using non-taxonomic knowledge to improve semantic matching peter yeh july 22, 2003
TRANSCRIPT
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Using Non-Taxonomic Knowledge to Improve Semantic Matching
Peter Yeh
July 22, 2003
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Talk Outline
• Introduction
• Analysis of Existing Techniques
• Our Approach
• Initial Evaluation
• Proposed Work
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Introduction
• Many AI tasks require determining whether two knowledge representations encode the same knowledge.
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Information Retrieval
• Match queries with documents.
Q: “A car with a bumper made of gold.”
Car
Bumper Gold
has-part
material
Car
Gold
material
Car Acme
Produce agentobject
Car
Bumper Gold
has-part
material
A: “Acme makes a car made of Gold.”
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Knowledge Acquisition• Match new knowledge with existing knowledge.
KB
KB: Are you trying to encode a conversion?
Microbe Pollution
agent object
Destroy Createcauses
Food
result
Microbe Pollution
agent object
Destroy Createcauses
Food
result
New Knowledge
Conversion
next-event
agent object
Destroy Create
resultagent
subevent subevent
EntityEntity Entity
Existing Knowledge
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Rule-based Classification• Match rule antecedents with working memory. For example,
Course of Action (COA) critiquing.
Attack Delay
Military-Unit agentagent
objectobject
causes
Military-Unit
IF
THEN<good, Enemy-Maneuver-Engagement>
Attack Block Delay
Advance
Artillery-Unit
Armor-Unit
agentagent
object
object
object
agent
agent
causes causes
Pattern COA
Attack Delay
Military-Unit agentagent
objectobject
causes
Military-Unit
“This COA has a rating of good for enemy maneuver engagement.”
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The Core Problem
• Solving this matching problem is hard because multiple encodings of the same knowledge rarely match exactly.
• Representations don’t match exactly because: – Expressive Ontology. – Knowledge is encoded by different sources.– Knowledge being encoded is complex.
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Types of Mismatches
• Informal examination of a knowledge-base containing: – Patterns. – COAs.
• Knowledge-base was built by two Subject Matter Experts (SMEs) participating in DARPA’s RKF project.
• Looked for cases of mismatch.
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Types of Mismatches (cont.)
• Taxonomic Differences
“an armored brigade engaging an armored battalion.”
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Types of Mismatches (cont.)
• Taxonomic Differences• Equivalent Alternatives
“One military unit attacking another unit.”
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Types of Mismatches (cont.)
• Taxonomic Differences• Equivalent Alternatives• Omissions
“Mechanized infantry brigade engaging mechanized infantry
battalion.”
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Types of Mismatches (cont.)
• Taxonomic Differences• Equivalent Alternatives• Omissions• Granularity
“Support attack occurs before main attack.”
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Analysis of Existing Techniques
• Analogy
• Inexact Matching
• Semantic Matching
• Conceptual Indexing
• Ontology Merging
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Analogy
• Analogy: mapping of knowledge from a base domain to a target domain.
• Structure Mapping Engine (Forbus et. al. 89): – Maps relational knowledge (mappable systems).– Systematicity Principle used to select best analogy.
• Analogy based on common generalizations (Leishman 92)– Maps both relational knowledge and object
attributes.– Prefers minimal common generalization.
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Analogy: Structure Mapping Engine
Attack Delay
Military-Unit agentagent
objectobject
causes
Military-Unit
agentagent
objectobject
causes
Attack Block Delay
Advance
Artillery-Unit
Armor-Unit
agentagent
object
object
object
agent
agent
causes causes
agentagent
object
object
object
agent
agent
causes causes
agent
object
object
agent
causes
agent
object
object
agent
causes
agent
object
object
agent
causes
agent
object
object
agent
causes
Attack Block
Artillery-Unit
Armor-Unit
agent
object
object
agent
causes
Block Delay
Artillery-Unit
Armor-Unit
agent
object
object
agent
causesAttack Delay
Military-Unit agentagent
objectobject
causes
Military-Unit
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Inexact Matching
• Inexact Matching: tries to address mismatches between representations
• Graph Editing (Tsai et. al. 83, Shapiro and Haralick 81, Messmer et. al. 93, Wolverton et. al. 2003)– Uses edit distance parameters. – Similarity based on shortest sequence of edits.
• Partial Matching – Does not require representations to be isomorphic.– Similarity based on amount of structural overlap.– Minimal Common Supergraph (Bunke et. al. 2000) and
Maximal Common Subgraph (Bunke and Shearer 98).
