March 11, 2004 1 Knowledge Representation Chapter 10

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Knowledge RepresentationChapter 10

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Outline

• KR Introduction• Ontological Engineering• Categories and Objects• Actions, Situations, and Events• Mental Events and Mental Objects• Reasoning Systems for Categories• Reasoning with Default Information• Truth Maintenance Systems• Bio-Ontologies

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KR Introduction• General problem in Computer Science• Solutions = Data Structures

– words– arrays– records– list

• More specific problem in AI• Solutions = knowledge structures

– lists– trees– procedural representations– logic and predicate calculus– rules– semantic nets and frames– scripts

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Kinds of Knowledge

• Objects– Descriptions– Classifications

• Events– Time sequence– Cause and effect

• Relationships– Among objects– Between objects and events

• Meta-knowledge

Things we need to talk about and reason about; what do we know?

Distinguish between knowledge and its representation

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Representation Mappings

• Knowledge Level

• Symbol Level

• Mappings are not one-to-one

• Never get it complete or exactly right

Internal Representation

English Representation

FactsReasoning Programs

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Ontological Engineering

• Like knowledge engineering but applies to general-purpose knowledge bases

• Ultimate goal is to represent everything in the world!!

• Result is an upper ontologyAnything/Root

AbstractObjects GeneralizedEvents

RepresentationalObjects

Categories

NumbersSets

Sentences

Places

Measurements

Interval

WeightsTimes

Processes

ThingsMoments

PhyscialObjects

Solid Liquid Gas

Stuff

Agents

Humans

Animals

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Special- and General-purpose Ontologies

• Special-purpose ontology:– Designed to represent a specific domain of

knowledge;• genetics (GO)• immune system (IMGT)• mathematics (Tom Gruber)

• General-purpose ontology:– Should be applicable in any special-purpose

domain– Unifies different domains of knowledge

• Upper ontology provides highest level framework - all other concepts follow

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Cyc Upper Ontology

• Cycorp released 3,000 upper-level concepts into public domain

• Cyc Upper Ontology satisfies two important criteria;– It is universal: Every concept can be linked to it– It is articulate: Distinctions are necessary and

sufficient for most purposes

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Categories - Representation

• Two choices for representation:– Predicate

• Basketball(b)

– Object• Basketballs

• Member(b, Basketballs)

• Subset(Basketballs, Balls)

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Categories - Organizing

• Inheritance:– All instances of the category Food are edible– Fruit is a subclass of Food– Apples is a subclass of Fruit– Therefore, Apples are edible

• The Class/Subclass relationships among Food, Fruit and Apples is a taxonomy

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Categories - Partitioning

• Disjoint: The categories have no members in common

• Exhaustive Decomposition: Every member of the category is included in at least one of the subcategories

• Partition: Disjoint exhaustive decomposition

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Disjoint({Animals,Vegetables})

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Disjoint(s) <=> (c1,c2 c1s c2s c1c2 Intersection(c1,c2) = {})

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ExhaustiveDecomposition({Americans,Canadians,Mexicans},NorthAmericans})

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ExhaustiveDecomposition(s,c) (i ic c2 c2s ic2)

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Partition({Males,Females},Animals)

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Partition({Males,Females},Animals)

Parition(s,c) Disjoint(s) ExhaustiveDecomposition(s,c)

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Categories - More

• PartOfPartOf(Bucharest,Romania)

PartOf(Romania,EasternEurope)

PartOf(EasternEurope,Europe)

PartOf(Europe,Earth)

• Composite ObjectsBiped(a) c1,c2,b Leg(c1) Leg(c2)

Body(b) PartOf(c1,a) PartOf(c2,a) PartOf(b,a) Attached(c1,b) Attached(c2,b) c1c2 [c3 Leg(c3) PartOf(c3,a) (c3=c1 c3=c2)]

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Categories – And More

• Count Nouns and Mass Nouns– How many aardvarks? How many butters!?!

x Butter PartOf(y,x) y Butter

• Intrinsic and Extrinsic Properties– Intrinsic properties belong to the very substance

of the object; e.g. flavor, color, density, boiling point, etc.

