Grande Challenges for Ontology Design

(or is it Vente?)

Tom Grubertomgruber.org

(c) 2007 Thomas Gruberpage 2

Questions for Today

Why make ontologies? What are they for? How can we guide ontology development? What are important applications for

ontology development?

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Why make ontologies?

Truth? Beauty? Fame? Fortune?

Why make software?

ontologies methods applications

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What makes a Good Ontology?

Truth? Beauty? Popularity? Commercial Success?

ontologies methods applications

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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

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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.

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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.)

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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

http://tagcommons.org

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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

http://tagcommons.org/2007/02/28/functional-requirements-for-sharing-tag-data/

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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

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Tag Ontology Design Issues are framed and guided by use cases.

How to represent taggers (people)? Don’t want to solve the whole problem of

identity on the web – just matching of taggers How to handle missing data and

extensions? Don’t need hard core nonmonotonic logics –

just polymorphic relations with defaults

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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

http://tomgruber.org/writing/onto-design.htm

ontologies methods applications

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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

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Applying this to the Larger Ontology Community

What are the killer apps for ontologies?

What could be done with ontologies that couldn’t be done more cheaply, easily, or quickly without them?

What problems are important enough to do things “the right way”?

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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

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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 hadn’t got around to saying in public before blogs, wikis, discussion lists

“database of intentions” – Tim O’Reilly

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the wisdom of clouds?

http://flickr.com/photos/tags/

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“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

http://tomgruber.org/writing/social-meets-semantic-web.htm

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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

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What about Ontologies and the Semantic Web?

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Roles for Technology

capturing everything storing everything distributing everything enabling many-to-many communication creating value from the data

Your ontology here

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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

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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.

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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.

http://tomgruber.org/technology/realtravel.htm

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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.

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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

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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.

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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.

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Recommendation Engine Results

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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 RealTravel’s folksonomy

Integrating with other tagging sites is ad hoc need a matching / translation service

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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

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Grande Challenges

Distributing and adding structured data to systems like Del.icio.us, 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

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Vente Challenges

How to get knowledge from all those intelligent people on the Internet

How to give everyone the benefit of everyone else’s experience

How to leverage and contribute to the ecosystem that has created today’s web.

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What will the future look like?

Social Web Social + Semantic Web

stock images from istockphoto.com; cover image by neilsethlevine.com