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Grande Challenges for Ontology Design (or is it Vente?) Tom Gruber

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Presentation on theme: "Grande Challenges for Ontology Design (or is it Vente?) Tom Gruber"— Presentation transcript:

1 Grande Challenges for Ontology Design (or is it Vente?) Tom Gruber

2 (c) 2007 Thomas Gruber page 2 Questions for Today Why make ontologies? What are they for? How can we guide ontology development? What are important applications for ontology development? ontologies methods applications

3 (c) 2007 Thomas Gruber page 3 Why make ontologies? Truth? Beauty? Fame? Fortune? Why make software? ontologies methods applications

4 (c) 2007 Thomas Gruber page 4 What makes a Good Ontology? Truth? Beauty? Popularity? Commercial Success? ontologies methods applications

5 (c) 2007 Thomas Gruber page 5 What are Ontologies* For? Enable data and information exchange (for example, the Semantic Web) Provide a conceptual and representational foundation on which to build systems. Thus, Ontologies are Enabling Technology for Applications that Matter. ontologies methods applications *Which Ontologies? The ones we are talking about here

6 (c) 2007 Thomas Gruber page 6 What makes a Good Ontology. Claim: Ontologies should be designed and evaluated with respect to how well they achieve their purposes. Observation: Ontologies are agreements, made in a social context, to accomplish shared objectives. Question: Which objectives? Approach: Follow the process of collaborative engineering design. ontologies methods applications

7 (c) 2007 Thomas Gruber page 7 Engineering Design Process Requirements: Identify needs, use cases, constraints, desired functionality Review existing solutions, technologies, tools, and operational environments Design solution Implement and Test solution Deploy and Maintain solution (In modern practice, the process is iterative.) ontologies methods applications

8 (c) 2007 Thomas Gruber page 8 Example: Tag Ontology TagCommons group is working on agreements to enable the sharing of tagging data across the Web. To guide the collaborative process, we are Identifying use cases and functions Derive ontology requirements Survey existing ontologies and applications Design/adapt/extend/minimize an ontology Map it to formats, other ontologies, data sources, applications ontologies methods applications

9 (c) 2007 Thomas Gruber page 9 Use Cases for Tag Ontology Bookmarking across sites Browsing others tags across sites Social search (collab filtering using tags) Multimedia cross reference resources Indexing documents and code in source repositories Tag Metasearch and Metamonitoring Social Science research Connecting the social and semantic webs ontologies methods applications

10 (c) 2007 Thomas Gruber page 10 Resulting Requirements Core concepts: tagger, tagged, tag label, tag source/venue Auxiliary metadata: dates, polarity, language Identity and matching on core concepts Namespaces for core concepts Mappings among sources with different identity schemes Bridges to other ontologies and standards ontologies methods applications

11 (c) 2007 Thomas Gruber page 11 Tag Ontology Design Issues are framed and guided by use cases. How to represent taggers (people)? Dont want to solve the whole problem of identity on the web – just matching of taggers How to handle missing data and extensions? Dont need hard core nonmonotonic logics – just polymorphic relations with defaults ontologies methods applications

12 (c) 2007 Thomas Gruber page 12 General Ontology Design Principles clarity - context-independent, unambiguous, precise definitions coherence – internally consistent extendibility – anticipate the uses of the vocabulary, allow monotonic extension minimal encoding bias – avoid representational choice for benefit of implementation minimal ontological commitment – define only necessary terms, omit domain theory ontologies methods applications

13 (c) 2007 Thomas Gruber page 13 How to stay grounded in applications? Practical, application development stakeholders on the working group They need an agreement on tag data to make their work feasible, not as the goal of their work. Bridge to Wild Wild Web culture of microformats, REST APIs, etc. Semantic Web GRRDL ontologies methods applications

14 (c) 2007 Thomas Gruber page 14 Applying this to the Larger Ontology Community What are the killer apps for ontologies? What could be done with ontologies that couldnt be done more cheaply, easily, or quickly without them? What problems are important enough to do things the right way? ontologies methods applications

15 (c) 2007 Thomas Gruber page 15 Semantic Web, meet the Social Web Social Web: architecture of participation – user data emergent, bottom-up value creation vital ecosystem of software and data reuse Semantic Web: architecture of computation – structured data value from integration ecosystem of service composition The Killer Apps of Social + Semantic Web: Collective Knowledge Systems ontologies methods applications