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Inexact Matching: MCS
Attack Delay
Military-Unit agentagent
objectobject
causes
Military-Unit
Attack Block Delay
Advance
Artillery-Unit
Armor-Unit
agentagent
object
object
object
agent
agent
causes causes
Block Delay
Artillery-Unit
Armor-Unit
agent
object
object
agent
causes
Attack Block
Artillery-Unit
Armor-Unit
agent
object
object
agent
causes
Attack Delay
Military-Unit agentagent
objectobject
causes
Military-Unit
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Semantic Matching• Semantic Matching: uses knowledge to match
representations.• Projection:
– Uses taxonomic knowledge. – Ontoseek (Guarino et. al. 99) and ELEN (Huibers et. al. 96).
• Projection+: Projection alone is too restrictive -projection (Genest and Chein 97).– Common generalization, graph splitting, regular expressions
(Fargues 92, Buche et. al. 2000, Martin et. al. 2001).
• Semantic Overlap– Maximal Joins and Generalizations (Myaeng 92, Poole et.
al. 95).– Shared Semantic Structures (Zhong et. al. 2002).
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Semantic Matching: Semantic Overlap
Attack Delay
Military-Unit agentagent
objectobject
causes
Military-Unit
Attack Block Delay
Advance
Artillery-Unit
Armor-Unit
agentagent
object
object
object
agent
agent
causes causesAttack Block Delay
Advance
Artillery-Unit
Armor-Unit
agentagent
object
object
object
agent
agent
causes causesAttack Delay
Artillery-Unit
Armor-Unit
agentagent
objectobject
Attack Delay
Artillery-Unit
Armor-Unit
agentagent
objectobject
Attack Delay
Military-Unit agentagent
objectobject
causes
Military-Unit
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Conceptual Indexing
• Conceptual indexing: how to organize and index knowledge.
• Requires so form matching.• Generalization hierarchy (Bournard et. al. 95,
Ellis 92, Levinson 82, Woods 97). – Knowledge indexed by common generalizations. – Generalizations organized hierarchically by
subsumption relationships. – Retrieve Most Specific Subsumer (MSS) of a query.
• Match procedure is similar to Projection - suffers the same problems.
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Ontology Merging and Translation
• Ontology Merging: merge multiple ontologies built by different sources– Chimaera (McGuinness et. al. 2000)– SMART (Noy and Musen 99).
• Ontology Translation: translates a representation from one language to another– Ontomorph (Chalupsky 2000).
• Goals are different but share some of the same problems.
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Our Approach
• The goal of this research is to solve the matching problem.
• We believe existing semantic approaches can be extended with additional knowledge to significantly improve matching.
• What kinds of additional knowledge?– Transformations
• Handle mismatches.• Improve matching.
– Not taxonomic knowledge.
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Our Approach (cont.)
• Generality and domain-independence.– Want additional knowledge (e.g. Transformations) to
be useful across domains.
• We believe domain-independence is possible given a reusable domain-neutral upper ontology.– Contains a small set of general concepts.
– SMEs use this upper ontology to build KBs on specialized topics (e.g. chemistry, biology, battle space planning).
– No training in logic or knowledge representation.
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Illustration of Our Framework
Transformations
Ontology
KB
KE
SME/KE KB can be viewed as a domain-specific matcher (e.g. match symptoms to diseases).
Domain-independent KB for the task of matching.
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Our Prototype
• Extend semantic matchers with transformations.• Apply transformations in a forward-chaining
manner.• Use existing techniques for reasoning with
Conceptual Graphs (Corbett et. al. 99, Salvat et. al. 96, Willems 95): – Projection.– Unification.– Graph rules.
• Two caveats because existing techniques lead to promiscuous matches.