– Extrinsic properties change if the object is changed (cut in half); e.g. weight, length, shape, etc.

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Actions, Situations and Events

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Situation Calculus• The states resulting from executing actions

• Ontology:– Situations: logical terms describing initial

situation and all situations that result from executing actions on a given situation

Result(a,s)

– Fluents: functions and predicates that may be different in different situations

Age(Wumpus,S0) is Wumpus age in situation S0

– Atemporal or eternal: functions and predicates that are constant across all situations

Gold(G1)

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Situation Calculus – Actions

• Each action described by two axioms:– Possibility Axiom:

Preconditions Poss(a,s)– Effect Axiom:

Poss(a,s) changes that result from taking action

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Situation Calculus - ExamplePossibility Axioms:

At(Agent,x,s) Adjacent(x,y) Poss(Go(x,y),s).

Gold(g) At(Agent,x,s) At(g,x,s) Poss(Grab(g),s).

Holding(g,s) Poss(Release(g),s).

Effect Axioms:

Poss(Go(x,y),s) At(Agent,y,Result(Go(x,y),s).

Poss(Grab(g),s) Holding(g,Result(Grab(g),s)).

Poss(Release(g),s) Holding(g,Result(Grab(g),s)).

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Go for the Gold!GOAL: Bring the gold from [1,2] to [1,1]

At(Agent,[1,1],S0) At(G1,[1,2],S0).Holding(G1,S0).Gold(G1).Adjacent([1,1],[1,2]) Adjacent([1,2],[1,1]).

Do It:Go([1,1],[1,2]).

Result:At(Agent,[1,2],Result(Go([1,1],[1,2]),S0)).

Now, can I grab the gold?Grab(G1).

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Do It:Go([1,1],[1,2]).



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Do It:Go([1,1],[1,2]).



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Do It:Go([1,1],[1,2]).



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The Frame Problem

Result:

At(Agent,[1,2],Result(Go([1,1],[1,2]),S0)).

Now, can I grab the Gold?

Grab(G1).

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The Frame Problem

Result:

At(Agent,[1,2],Result(Go([1,1],[1,2]),S0).


Grab(G1).

What in the knowledge base allows me to go from my Result (above) to Grab(G1)?

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The Frame Problem

Result:

At(Agent,[1,2],Result(Go([1,1],[1,2]),S0).


Grab(G1).

What in the knowledge base allows me to go from my Result (above) to Grab(G1)?

nothing

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The Frame Problem

• How do we represent all the things in the world that stay the same?– Represent all things at all situations; the

representational frame problem– Project the results of a sequence of actions; the

inferential frame problem

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Representational Frame Problem

• Successor-State Axiom:Action is possible (Fluent is true in result state

Action’s effect made it true It was true before and action left it alone).

• Truth value of each fluent in the next state depends on action and truth value in the current state

Poss(a,s) (At(Agent,y,Result(a,s)) a = Go(x,y) (At(Agent,y,s) a Go(y,z))).

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Time and Event Calculus

• Event Calculus based on points in time

• Fluents hold at points in time as opposed to holding in situations

“A fluent is true at a point in time if the fluent was initiated by an event at some time in the past and was not terminated by an intervening event.”

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Event Calculus

• Initiates(e,f,t) and Terminates(w,f,t)

• Event Calculus Axiom:T(f,t2) e,t Happens(e,t) Initiates(e,f,t)

(t<t2) Clipped(f,t,t2)

Clipped(f,t,t2) e,t Happens(e,t1) Terminates(e,f,t1) (t < t1) (t1 < t2)

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Event Calculus - more

• Can be extended to handle;– indirect effects– continuous change– nondeterministic effects– causal constraints– . . .

