Ontology Evolution Mark A. Musen Stanford University

Ontology Evolution

Mark A. Musen

Stanford University

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Ontology Design Criteria (after Gruber)

• ClarityDefinitions should be objective and complete

• Coherence The ontology should sanction those inferences consistent with the definitions

• Extendibility An ontology should anticipate future uses

• Minimal encoding bias No assumptions about knowledge representation

• Minimal ontological commitment

Trade-offs in Ontology Design

• Minimizing ontological commitment requires specifying a weak theory

• Making definitions precise requires increasing ontological commitment

• Anticipating various uses of the ontology may require increasing the number of concepts represented

• Making an ontology maximally general may make it useless for any specific application

Common problems when people build ontologies

• Classes are not defined at useful levels of abstraction (e.g., is it necessary to distinguish among mammals or terriers?)

• Class definitions are overloaded (e.g., is it helpful to have the class red bicycle?)

• Hierarchical relationships are not uniformly taxonomic (e.g., amino acid is a subclass of protein)

• The world (or our perception of it) changes

The world does change!

• What happened to the ether? To phlogiston?• What happened to diseases such as dropsy,

consumption, neuresthenia, “gay lymph node syndrome”?

• What happened to HTLV III?• When did scurvy become a curable disease? • When did the central dogma of biology first

break down?• When did Poland begin to exist?

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Suggested Upper Merged Ontology (SUMO)

Part of the CYC Upper Ontology

A Parable: Protocol-Based Advisories

Protocol-Based Advisories

The ONCOCIN system (ca. 1986)

ONCOCIN: Object structure drives inference

OPAL first elicited the overall algorithm for the protocol

Clicking on “VAM” in the graph brought up

a form for entering the constituent drugs

We construed ONCOCIN’s PSM as Episodic Skeletal Plan Refinement (ESPR)

1. Planning entities form skeletal plan

2. Task-level actions modify customary execution of the plan

3. Input data predicate actions

PROTÉGÉ-1 (ca. 1987)

• A meta-level knowledge-entry system for generating knowledge-entry systems like OPAL

• Assumed an ontology of the ESPR method (but we didn’t call in that, since no one other than Barry knew about ontologies in 1987)

• Demonstrated in domains of oncology and hypertension clinical trials—allowing rapid generation of custom-tailored knowledge-entry tools

Attributes of a Planning Entity

What was PROTÉGÉ-1 doing?

• The system started with a an ontology of the kinds of data on which the ESPR method operates

• Developers subclassed the entities in that ESPR ontology to define the domain entities that relate to skeletal planning in a particular application area (e.g., oncology, hypertension)

• The system used the subclasses to generate UIs– For entry of instance-level knowledge (e.g., that of particular

clinical protocols)– For creating the electronic spreadsheet for interacting with

clinical users

PROTÉGÉ-1 asked users to subclass the ESPR method ontology

1. Planning entities form skeletal plan

2. Task-level actions modify customary execution of the plan

3. Input data predicate actions

“Ontology development as subclassing” was not sustainable

• Subclassing entities in the ESPR “method ontology” did ensure that anything we said about the domain immediately had an operational semantics

• There were lots of things that we wanted to say about the domain unrelated to the ESPR method

The Parable ContinuesTherapy Helper, ca. 1992

Mapping domain ontologies to problem-solving methods

ESPR

Domain Ontology(e.g., clinical data, treatment history)

MethodInput Ontology (e.g., skeletal plan, input data)

MethodOutput Ontology (e.g., fully formed plan)

Therapy Helper: Protocol-Based Care for HIV/AIDS

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ESPR Method Ontology

T-Helper Application Ontology

Mapping domain ontologies to problem-solving methods

ESPR

Domain Ontology(e.g., clinical data, treatment history)

MethodInput Ontology (e.g., skeletal plan, input data)

MethodOutput Ontology (e.g., fully formed plan)

Therapy Helper: Protocol-Based Care for HIV/AIDS

EON: Middleware that abstracts from T-Helper

The debut of Protégé/Win

Protégé/Win KA tool

The EON ontology continued to evolve

• Support for concurrent actions• Coordination of processes• Data abstraction from the primary inputs

(electronic medical record)• Temporal data abstraction from primary

data• Contextualization of actions into

“scenarios of care”

All the ontology changes took place at the “macro” level

• Major shifts in distinctions made about the world (e.g., stereotypic “scenarios”)

• Major new capabilities of underlying systems (e.g., ability to drive reasoning from large numbers of automatically acquired data)

• While all this was happening: Countless changes in small, individual modeling decisions

In the real world, ontolgies change all the time

• The number of distinctions that we can make about the world is practically infinite

• We have to start somewhere!• We constantly must make new distinctions

because– Our needs change– Our view of reality changes– We finally get around to it …

Supreme genus: SUBSTANCE

Subordinate genera: BODY SPIRIT

Differentiae: material immaterial

Differentiae: animate inanimate

Differentiae: sensitive insensitive

Subordinate genera: LIVING MINERAL

Proximate genera: ANIMAL PLANT

Species: HUMAN BEAST

Differentiae: rational irrational

Individuals: Socrates Plato Aristotle …

Porphyry’s depiction of Aristotle’s Categories

Locus of control for group ontology development

• Centralized– As in the NCI Thesaurus

• Decentralized– As in the Open Directory Project

NCI Enterprise Vocabulary Services

1997: R. Klausner, Director NCI, wanted a “science management system”

