Envisioning Semantic Web Technology Solutions for the Arts

Transcription

1 Information Integration Intelligence Solutions Envisioning Semantic Web Technology Solutions for the Arts Semantic Web and CIDOC CRM Workshop Ralph Hodgson, CTO, TopQuadrant National Museum of the American Indian Washington, DC October 25, 2009 What can Semantic Web Technologies do for the Arts Need Strategy Solution Outcome

2 Ralph Hodgson Introductions co-founder and CTO of TopQuadrant, first US-based company specializing in semantic technology Prior 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 In my spare time, a Pastel Artist and Sculptor Copyright 2009 TopQuadrant Inc. Slide 1

3 The universe of relevant technologies is large and expanding fast Knowledge Bases Semantic Web Intelligent Agents Search Document Management System Content Management System OLAP Data Warehouse Customer Data Business Applications Copyright 2009 TopQuadrant Inc. Slide 2

4 Key Benefits of Semantic Technology Information Integration Mapable terms to build consistent & extensible vocabularies. Integrate models with both structured and unstructured data Search and Analysis Semantic relationships between data enable powerful queries that leverage knowledge organized by people to deliver specific answers in a highly scalable fashion Non-programmers can connect, search and analyze data Application Longevity and Flexibility Future-proof applications (30, years) by enabling knowledge workers to participate in model-based application development Copyright 2009 TopQuadrant Inc. Slide 3

5 Semantic Web Layer Cake OWL = 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 Copyright 2009 TopQuadrant Inc. Slide 4

6 Why OWL - the Ontology Web Language? XML is document-based not model-based Container Hierarchies - weak support for relationships Weak support for aggregation (combining documents) Schema Limitations UML is Object-Based Restricted Type System Weak on Relationships Weak notion of identity Metamodel (Schema) is in a different language OWL is Set-Based Expressive Type System Strong on Relationships Strong notion of identity Graphs not Trees Metamodel is in the same language Copyright 2009 TopQuadrant Inc. Slide 5

7 Semantic Web Key Idea # 1 Think Triples : Subject Predicate Object Subject Museum Cultural Collection Artifact Predicate hascollection hasartifact fromperiod Object Cultural Collection Artifact History Period Artifact ownedby Party Artifact donatedby Party Copyright 2009 TopQuadrant Inc. Slide 6

8 Semantic Web Key Idea # 2 Identifiers not Names ( Everything has a URI ) Subject Collection Predicate hasartifactof Object SalishNation mnai:collection mnai:hasartifactof nai:salishnation + Collection hasartifactof HopiNation mnai:collection mnai:hasartifactof nai:hopination Statements from different sources but same URIs means more information about the same things Copyright 2009 TopQuadrant Inc. Slide 7

9 Capability Cases Copyright 2009 TopQuadrant Inc. Slide 8

10 Solution Envisioning Method Innovation happens with the creative interplay of solution ideas with business challenges, possibilities and opportunities. Solution Envisioning with Capability Cases Copyright 2009 TopQuadrant Inc. Slide 9

11 Quadrants of Meaning Formal Informal Semantic Descriptions Taxonomy Terminology Management Textual Descriptions PDF Portals Thesaurus CG Topic Maps RDFS HTML DL FOL UML Modal Logics OWL-2 Semantic Executable Models OWL-Lite Code MDA XML ER OWL Syntactical Consensus Rules Human Machine Copyright 2009 TopQuadrant Inc. Slide 10

12 Semantic Technology Capability Cases Formal Answer Engine Product Design Assistant Expert Locator Ontology Driven Information Retriever Context-Aware Retriever Concept-Based Search Semantic Multi-Faceted Search Semantic Web Services Composer Semantic Data Integrator Generative Documentation Connection and Pattern Explorer Navigational Search Information Aggregator Semantic Workplace Semantic Data Registry Semantic Portal Semantic Web Server Informal Human Recommender Application Integrator Machine Copyright 2009 TopQuadrant Inc. Slide 11

13 Solution Envisioning Workshop We never know exactly what we want until we see what is possible. Shared vision Shared understanding Shared memory A Gallery of Capabilities Copyright 2009 TopQuadrant Inc. Slide 12

14 From a Gallery of Ontology-enabled Capability Cases (1) Context Aware Retriever Answer Engine Copyright 2009 TopQuadrant Inc. Slide 13

15 From the Gallery of Ontology-enabled Capability Cases (2) Personalized Newsletter Concept Based Search Copyright 2009 TopQuadrant Inc. Slide 14

16 Some Application Areas of Semantic Technology 1 Content management Personalized Information Repurposing News feeds Markup 4 Advisors Design Assistants Matchmakers Recommenders Mediators Knowledge management 2 Concept-Based Search Context-Aware Retrieval Expert Locators Collaboration 3 Semantic Interoperability Data Integration Information Inferencing Web Services Discovery and Composition Copyright 2009 TopQuadrant Inc. Slide 15

17 The Meaning Quadrants Mapping Capability Cases Formal Answer Engine Product Design Assistant Expert Locator Concept-Based Search Ontology Driven Information Retriever Context-Aware Retriever Semantic Multi-Faceted Search Semantic Web Services Composer Semantic Data Integrator Generative Documentation Connection and Pattern Explorer Semantic Workplace Semantic Data Registry Semantic Portal Semantic Web Server Informal Navigational Search Human Information Aggregator Recommender Application Integrator Machine Copyright 2009 TopQuadrant Inc. Slide 16

18 TopQuadrant Capability Case: Recommender A slide from a past presentation CapabilityCase: Recommender Personal TV Recommender Formal Semantic Descriptions Semantic Executable Models Informal Textual Descriptions Syntactical Consensus Copyright TopQuadrant Inc., Solution Envisioning for Knowledge Enablement, slide 17 Human Machine

19 Copyright TopQuadrant Inc., Solution Envisioning for Knowledge Enablement, slide 18 TopQuadrant Capability Case: Recommender SolutionStory: Personalized TV (PTV) A slide from a past presentation 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". Personalized News and TV Program Guide 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.