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Generalized Events

• Combines aspects of space and time calculus

• Allows representation of events occurring in a space-time continuum

World War II is an event that happened in various geographic locations during a specific period of time within the 20th century.

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Processes

• Discrete Events: the event is a whole and a part of the event is no longer the same event

• Processes can include subintervals; a part of a plane flight is still a member of the Flying class (aka liquid events)

• Stated more precisely: “Any subinterval of a process is also a member of the same process category.”

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Intervals

• Moment: has temporal duration of zero

• Extended Interval: has temporal duration of greater than zeroPartition({Moments,ExtendedIntervals},Intervals)

Member(i,Moments) Duration(i) = Seconds(0).

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Intervals Ontology

Meet(i,j) Time(End(i)) = Time(Start(j)).

Before(i,j) Time(End(i)) < Time(Start(j)).

After(j,i) Before(i,j).

During(i,j) Time(Start(j)) Time(Start(i)) Time(End(i)) Time(End(j)).

Overlap(i,j) k During(k,i) During(k,j).

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Mental Events and Mental Objects

• Knowledge about beliefs, specifically about those beliefs held by an agent– “Which agent knows about the geography of Maine?”

• Provides an agent the ability to reason about beliefs of agents

• However, need to define propositional attitudes, such as Believes, Knows and Wants as relations where the second argument is referentially opaque (no substitution of equal terms)

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Reasoning Systems for Categories• Categories are KR building blocks

• Two primary systems for reasoning:– Semantic Networks

• Graphical aids for visualizing knowledge

• Mechanisms for inferring properties of objects based on category membership

– Description Logics• Formal language for constructing and combining

category definitions

• Algorithms for classifying objects and determining subsumption relationships

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Semantic Networks

• Graphical notation with underlying logical representation

• A form of logic, but not FOL

• Capable of representing objects, relations, quantification, …

• Convenient representation of inheritance

• Multiple Inheritance (sometimes)

• Inverse links

• Extendable using procedural attachments

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Semantic Networks - More

• Can only express binary relationships – making it more difficult to express n-ary predicates; e.g. Fly(Shankar,NewYork,NewDelhi,Monday)

• Negation, disjunction, nested function symbols, and existential quantification are missing

• Some SNs include procedural attachments

• Represents default values – assertions may be overridden by more specific values

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Semantic Networks

Mammals

Persons

Females Males

Mary JohnSisterOf

SubsetOf

SubsetOf SubsetOf

Legs 1

HasMother2Legs

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Description Logics

• Notations to make it easier to describe definitions and properties of categories

• Taxonomic structure is organizing principle• Subsumption: Determine if one category is a

subset of another• Classification: Determine the category in

which an object belongs• Consistency: Determine if membership criteria

are logically satisfiable

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Description Logics

• CLASSIC was one of first languages (Borgida, et al, 1989)

• “All bachelors are unmarried adult males.”– DL:Bachelor = And(Unmarried,Adult,Male).– FOL:Bachelor(x) Unmarried(x) Adult(x) Male(x)

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Description Logics

• What does this DL statement say?

And(Man,AtLeast(3,Son), AtMost(2,Daughter), All(Son,And(Unemployed,Married, All(Spouse,Doctor))), All(Daughter,And(Professor, Fills(Department,Physics,Math)))).

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Description Logics - More

• Emphasis on tractability of inference• Inference happens by;

– Describe the problem instance

– Asserting the instance into the KB to be handled by the subsumption apparatus

• FOL cannot predict solution time• DL solve in time polynomial in size of KB• DLs usually lack disjuntion and negation (for

time/speed considerations)

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Current Description Logic• DAML+OIL

– DARPA Agent Mark-up Language + Ontology Inference Language (OIL)

– Comes out of DARPA initiative– OIL from University of Manchester– http://www.w3.org/TR/daml+oil-reference

