed to enrich the Rijksmuseum vocabularies and providing semantic browsing, searching and semantic recommendations; andhow personalization and user modeling techniques can be explored to enhance users’ experiences both on the museum Web site and in the physical museum space.
http://www.chip-project.org/index.html
© Copyright 2009 TopQuadrant Inc. Slide 32
“Semantic Web” is happening in the Arts: The CHIP Project (2 of 2)
The CHIP (Cultural Heritage Information Presentation) project is funded by Dutch Science Foundation NWO-CATCH (Continuous Access to Cultural Heritage) program. This work is a collaboration between the Technical University Eindhoven, the Rijksmuseum Amsterdam and the Telematica Institute.
http://www.chip-project.org/index.html
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“Semantic Web” is happening in the Arts: The CIDOC CRM Initiative
http://cidoc.ics.forth.gr/
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“Semantic Web” is happening in the Arts: CIDOC Ontology Example in TopBraid Composer
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“Semantic Web” is happening in the Arts: CIDOC Ontology Example – Physical Thing
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What I consider doing
Artist OntologyArt Works OntologyMuseum Tweets OntologyMaking ontologies available at the domain name www.artsweb.us
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Museum Tweets OntologySpreadsheet from http://www.museummarketing.co.uk
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Museum Tweets Ontology:Arizona Museums on Twitter
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Thank youRalph HodgsonE-mail: [email protected]: @ralphtq, @meddera