16 (c) 2007 Thomas Gruber page 16 But what is collective intelligence in the social web sense? intelligent collection? collaborative bookmarking, searching database of intentions clicking, rating, tagging, buying what we all know but hadnt got around to saying in public before blogs, wikis, discussion lists database of intentions – Tim OReilly ontologies methods applications

17 (c) 2007 Thomas Gruber page 17 the wisdom of clouds? ontologies methods applications

18 (c) 2007 Thomas Gruber page 18 Collective Knowledge Systems The capacity to provide useful information based on human contributions which gets better as more people participate. typically mix of structured, machine-readable data and unstructured data from human input ontologies methods applications

19 (c) 2007 Thomas Gruber page 19 Collective Knowledge is Real FAQ-o-Sphere - self service Q&A forums Citizen Journalism – We the Media Product reviews for gadgets and hotels Collaborative filtering for books and music Amateur Academia ontologies methods applications

20 (c) 2007 Thomas Gruber page 20 What about Ontologies and the Semantic Web? ontologies methods applications

21 (c) 2007 Thomas Gruber page 21 Roles for Technology capturing everything storing everything distributing everything enabling many-to-many communication creating value from the data Your ontology here ontologies methods applications

22 (c) 2007 Thomas Gruber page 22 Potential Roles for Semantic Net Technology: Two examples Composing and integrating user- contributed data across applications example: tagging data Creating aggregate value from a mix of structured and unstructured data example: blogging data ontologies methods applications

23 (c) 2007 Thomas Gruber page 23 Role 2: Creating aggregate value from structured data Problem: In a collective knowledge system, the value of the aggregate content must be more than sum of parts Approach: Create aggregate value by integrating user contributions of unstructured content with structured data. ontologies methods applications

24 (c) 2007 Thomas Gruber page 24 Example: Collective Knowledge about Travel RealTravel attracts people to write about their travels, sharing stories, photos, etc. Travel researchers get the value of all experiences relevant to their target destinations. ontologies methods applications

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26 (c) 2007 Thomas Gruber page 26 Pivot Browsing – surfing unstructured content along structured lines Structured data provides dimensions of a hypercube location author type date quality rating Travel researchers browse along any dimension. The key structured data is the destination hierarchy Contributors place their content into the destination hierarchy, and the other dimensions are automatic. ontologies methods applications

27 (c) 2007 Thomas Gruber page 27 Destination data is the backbone Group stories together by destination Aggregate cities to states to countries, etc Inherit locations down to photos From destinations infer geocoordinates, which drive dynamic route maps Destinations must map to external content sources (travel guides) Destinations must map to targeted advertising ontologies methods applications

28 (c) 2007 Thomas Gruber page 28 Contextual Tagging Tags are bottom up labels, words without context. A structured data framework provides context. Combining context and tags creates insightful slices through the aggregate content. ontologies methods applications

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31 (c) 2007 Thomas Gruber page 31 Travel Recommendation Engine Interview users about travel interests. Match them to trips that people have written about. Recommend places to go and things to do. ontologies methods applications

32 (c) 2007 Thomas Gruber page 32 Recommendation Engine Results

33 (c) 2007 Thomas Gruber page 33 Problems that Semantic Web could have helped No standard source of structured destination data for the world or way to map among alternative hierarchies Integrating with other destination-based sites is expensive e.g. travel guides No standard collection of travel tags or way to share RealTravels folksonomy Integrating with other tagging sites is ad hoc need a matching / translation service ontologies methods applications

34 (c) 2007 Thomas Gruber page 34 Resources That Did Help Open source software or free services powerful databases fancy UI libraries search engines usage analytics Open APIs from Google (maps) and Flickr (photos) Commercially available geocoordinate data and services ontologies methods applications

35 (c) 2007 Thomas Gruber page 35 Grande Challenges Distributing and adding structured data to systems like, Wikipedia, and RealTravel Tag spaces and tag data sharing World destination hierarchy and other geospatial databases Portable user identity and reputation Site-independent rating and filtering Semantic search and spam filtering ontologies methods applications

36 (c) 2007 Thomas Gruber page 36 Vente Challenges How to get knowledge from all those intelligent people on the Internet How to give everyone the benefit of everyone elses experience How to leverage and contribute to the ecosystem that has created todays web. ontologies methods applications

37 (c) 2007 Thomas Gruber page 37 What will the future look like? Social Web Social + Semantic Web stock images from; cover image by

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