• OWL– Ontology Web Language– A language for the semantic web– “Next generation” DAML+OIL– Flavors: OWL-Lite, OWL-DL and OWL (full)– W3C recommendation as of Feb 10, 2004– http://www.w3.org/TR/2004/REC-owl-features-20040210/

http://www.w3.org/TR/2004/REC-owl-features-20040210/

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Reasoning with Default Information

• Open and Closed worlds– Open World: Information provided is not

assumed to be complete, therefore inferences may result in sentences whose truth value is unknown

– Closed World: Information provided is assumed complete, therefore ground sentences not asserted to be true are assumed false

– Negation as Failure: A negative literal, not P, can be “proved” true if the proof of P fails

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Nonmonotonic Logics: Circumscription

• Version of closed-world assumption• Specify predicates that are almost always

false• Default rule stating that birds fly:Bird(x) Abnormal(x) Flies(x)• Abnormal() is circumscribed – reasoner

assumes Abnormal() unless Abnormal() is known to be true

• Circumspection is model preference logic; notion of preferred models in KB

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Nonmonotonic Logics:Default Logic

• Default rules express contingencies:Bird(x) : Flies(x)/Flies(x)• If Bird(x) is true, and Flies(x) consistent

with KB, then conclude Flies(x) (by default)

• Default rule form is;

P : J1, …, Jn/C• P = Prerequisite; J = Justifications; C =

Conclusions• If any J is false, then C is not true

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Truth Maintenance Systems

• Designed to handle Belief Revision:

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Let’s say our KB contains sentence P

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But P is found to be incorrect/untrue

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So, we want to say Tell(KB,P)

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First, though, Retract(KB,P) to avoid P P

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What if P Q? What happens to Q?

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Retract Q?

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Retract Q?

But what if we also have R Q?

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Retract Q?

But what if we also have R Q?

Therefore…

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Truth Maintenance Systems• “Rollback” mechanism – doesn’t scale up• Justification-based Truth Maintenance System

(JTMS)– Includes in the KB the set of sentences from which the

sentence was inferred

– Sentences are in or out, based on truth value of supporting sentences

• Assumption-based Truth Maintenance System (ATMS)– Maintains a set of supporting sentences, representing

all states

– Sentence holds in just those cases where all assumptions in one of the assumptions sets hold

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Justification-based TMS

• Each sentence in KB includes all sentences that made it true

P Q has justification {P, P Q}

• What if Q has the following justifications, and we Retract(P)?

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{P, P Q}

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{P, P Q}

{P, P R Q}

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{P, P Q}

{P, P R Q}

{R, R P Q}

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{P, P Q}

{P, P R Q}

{R, R P Q}

• Sentences that comprise Justifications are in or out (not removed from KB) – efficiency

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Assumption-based TMS

• Designed to make Belief Revision efficient

• Represents all states at the same time

• Each sentence in the KB has a set of assumption sets

• For each sentence in the KB, the sentence holds when all assumptions in one of it’s assumption sets hold

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Ontologies in Practice:The BioOntologies Consortium

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Outline

• Motivation– The problem– The solution

• Exchange Languages Evaluation– Initial Evaluation– Second-level Evaluation– Conclusions/Recommendations

• Future Work

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Motivation

• Explosive and uncontrolled growth of Bioinformation

• It is increasingly important in the life sciences to integrate information across scientific disciplines and business areas– Terminology in the domain of molecular

biology is inconsistent - information searches can be incomplete and inaccurate

– Definitions and descriptions of life sciences objects differ among data sources - significant time and effort is required to integrate those data sources

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What is DNA Topoisomerase?EC 5.99.1.2

DNA Nicking-Closing Protein

DNA Relaxing Enzyme

DNA Relaxing Protein

DNA Topoisomerase

DNA Topoisomerase I

DNA Type 1 Topoisomerase

DNA Untwisting Enzyme

DNA Untwisting Protein

Omega Protein

Topoisomerase I

Type I DNA Topoisomerase

Nicking-closing enzyme

Relaxing enzyme

Untwisting enzyme

w-Protein

Swivelase

UMLS says it’s ==>

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Motivation - Shared Ontologies

• Ontologies in the life sciences currently exist, but not in a coordinated/shared manner

• Shared ontologies provide benefits;– sharing the work– database integration– exchange of biological data– developing shared understandings– differences can provide focus on interesting

problems

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The Solution: Ontologies

“An ontology is a specification of a conceptualization.”