20 Copyright TopQuadrant Inc., Solution Envisioning for Knowledge Enablement, slide 19 TopQuadrant Recommender Systems A slide from a past presentation Current Approaches Collaborative Filtering Case-Based Reasoning Future Approaches Semantic Web Technologies Inferencing Recommendation Engines ac.il/events/2005/recommender.htm

21 TopQuadrant A slide from a past presentation Capability Case: Semantic Multi-Faceted Search CapabilityCase: Semantic Multi-Faceted Search Museum Finland Indiana s Learning Resource Clearinghouse Formal Semantic Descriptions Semantic Executable Models Informal Textual Descriptions Syntactical Consensus Copyright TopQuadrant Inc., Solution Envisioning for Knowledge Enablement, slide 20 Human Machine

22 TopQuadrant CapabilityCase: Semantic Multi-Faceted Search SolutionStory: MuseumFinland A semantic portal for Finnish museums to publish their collections together on the Semantic Web A Semantic Web research project web publishing From 3/2002 to 3/2004 Public pilot version from March 8, 2004: The Vision: Global View to Distributed Collections One seamless national collection (virtually) Museums in Finland -> Museum of Finland Intelligent Services to End-Users Search: Concept-Based Information Retrieval Browsing: Semantically Linked Contents Easy Content Publication for Museums A slide from a past presentation Adapted from: Mirva Salminen, University of Helsinki, Helsinki Institute for Information Technology (HIIT) Semantic Computing Research Group, Copyright TopQuadrant Inc., Solution Envisioning for Knowledge Enablement, slide 21

23 TopQuadrant MuseumFinland A slide from a past presentation Copyright TopQuadrant Inc., Solution Envisioning for Knowledge Enablement, slide 22

24 TopQuadrant Capability Case: Expert Locator A slide from a past presentation CapabilityCase: Expert Locator Boeing Expert Locator Formal Semantic Descriptions Semantic Executable Models Informal Textual Descriptions Syntactical Consensus Copyright TopQuadrant Inc., Solution Envisioning for Knowledge Enablement, slide 23 Human Machine

25 Copyright TopQuadrant Inc., Solution Envisioning for Knowledge Enablement, slide 24 TopQuadrant CapabilityCase: Expert Locator SolutionStory: Boeing Expert Locator A slide from a past presentation 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. Exploiting a Thesaurus-Based Semantic Net for Knowledge-Based Search 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.

26 What is currently happening? Museum Twitter Copyright 2009 TopQuadrant Inc. Slide 25

27 What is currently happening? Top Museums on Twitter Copyright 2009 TopQuadrant Inc. Slide 26

28 Musing the possibilities of Semantic Web for the Art World If we had ontologies of Galleries, Museums, Cultural Collections, Private Collections Exhibitions and Events Artists and their Art Works RSS and Tweet feeds We could know What is being shown where and when Where is this specific Art Work now Who owns this Art Work What influenced this Art Work Where was it painted show me that on the Web Do I need to go to this exhibition Can see the same some where nearer Copyright 2009 TopQuadrant Inc. Slide 27

29 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. Copyright 2009 TopQuadrant Inc. Slide 28

30 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 29

31 Arts World Capability Cases (3 of 3) ArtOnDemand personalized web-based provisioning of Art to homea, work-places, and public places. iart an itunes-like application for provisioning art on the web Copyright 2009 TopQuadrant Inc. Slide 30

32 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; and how personalization and user modeling techniques can be explored to enhance users experiences both on the museum Web site and in the physical museum space. Copyright 2009 TopQuadrant Inc. Slide 31

33 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. Copyright 2009 TopQuadrant Inc. Slide 32

34 Semantic Web is happening in the Arts: The CIDOC CRM Initiative Copyright 2009 TopQuadrant Inc. Slide 33

35 Semantic Web is happening in the Arts: CIDOC Ontology Example in TopBraid Composer Copyright 2009 TopQuadrant Inc. Slide 34

36 Semantic Web is happening in the Arts: CIDOC Ontology Example Physical Thing Copyright 2009 TopQuadrant Inc. Slide 35

37 What I consider doing Artist Ontology Art Works Ontology Museum Tweets Ontology Making ontologies available at the domain name Copyright 2009 TopQuadrant Inc. Slide 36

38 Museum Tweets Ontology Spreadsheet from Copyright 2009 TopQuadrant Inc. Slide 37

39 Museum Tweets Ontology: Arizona Museums on Twitter Copyright 2009 TopQuadrant Inc. Slide 38

40 Thank you Ralph Hodgson Copyright 2009 TopQuadrant Inc. Slide 39