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SOCoP 2012 Workshop:

Vision and Strategy

Gary Berg-CrossSOCoP Executive Secretary

Nov. 29-30, 2012

U. S. Geological Survey National Center 12201 Sunrise Valley Dr., Reston VA

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Outline for a VoCamp-Style Workshop

VoCamp Style is informal Workshop Ingredients

1. Goals

2. Workgroup Teams

3. Logic of Phased Structure Sessions

4. Lightweight Methods

5. From Conceptualizations to Formalizations

6. Cautions and Tradeoffs

7. Ontologies and Ontology Patterns

We are not here to teach a new discipline, but to employ multiple disciplinesas a team.

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Goals: “Group Work on Semantic Models of Interest”

Building on past workshops and interests: We seek clarified agreement & reduced

ambiguities/conflicts on geospatial/earth science phenomena that can be formally represented in:1. Constrained, engineered models to support understanding,

reasoning & data interoperability and/or2. Creation of general patterns that provide a common

framework to generate ontologies that are consistent and can support interoperability.

We like data-grounded work since: Much of the utility of geospatial ontologies will likely

come from their ability to relate geospatial data to other kinds of information.

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Workgroups Include Multiple Roles:Semantic Engineering is a Social Process

Ontologist

Domain/Data Expert

Facilitator

Note taker

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Logic of Work Sessions

Start Group organization & Introductions, goals and process

After lunch Group Work on Concepts, Vocabulary & Model(s)

After break Group Work on Draft Models

At end of day Group Reports on status

At end of day Report back to whole and wrap up

2nd day draft final model & initial formalizations

After break Work Groups polish, formalize models

After lunch firm up products and test against data

After break Prepare Report

Day One Intro, Topics, Methods..

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Lightweight Methods & Products

Choose lightweight approaches grounded by scenarios and application needs. Low hanging fruit leverages initial vocabularies and existing

conceptual models to ensure that a semantics-driven infrastructure is available for use in early stages of work

Reduced entry barrier for domain scientists to contribute data

Simple parts/patterns & direct relations to data

Ecological..

Triple like parts

Constrained not totallySpecified.Grounded

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How Ontologies Arise from “Analysis and Conceptualization”

Adapted liberally from Guarino’s 1998Formal Ontology in Information Systems

1 World Situations

Perceived Distinctions& Situated Experience

.. Stream Reaches are segments of surface water with similar hydrologic

characteristics

Conceptualizationstarts to adaptively model

(part of) the world

2 Abstraction

Stream Reach distinctionRationalized Intuition expressed in

Some conceptual language

Things D in world state W with conceptual relations R

C= <SW, H, S>SW =surface water

H=has S = Segments

Pragmatic validation

Real World Semantics

Domain folks and Modelers

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Ontological Engineering – Formalization

1 World Situations

Interaction..That is a

type of Stream

segment

C Conceptualization

model (part of) the worldSay D Hydrology

In some Conceptual Domain space

UML OWL

3. To express C weneed Language L (Term StreamX corresponds to entity in world)

We assign interpretative Functions I &Commitment K. K=<C,I)

Models forDomain DExpressibleIn L

IntendedModelFitting

COntologyModels forDExpressedIn L using K

Adapted liberally from Guarino’s 1998Formal Ontology in Information Systems

Models defines relationship between L syntax and interpretations

Logical ModelLogical ModelTheories are Theories are consistent sets of sentences; consistent sets of sentences; closed under logical closed under logical consequenceconsequence

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Caution(s)

No single (general or domain) taxonomy or ontology could satisfy all needs. That’s part of the reason we have so many alternative ones.

We can make quality, useful ones for one or more purposes. We have intended models of focus. But if we make commitments that make strong claims that are

greater our understanding and focus we may be making a mistake(s).

Smith and Mark describe a good/quality ontology as one that is: “neutral”,

And/or perhaps explicit about what it is not neutral about computationally tractable, acceptable to and reusable by domain people

One might add that it is rationalized.

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Many Challenges – more on this later….

These include tradeoffs between generality and precision. Tom Bittner, for example, notes:

“The need for geometric representations, many of which rely on relatively precise boundaries, conflicts with the need for sophisticated classification systems for scientific and integration purposes. “

Vagueness and the tradeoff between the classification and delineation of geographic regions - an ontological analysis

http://www.geoinfo.info/geoinfo2012/programa.php

But these, of course, make the work interesting…..

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Ontologies and Ontology Patterns

Many levels & types of ontologies Something like DOLCE is quite complex and so are

domain ontologies like SWEET One may pick some small repeating patterns (ODPs) out

of large ontologies. ODPs, like OWL, are tools for ontologies

They are more easily understandable with good explicit documentation for design rationales

RobustCan be used to build on modularly for reoccurring problems needing representation

Capture best practicesShould help bridging/integrating ontologies

We focus on content ODPs using domain expertise rather than logical ODPs etc.owl:Class:_:x rdfs:subClassOf owl:Restric(on:_:y

Inflammation>on rdfs:subClassOf (localizedIn some BodyPart)

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Dolce UltraLite?

Apps( Uses Cases &

Demos)

“Geo”-Domain

“Geo” Top

Patterns

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Ontology-ODP RelationsSmall DUL Portion <owl:Class rdf:ID="SocialObjectAttribute"> <rdfs:label xml:lang="en">Social attribute</rdfs:label> <rdfs:subClassOf> <owl:Restriction> <owl:onProperty> <owl:ObjectProperty rdf:ID="isRegionFor"/> </owl:onProperty> <owl:allValuesFrom> <owl:Class rdf:ID="SocialObject"/> </owl:allValuesFrom> </owl:Restriction> </rdfs:subClassOf> <rdfs:label xml:lang="it">Caratteristica sociale</rdfs:label> <rdfs:subClassOf> <owl:Class rdf:ID="Region"/> </rdfs:subClassOf> <rdfs:comment>Any Region in a dimensional space that is

used to represent some characteristic of a SocialObject, e.g. judgment values, social scalars, statistical attributes over a collection of entities, etc.</rdfs:comment>

</owl:Class> <owl:Class rdf:ID="WorkflowExecution">…

ODP

Abstract From

Use for

NewOntology Design

“Unfriendly logical structures, some large, hardly comprehensibleOntologies” (Aldo Gangemi)

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ODP Rationale –Reuse, Minimal Constraints..

Problem It is hard to reuse only the “useful pieces" of a comprehensive

(foundational) ontology, and the cost of reuse may be higher than developing a scoped

ontology for particular purpose from scratch “For solving semantic problems, it may be more productive

to agree on minimal requirements imposed on .. Notion(s) Werner Kuhn (Semantic Engineering, 2009)

Solution Approach Use small, well engineered, modular starter set ontologies

with explicit documentation of design rationales, and best reengineering practices

These serve as an initial constraining network of “concepts” with vocabulary which people may build on/from for various purposes.