“An ontology is a description of the concepts and relationships that can exist for an agent or a community of agents. ... A common ontology defines the vocabulary with which queries and assertions are exchanged among agents.”

T.R. Gruber (1993)

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Goals of Ontologies• Provide standardized vocabularies for text

mining and information retrieval

• Formalized ontologies are expressed in a common language (or a small number of languages), facilitating representation and exchange of ontological knowledge

• Building common ontologies will establish shared understandings within the community so, create a consortium as a forum to develop these ontologies

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Bio-Ontologies Consortium Goals• Enable interoperability/exchange of life

sciences information• Establish a consortium for promoting and

sharing open-source ontologies in the Life Sciences

• Establish user community for sharing experiences with designing and building ontologies for the Life Sciences

• Develop synergies with the Knowledge Management community to target tools/languages to life sciences ontologies

• Create a permanent portal for the exchange of ontologies and ontology building tools

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Bio-Ontologies Consortium Activity

• Enable interoperability/exchange of life sciences information– Successful exchange depends on;

• Common, shared definitions

• Common language to describe definitions

– Therefore, select a language, or a small set of languages, for the exchange of life sciences ontologies

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Select Candidate Languages (1)• Ontolingua

– Long-standing effort in KR community

– Based on work for common interchange language

• CycL– Significant effort in KR community

– Largest commercial vendor of ontological tools

• OML/CKML– XML based language

– new language, so possible to influence development

• OPM– OO model to describe single- and multi-DB schemas

– tool used in bioinformatic community

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Select Candidate Languages (2)• XML and XML/RDF

– Web-based language– Significant work going on to extend expressivity

• UML– Widely used modeling tool in commercial marketplace– Based on OO concepts (supported by industry)

• OKBC– API for accessing distributed Knowledge Bases– Current work by KR community

• ASN.1– Early representation language for Bioinformatics

• ODL– De facto standard for OO databases

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Evaluation Criteria (1)• Language Support and Standardization

– Does the language have a formal specification?– What support (documentation, tutorials, tech

support, …) is available?– Does the language implement a standard? If so,

who controls this standard?

• Data model/capabilities– How rich is the expressiveness of the language,

I.e., does the language support negation, conjunction, disjunction, relations, ...

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Evaluation Criteria (2)

• Performance– Scalability to real-world problems– Stability (languages with tools/environments)

• Other Issues/Pragmatics– Current users of the language– Domains in which the language has been

applied– Connection to data sources (knowledge sources

& storage formats (relational, OO, …))

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Initial Evaluation - Results

• Keys to acceptance:– Rich expressive power– Stability and history of use– Approachable/understandable syntax– Open to collaboration

• Keys to non-acceptance:– Proprietary language– Wedded to a commercial system

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Initial Evaluation - Results

Express Stable Tools Support Collab Status

Ontolingua High High High Med Med IN

OML/CKML High High Low Med High IN

XML/RDF Low Med Low High High OUT

OKBC Med High Med Med Med OUT

OPM Med High High Med Low OUT

CycL High High High Med Low OUT

UML/XMI Low Med High Low Low OUT

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Next Level Evaluation• Two languages stood out as strong

candidates;– Ontolingua– OML/CKML

• Conduct experiments to represent biological entities;– select two life sciences ontologies

