Knowledge Representation Questions and Answers

Q: What is a truth maintenance system?

A TMS stores why each conclusion was drawn, so when a premise is withdrawn the conclusions depending on it can be retracted efficiently rather than by rebuilding everything from scratch. It is the practical machinery that makes non-monotonic reasoning affordable. Verification, consistency enforcement and timestamps are related but different. Recording the derivation as well as the result is what allows a system to change its mind without starting again.

Q: What is a description logic?

Description logics restrict expressiveness deliberately so that subsumption, whether one concept necessarily includes another, remains decidable and often tractable. They underpin the web ontology language. Program logics, single object restriction and natural language are different notions. Choosing what to give up in order to keep reasoning decidable, rather than seeking maximum expressiveness, is the design philosophy that distinguishes this family.

Q: What is subsumption in a description logic?

Subsumption asks whether one concept description necessarily includes another, which arranges the concepts into a hierarchy computed rather than declared. Instance checking is the related question about individuals, and knowledge base merging and simplification are different operations. That the taxonomy is derived from the definitions rather than asserted by hand is the principal advantage of the approach and it keeps the hierarchy consistent automatically.

Q: Why is a computed taxonomy better than an asserted one?

If the hierarchy is derived from the definitions, it is impossible for a concept to be placed under a parent it does not actually specialise, which in a large hand built ontology happens constantly. Multiple inheritance is supported by both approaches. Concept counts and speed are secondary. Deriving a structure from its definitions rather than maintaining it separately removes a whole class of error, which is the same argument that favours generated over hand written code.

Q: What is an ontology in this context?

An engineering ontology fixes the categories, the relations between them and the constraints they obey, so that several systems can exchange knowledge with a shared understanding. It borrows its name from the philosophical enquiry but is a practical artefact. Instance data populate it, and a dictionary lacks the formal structure. That interchange between systems requires agreement about categories, not merely about syntax, is what motivates the whole enterprise.

Q: What is an upper ontology?

An upper ontology fixes the most general distinctions, such as between objects and processes or between abstract and physical things, so that domain ontologies can be built beneath it consistently. Agreement at that level has proved contentious. Single top concepts, software layers and version currency are different notions. Whether general agreement about the highest categories is achievable at all is a genuine open question that has limited the interoperability the field hoped for.

Q: Why has agreement on an upper ontology proved difficult?

A medical ontology and a manufacturing one may need incompatible treatments of parts, processes and substances, and forcing both into one scheme distorts at least one of them. Practical projects therefore tend to agree locally and translate between ontologies rather than unify. Category counts, expressiveness and testability are secondary. That a shared standard may be unattainable rather than merely unagreed is a conclusion worth reaching before a great deal of effort is spent.

Q: What is the distinction between an object and a substance?

Half a chair is not a chair, but half a quantity of butter is still butter, so the two require different treatment in an ontology. Substances are handled as stuff with properties that are intrinsic rather than extrinsic. Both are physical and both are representable, and countability is a linguistic reflection of the distinction rather than its basis. A distinction that language marks and that matters for inference is exactly the kind an ontology must capture explicitly.

Q: What is an intrinsic property of a substance?

Cutting a quantity of butter in half leaves both halves with the same density and melting point, which are intrinsic, but each has half the mass and volume, which are extrinsic. That division is what allows inference about parts of a substance. Internal structure, destructiveness and uniqueness are different notions. Classifying properties by whether they survive division is what lets an ontology reason correctly about quantities rather than only about individuals.

Q: How are events usually represented in an ontology?

Reifying an event as an individual lets properties be attached to it, so one can say the meeting was long, was in London and involved three people, which a bare relation cannot express. That is why event calculus and similar formalisms treat events as objects. Transitions and functions are alternative treatments used in situation calculus and temporal logic respectively. Turning a relation into an object so that it can itself be described is the technique called reification and it recurs constantly.

11.

What is a truth maintenance system?

A.A mechanism ensuring the knowledge base remains consistent
B.A record of when each fact was added to the knowledge base
C.A component recording justifications so conclusions can be retracted
D.A system that verifies the truth of the facts against a database

Answer: C

A TMS stores why each conclusion was drawn, so when a premise is withdrawn the conclusions depending on it can be retracted efficiently rather than by rebuilding everything from scratch. It is the practical machinery that makes non-monotonic reasoning affordable. Verification, consistency enforcement and timestamps are related but different. Recording the derivation as well as the result is what allows a system to change its mind without starting again.

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12.

What is a description logic?

A.A logic for describing the behaviour of a program
B.A logic in which every sentence describes a single object
C.A logic using ordinary natural language descriptions as its sentences
D.A decidable fragment of first order logic for concept definitions

Answer: D

Description logics restrict expressiveness deliberately so that subsumption, whether one concept necessarily includes another, remains decidable and often tractable. They underpin the web ontology language. Program logics, single object restriction and natural language are different notions. Choosing what to give up in order to keep reasoning decidable, rather than seeking maximum expressiveness, is the design philosophy that distinguishes this family.