• Know about everything funded by NCI• Goals and results – “bench to bedside”

- Thereby improve and speed translation of research

Approach:1. Create integrative terminology2. Evolve terminology scope from supporting grants

management to supporting science3. Build Web-accessible infrastructure – caCORE

The NCI Thesaurus

NCI Thesaurus Guidelines

• Develop content model• Leverage existing sources as appropriate

– MeSH, VA NDF-RT, MedDRA …

• Develop unique content where needed– Cancer genes, gene products, cancer diagnoses, drugs,

chemotherapies, molecular abnormalities etc., and relationships among them

• Link to other standards using URLs where possible– OMIM, Swissprot, GO

:

ProductionRelease

ExternalTesting

NCI ThesaurusTest DTSServers

NCI ThesaurusEditing Environment

NCI ThesaurusWorkflow

Conflict Detectionand Resolution

Work ListGeneration

Classification

HxValidation

Hx

Baseline

Schema

Schema

Schema

Individual Editors’ TDEØ Workflow ClientØ Editing ApplicationØ DB Schema - Current NCI Baseline - Local History

Lead Editor TDEØ Work Manager ClientØ Editing ApplicationØ Conflict Detection/ResolutionØ DB Schema - Master NCI Baseline - Master History

ChangeSet

WorkAssignment

CandidateRelease

Hx

NCI ThesaurusProductionDTS Servers

Hx

Release

NCI uses a Centralized Process

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Open Directory Project

• Started in 1998 as a volunteer effort to develop an open-content directory of Web pages

• In its first year, 4500 editors had indexed 100K Web sites

• By July 2005, 69K editors had indexed 4.6M sites using 580K categories

• On average, between 9K and 10K volunteer editors are working on ODP at any given time

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Dimensions for Ontology Change Management

• Central vs. Decentralized control

• Continuous editing vs. Periodic archiving

• Curation vs. No curation

• Monitored editing vs. Nonmonitored editing

Monitored editing in Protégé

History of Changes is Stored in a “Change Ontology”

Workflow for Change Management

:

ProductionRelease

ExternalTesting

NCI ThesaurusTest DTSServers

NCI ThesaurusEditing Environment

NCI ThesaurusWorkflow

Conflict Detectionand Resolution

Work ListGeneration

Classification

HxValidation

Hx

Baseline

Schema

Schema

Schema

Individual Editors’ TDEØ Workflow ClientØ Editing ApplicationØ DB Schema - Current NCI Baseline - Local History

Lead Editor TDEØ Work Manager ClientØ Editing ApplicationØ Conflict Detection/ResolutionØ DB Schema - Master NCI Baseline - Master History

ChangeSet

WorkAssignment

CandidateRelease

Hx

NCI ThesaurusProductionDTS Servers

Hx

Release

The Goal: To Streamline NCI’s Cumbersome Process

Why Most Ontologies Stagnate

• It is tedious to evaluate ontological soundness by inspection

• It is impossible to evaluate ontological coverage by inspection

• It is often plain difficult to determine what an ontology is good for by inspection

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A Portion of the OBO Library

Ontologies are not like journal articles

• It is difficult to judge methodological soundness simply by inspection

• We may wish to use an ontology even though some portions – Are not well designed– Make distinctions that are different from

those that we might want

Ontologies are not like journal articles II

• The utility of ontologies– Depends on the task– May be highly subjective

• The expertise and biases of reviewers may vary widely with respect to different portions of an ontology

• Users should want the opinions of more than 2–3 hand-selected reviewers

• Peer review needs to scale to the entire user community

Solution Snapshot

In an “open” rating system:

• Anyone can annotate an ontology to say anything that one would like

• Users can “rate the raters” to express preferences for those reviewers whom they trust

• A “web of trust” may allow users to create transitive trust relationships to filter unwanted reviews

Possible Review Criteria

• What is the level of user support? • What documentation is available?• What is the granularity of the ontology content in

specific areas?• How well does the ontology cover a particular

domain?• In what applications has the ontology been used

successfully? Where has it failed?

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An ontology of “marginalia” would go a long way

Trade-offs in Ontology Design

• Minimizing ontological commitment requires specifying a weak theory

• Making definitions precise requires increasing ontological commitment

• Anticipating various uses of the ontology may require increasing the number of concepts represented

• Making an ontology maximally general may make it useless for any specific application

The National Center for Biomedical Ontology

• One of three National Centers for Biomedical Computing launched by US NIH in 2005

• Collaboration of Stanford, Berkeley, Buffalo, Mayo, Victoria, UCSF, Oregon, and Cambridge

• Primary goal is to make ontologies accessible and usable

• Research will develop technologies for ontology indexing, alignment, and peer review

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The Center move us beyond individual, one-off ontologies and one-off tools to:

• Integrated ontology libraries in cyberspace• Meta-data standards for ontology annotation• Comprehensive methods for ontology indexing and

retrieval• Easy-to-use portals for ontology access, annotation,

and peer review• End-user platforms for putting ontologies to use for

– Data annotation– Decision support– Natural-language processing– Information retrieval– And applications that we have not yet thought of!

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