• Ecocyc Gene Ontology

• GeneClinics data model/ontology

– represent each ontology in both Ontolingua and OML

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Gene Ontology - Ontolingua (1)(DEFINE-CLASS |Genes| (?X)"The class of all genes is divided into several subclasses. Genes whose function is unknown or known only approximately are grouped into the classes ORFs and Unclassified-Genes, respectively. Genes of known function have been classified using two orthogonal classification schemes developed by Monica Riley. One scheme classifies genes according to the physiological role of their product class (Physiological-Roles); the other scheme classifies genes according to the function of their product, such as enzymes and transport proteins (Product-Types).”:DEF (AND (|DNA-Segments| ?X))) ?VALUE)))

(DEFINE-FUNCTION CENTISOME-POSITION (?FRAME) :-> ?VALUE"This slot lists the map position of this gene on the chromosome in centisome units.”:DEF (AND (|Genes| ?FRAME) (NUMBER ?VALUE)))

(DEFINE-RELATION CITATIONS (?FRAME ?VALUE) "This slot lists general citations pertaining to the object containing the slot. Each value of the slot is a citation of the form[reference-id].”:DEF (AND (|Organisms| ?FRAME) (STRING ?VALUE)))

(DEFINE-RELATION COMMENT (?FRAME ?VALUE)"The Comment slot stores a general comment about the object that contains the slot.”:DEF (AND (:THING ?FRAME) (STRING ?VALUE)))

(DEFINE-FUNCTION COMMON-NAME (?FRAME) :-> ?VALUE "The primary name by which an object is known to scientists -- a widely used and familiar name (in some cases arbitrary choices must be made).”:DEF (AND (|Organisms| ?FRAME) (STRING ?VALUE)))

(DEFINE-RELATION EVIDENCE (?FRAME ?VALUE) "Describes evidence for the defined function of this object. Currently we distinguish between function that is determined experimentally, and function that is determined through computational sequence analysis.”:DEF (AND (|Genes| ?FRAME) ((:ONE-OF :EXPERIMENT :SEQUENCE-ANALYSIS) ?VALUE)))

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Gene Ontology - Ontolingua (2)(DEFINE-RELATION HISTORY (?FRAME ?VALUE)"Contains a textual history of changes made to this frame. Each item is either a string or a note frame.":DEF (AND (:THING ?FRAME) ((:OR :STRING |Notes|) ?VALUE)))

(DEFINE-FUNCTION INTERRUPTED? (?FRAME) :-> ?VALUE "The value of this slot is T for genes that are interrupted, i.e., those that have an early stop codon inserted.”:DEF (AND (|Genes| ?FRAME) (BOOLEAN ?VALUE)))

(DEFINE-FUNCTION LEFT-END-POSITION (?FRAME) :-> ?VALUE“”:DEF (AND (|DNA-Segments| ?FRAME) (NUMBER ?VALUE)))

(DEFINE-RELATION PRODUCT (?FRAME ?VALUE)"This slot lists the product of a gene, which could be a polypeptide or a tRNA. Multiple products will be recorded in the case that several chemically modified forms of the protein product exist." :DEF (AND (|Genes| ?FRAME) ((:OR |Polypeptides| RNA) ?VALUE)))

(DEFINE-RELATION PRODUCT-STRING (?FRAME ?VALUE) "This slot holds a text string that describes the product of this gene; this slot is only used when EcoCyc does not describe the gene product as a frame (such as a polypeptide frame).”:DEF (AND (|Genes| ?FRAME) (STRING ?VALUE)))

(DEFINE-RELATION PRODUCT-TYPES (?FRAME ?VALUE) "Describes the type of the gene product, e.g., is it an enzyme, an RNA, etc.”:DEF (AND (|Genes| ?FRAME) ((:ONE-OF :ENZYME :REGULATOR :LEADER :MEMBRANE :TRANSPORT :STRUCTURAL :RNA :PHENOTYPE :FACTOR :CARRIER) ?VALUE)))

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Gene Ontology - Ontolingua (3)(DEFINE-FUNCTION RIGHT-END-POSITION (?FRAME) :-> ?VALUE””:DEF (AND (|DNA-Segments| ?FRAME) (NUMBER ?VALUE)))

