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


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


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


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


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


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


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


Capability Cases

http://www.amazon.com/exec/obidos/tg/detail/-/0321205766/qid=1122653418/sr=8-1/ref=pd_bbs_1/104-2961096-8219145


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


“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

PDF

Portals

Terminology Management


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


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


From a Gallery of Ontology-enabled Capability Cases (1)

Context Aware Retriever Answer Engine


From the Gallery of Ontology-enabled Capability Cases (2)

Personalized Newsletter Concept Based Search


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


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



Semantic Executable

Models


Personal TV Recommender

A slide from a past presentation

http://del.icio.us/CapabilityCases/Recommender

TopQuadrant


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”


TopQuadrant


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


TopQuadrant


Capability Case: Semantic Multi-Faceted Search

CapabilityCase:Semantic Multi-Faceted Searchhttp://del.icio.us/CapabilityCases/SemanticMultiFacetedSearch

Info

rmal

Human

Form

al

Machine



Semantic Executable

Models


Museum Finland Indiana’s Learning Resource Clearinghouse


http://del.icio.us/CapabilityCases/SemanticMultiFacetedSearch

TopQuadrant


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/


http://museosuomi.cs.helsinki.fi/

TopQuadrant


MuseumFinland

http://www.cs.helsinki.fi/group/seco/museums/tutorial/step1.html


TopQuadrant


Capability Case: Expert Locator

CapabilityCase:Expert Locator

http://del.icio.us/CapabilityCases/ExpertLocator

Info

rmal

Human

Form

al

Machine



Semantic Executable

Models


Boeing Expert Locator


http://del.icio.us/CapabilityCases/ExpertLocator

TopQuadrant


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”



What is currently happening?Museum Twitter

http://museumpods.com/museums_twitter.html


What is currently happening?Top Museums on Twitter

http://www.museummarketing.co.uk/?p=132


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


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.


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.


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.


“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


“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

http://www.nwo.nl/nwohome.nsf/pages/NWOP_5XSKYG

http://www.nwo.nl/nwohome.nsf/pages/NWOP_5XSKYG

http://www.tue.nl/

http://www.rijksmuseum.nl/

http://www.rijksmuseum.nl/

http://www.telin.nl/


“Semantic Web” is happening in the Arts: The CIDOC CRM Initiative

http://cidoc.ics.forth.gr/


“Semantic Web” is happening in the Arts: CIDOC Ontology Example in TopBraid Composer


“Semantic Web” is happening in the Arts: CIDOC Ontology Example – Physical Thing


What I consider doing

Artist OntologyArt Works OntologyMuseum Tweets OntologyMaking ontologies available at the domain name www.artsweb.us

http://www.artsweb.us/


Museum Tweets OntologySpreadsheet from http://www.museummarketing.co.uk


Museum Tweets Ontology:Arizona Museums on Twitter


Thank youRalph HodgsonE-mail: [email protected]: @ralphtq, @meddera

mailto:[email protected]

Envisioning Semantic Web Technology Solutions for the Arts

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