Discuss in Forum

13.

What is subsumption in a description logic?

A.Determining whether every instance of one concept is an instance of another
B.Absorbing one knowledge base into a larger one
C.Deciding whether one particular object belongs to a given named concept
D.Replacing a complex concept with a simpler equivalent one

Answer: A

Subsumption asks whether one concept description necessarily includes another, which arranges the concepts into a hierarchy computed rather than declared. Instance checking is the related question about individuals, and knowledge base merging and simplification are different operations. That the taxonomy is derived from the definitions rather than asserted by hand is the principal advantage of the approach and it keeps the hierarchy consistent automatically.

Discuss in Forum

14.

Why is a computed taxonomy better than an asserted one?

A.It permits concepts to belong to several parents at once
B.It cannot become inconsistent with the concept definitions
C.It requires fewer concepts to be defined in the first place
D.It can be computed more quickly than a hierarchy can be read

Answer: B

If the hierarchy is derived from the definitions, it is impossible for a concept to be placed under a parent it does not actually specialise, which in a large hand built ontology happens constantly. Multiple inheritance is supported by both approaches. Concept counts and speed are secondary. Deriving a structure from its definitions rather than maintaining it separately removes a whole class of error, which is the same argument that favours generated over hand written code.

Discuss in Forum

15.

What is an ontology in this context?

A.A database of instances belonging to known categories
B.A dictionary defining all the terms used in a knowledge base
C.A formal specification of the categories and relations of a domain
D.A philosophical theory about what kinds of thing exist

Answer: C

An engineering ontology fixes the categories, the relations between them and the constraints they obey, so that several systems can exchange knowledge with a shared understanding. It borrows its name from the philosophical enquiry but is a practical artefact. Instance data populate it, and a dictionary lacks the formal structure. That interchange between systems requires agreement about categories, not merely about syntax, is what motivates the whole enterprise.

Discuss in Forum

16.

What is an upper ontology?

A.The topmost concept in a particular domain's hierarchy
B.An ontology describing the software upper layers of a large system
C.The most recently revised version of a shared ontology
D.A very general framework of categories applicable to any domain

Answer: D

An upper ontology fixes the most general distinctions, such as between objects and processes or between abstract and physical things, so that domain ontologies can be built beneath it consistently. Agreement at that level has proved contentious. Single top concepts, software layers and version currency are different notions. Whether general agreement about the highest categories is achievable at all is a genuine open question that has limited the interoperability the field hoped for.

Discuss in Forum

17.

Why has agreement on an upper ontology proved difficult?

A.Different domains carve the world up in incompatible ways
B.The categories involved are far too numerous to enumerate
C.No formal language is expressive enough to state them
D.Upper ontologies cannot be tested against any data

Answer: A

A medical ontology and a manufacturing one may need incompatible treatments of parts, processes and substances, and forcing both into one scheme distorts at least one of them. Practical projects therefore tend to agree locally and translate between ontologies rather than unify. Category counts, expressiveness and testability are secondary. That a shared standard may be unattainable rather than merely unagreed is a conclusion worth reaching before a great deal of effort is spent.

Discuss in Forum

18.

What is the distinction between an object and a substance?

A.Objects can be represented and substances cannot be
B.Objects have identity and parts, substances divide without limit
C.Objects are physical while substances are purely abstract concepts
D.Objects are countable and substances are always continuous

Answer: B

Half a chair is not a chair, but half a quantity of butter is still butter, so the two require different treatment in an ontology. Substances are handled as stuff with properties that are intrinsic rather than extrinsic. Both are physical and both are representable, and countability is a linguistic reflection of the distinction rather than its basis. A distinction that language marks and that matters for inference is exactly the kind an ontology must capture explicitly.

Discuss in Forum

19.

What is an intrinsic property of a substance?

A.One that cannot be measured without destroying a sample
B.One that distinguishes the substance from every other
C.One retained by every part, such as density or colour
D.One belonging to the substance's internal structure only

Answer: C

Cutting a quantity of butter in half leaves both halves with the same density and melting point, which are intrinsic, but each has half the mass and volume, which are extrinsic. That division is what allows inference about parts of a substance. Internal structure, destructiveness and uniqueness are different notions. Classifying properties by whether they survive division is what lets an ontology reason correctly about quantities rather than only about individuals.

Discuss in Forum

20.

How are events usually represented in an ontology?

A.As relations holding between all of the objects involved
B.As transitions between two states of the world
C.As functions mapping times to propositions
D.As individuals with participants, times and locations

Answer: D

Reifying an event as an individual lets properties be attached to it, so one can say the meeting was long, was in London and involved three people, which a bare relation cannot express. That is why event calculus and similar formalisms treat events as objects. Transitions and functions are alternative treatments used in situation calculus and temporal logic respectively. Turning a relation into an object so that it can itself be described is the technique called reification and it recurs constantly.

Discuss in Forum

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