(DEFINE-RELATION SYNONYMS (?FRAME ?VALUE) "One or more secondary names for an object -- names that a scientist might attempt to use to retrieve the object. The Synonyms should include any name a user might use to try to retrieve an object.”:DEF (AND (|Generalized-Reactions| ?FRAME) (STRING ?VALUE)))

(DEFINE-FUNCTION TRANSCRIPTION-DIRECTION (?FRAME) :-> ?VALUE "This slot specifies the direction along the chromosome in which this gene is transcribed; allowable values are + or -." :DEF (AND (DNA ?FRAME) ((:ONE-OF "+" "-") ?VALUE)))

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Gene Ontology - OML/CKML (1)<CKML> <Ontology id="Riley's Gene Classes" version="1.0"> <comment> This OML ontology defines an encoding of the gene classificationsystem developed by Monica Riley. </comment> <extends ontology="http://www.ckml.org/ontology/" prefix="CKML"/> <Object type="Genes"> <comment> The class of all genes is divided into several subclasses. Genes whose function is unknown or known only approximately are grouped into the classes ORFs and Unclassified-Genes, respectively. Genes of known function have been classified using two orthogonal classification schemes developed by Monica Riley. One scheme classifies genes according to the physiological role of their product class (Physiological-Roles); the other scheme classifies genes according to the function of their product, such as enzymes and transport proteins (Product-Types). </comment> </Object>

<Function type="LEFT-END-POSITION" srcType="Genes" tgtType="data.Real"/> <Function type="INTERRUPTED?" srcType="Genes" tgtType="data.Boolean"> <comment> The value of this slot is T for genes that are interrupted, i.e., those that have an early stop codon inserted. </comment> </Function> <BinaryRelation type="HISTORY" srcType="CKML#Object" tgtType="data.String"> <comment> Contains a textual history of changes made to this frame. Each item is either a string or a note frame. </comment> </BinaryRelation> <Theory genus="Evidence"> <Object type="EXPERIMENT"/> <Object type="SEQUENCE-ANALYSIS"/> </Theory>

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Gene Ontology - OML/CKML (2)<BinaryRelation type="EVIDENCE" srcType="Genes" tgtType="Evidence"> <comment> Describes evidence for the defined function of this object. Currently we distinguish between function that is determined experimentally, and function that is determined through computational sequence analysis. </comment> </BinaryRelation> <Function type="CENTISOME-POSITION" srcType="Genes" tgtType="data.Real"> <comment> This slot lists the map position of this gene on the chromosome in centisome units. </comment> </Function> <BinaryRelation type="CITATIONS" srcType="CKML#Object" tgtType="data.String"> <comment> This slot lists general citations pertaining to the object containing the slot. Each value of the slot is a citation of the form [reference-id]. </comment> </BinaryRelation> <BinaryRelation type="COMMENT" srcType="CKML#Object" tgtType="data.String"> <comment> The Comment slot stores a general comment about the object that contains the slot. </comment> </BinaryRelation> <Function type="COMMON-NAME" srcType="CKML#Object" tgtType="data.String"> <comment> The primary name by which an object is known to scientists -- a widely used and familiar name (in some cases arbitrary choices must be made). </comment> </Function> <Theory genus="Transcription-Direction"> <Object type="+"/> <Object type="-"/> </Theory> <Function type="TRANSCRIPTION-DIRECTION" srcType="Genes"tgtType="Transcription-Direction"> <comment> This slot specifies the direction along the chromosome in which this gene is transcribed; allowable values are + or -. </comment> </Function> <BinaryRelation type="PRODUCT" srcType="Genes" tgtType="Polypeptides"/> </BinaryRelation>

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Gene Ontology - OML/CKML (3)<BinaryRelation type="SYNONYMS" srcType="CKML#Object" tgtType="data.String"> <comment> One or more secondary names for an object -- names that a scientist might attempt to use to retrieve the object. The Synonyms should include any name a user might use to try to retrieve an object. </comment> </BinaryRelation> <BinaryRelation type="PRODUCT-STRING" srcType="Genes" tgtType="data.String"> <comment> This slot holds a text string that describes the product of this gene; this slot is only used when EcoCyc does not describe the gene product as a frame (such as a polypeptide frame). </comment> </BinaryRelation> <Theory genus="Product-Types"> <Object type="ENZYME"/> <Object type="REGULATOR"/> <Object type="LEADER"/> <Object type="MEMBRANE"/> <Object type="TRANSPORT"/> <Object type="STRUCTURAL"/> <Object type="RNA"/> <Object type="PHENOTYPE"/> <Object type="FACTOR"/> <Object type="CARRIER"/> </Theory> <BinaryRelation type="PRODUCT-TYPES" srcType="Genes" tgtType="Product-Types"> <comment> Describes the type of the gene product, e.g., is it an enzyme, an RNA, etc. </comment> </BinaryRelation> <Function type="RIGHT-END-POSITION" srcType="Genes" tgtType="data.Real"/>

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<Collection.Object> <Genes id="EG10707" text="pheA"> <LEFT-END-POSITION tgt="2735765"/> <CENTISOME-POSITION tgt="58.97035d0"/> <TRANSCRIPTION-DIRECTION tgt="+"/> <RIGHT-END-POSITION tgt="2736925"/> </Genes> </Collection.Object> <Collection.BinaryRelation> <EVIDENCE src="EG10707" tgt="EXPERIMENT"/> <NAMES src="EG10707" tgt="pheA"/> <NAMES src="EG10707" tgt="b2599"/> <PRODUCT src="EG10707" tgt="CHORISMUTPREPHENDEHYDRAT-MONOMER"/> <PRODUCT-STRING src="EG10707" tgt="chorismate mutase-P and prephenate dehydratase"/> </Collection.BinaryRelation>

Gene Ontology - OML/CKML (4)

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Experiments - Results (Ecocyc)

• OML representation:– OML’s expressive capabilities captured most

aspects of gene ontology– some limitations in expressive capability; no

facets, cardinality or multiple collection types– terminology differences and definitions not

modular

• Ontolingua representation:– Ontolingua expressed all of gene ontology– Lisp syntax of Ontolingua not readily

approachable

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Experiments - Results (GeneClinics)

• OML representation:– Expressive capabilities adequate to the job– OML/CKML is based on conceptual graphs and

may have more expressive capabilities in the long term

• Ontolingua representation:– Ontolingua based on frames semantics which

more closely aligns with relational and OO data models

– Lisp syntax not acceptable to larger community

• Both languages would benefit from life sciences examples

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Conclusions and Recommendations

• The language most suitable for the exchange of life sciences ontologies should have the following key characteristics:– Frame-based representation

• Long history of work with frame-based representation model

• Mappings between this model and relational and/or OO data sources are easily expressed

– XML-based syntax• Critical for exchange among physically dispersed

community• New tools being developed in XML community• Lots of momentum in the web-based community

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Current Efforts

• Developed specification for an XML-based exchange language – XOL (XML Ontology Language) based on Ontolingua (Karp/Chaudhri)

• Frame-based semantics for OML/CKML

• Developing process for submission of life sciences ontologies to the Bio-Ontologies Consortium

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Other Ontology Efforts

• Gene Ontology Consortium (http://genome-www.stanford.edu/GO/)

• BioPathways Consortium (http://www.3rdmill.com/BioPathways)

• mmCIF (http://ndbserver.rutgers.edu/mmcif)

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Bio-Ontologies Consortium - Future Work

• Content development• Elicit and review ontology submissions• Synergies with OMG• Provide public-domain ontologies to the

Life Sciences community and encourage use of those ontologies

• Bio-Ontologies 2000

Documents

March 11, 2004 1 Knowledge Representation Chapter 10