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Transformations that Retains Semantics
Buy object
agent
origin
Car
Person: Y
Person: X
Buy object
agent
origin
Car
Person: Y
Person: X
Car Like
Person: X
object
agent
Projection
Car Likeobject
agent
Driving-License possesses
Buy object
agent
origin
Car
Person: John
Person: Bob
Driving-License possesses
Buy object
agent
origin
Car
Person: John
Person: BobCar Sell
Person
object
agent
recipientPerson
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Transformations that Retains Semantics
Buy object
agent
origin
Car
Person: Y
Person: X
Driving-License possesses
Buy object
agent
origin
Car
Person: John
Person: BobCar Sell
Person
object
agent
recipientPerson
Buy object
agent
origin
Car
Person: Y
Person: X
Car Sell
Person: Y
object
agent
Driving-License possesses
Buy object
agent
origin
Car
Person: John
Person: Bob
Sellobject
agent
Car
Person: John
Sellobject
agent
Car Sell
Person: Y
object
agent
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Rule Applicability
Buy object
agent
origin
Car
Person: Y
Person: X
Car Sell
Person: Y
object
agent
Driving-License possesses
Buy object
agent
origin
Car
Person: John
Person: Bob
Sellobject
agent
Driving-License possesses
Buy object
agent
origin
Car
Person: John
Person: Bob
Buy object
agent
origin
Car
Person: Y
Person: X
Buy object
agent
origin
Car
Person
Person
Driving-License possesses
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Rule Applicability
Driving-License possesses
Buy object
agent
origin
Car
Person: John
Person: BobCar Sell
Person
object
agent
recipientPerson
Buy object
agent
origin
Car
Person: Y
Person: X
Car Sell
Person: Y
object
agent
Driving-License possesses
Buy object
agent
origin
Car
Person: John
Person: Bob
Sellobject
agent
Buy object
agent
origin
Car
Person: Y
Person: XCar Sell
Person: Y
object
agent
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Enumerating Transformations
• Transformations derived from our domain-neutral upper ontology.
• Enumerated all ways that a relation can be legally used to encode information in a conceptual graph.
• Considered whether the same information can be expressed differently.
• Enumeration was possible because:– Small upper ontology. – Each concept had well-defined semantics.
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Transformations Enumerated
• We were able to enumerate about 300 transformations.
• Resulting transformations fall into three general categories:– Transitivity – Part Ascension – Transfers Through
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Transformations Enumerated (cont.)
relation Transitive Part Ascension Transfers Through
causes X - subevent, resulting-state
caused-by X subevent-of resulting-from
defeats - - -
defeated-by - subevent-of caused-by
enables X - causes, resulting-state, subevent
enabled-by X subevent-of caused-by, resulting-from
inhibits - subevent-of resulting-state
inhibited-by - subevent-of caused-by, resulting-from
by-means-of X - -
means-by-which X - -
prevents - subevent-of -
prevented-by - subevent-of caused-by, resulting-from
resulting-state - - causes
resulting-from - - -
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Example: Our Approach
Attack Delay
Military-Unit agentagent
objectobject
causes
Military-Unit
Attack Block Delay
Advance
Artillery-Unit
Armor-Unit
agentagent
object
object
object
agent
agent
causes causes
agent
agent
object
objectMilitary-Unit
Delaycauses
Attack
Military-Unit
Attack
Attack
Military-UnitDelay
Military-Unit
Delay
1:
2:
3:
4:
5:
agent
agent
object
object
causes
Attack
Attack
Attack
Delay
Delay
A:
B:
C:
D:
E:
Advance agent
Block
Artillery-Unit
Artillery-Unit
Artillery-UnitBlock
Armor-Unit
Armor-Unit
object
agent
Block Armor-Unit
Block Delaycauses
Armor-Unit
F:
G:
H:
I:
l1 = l1 = {(1,A)}
M = { }{(1,A)}
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Example: Our Approach
{(1, A)}, {(3,C)}, {(4,D)}, {(5,E)} }
M = {
A
B
C D
E
F
G
H
I
Attack Block Delay
Advance
Artillery-Unit
Armor-Unit
agentagent
object
object
object
agent
agent
causes causes
Attack Delay
Military-Unit agentagent
objectobject
causes
Military-Unit1
4
2
3
5
A
B
C D
E
F
G
H
I
Attack Block Delay
Advance
Artillery-Unit
Armor-Unit
agentagent
object
object
object
agent
agent
causes causes
Attack Delay
Military-Unit agentagent
objectobject
causes
Military-Unit1
4
2
3
5
A
B
C D
E
F
G
H
I
Attack Block Delay
Advance
Artillery-Unit
Armor-Unit
agentagent
object
object
object
agent
agent
causes causes
Attack Delay
Military-Unit agentagent
objectobject
causes
Military-Unit1
4
2
3
5
A
B
C D
E
F
G
H
I
Attack Block Delay
Advance
Artillery-Unit
Armor-Unit
agentagent
object
object
object
agent
agent
causes causes
Attack Delay
Military-Unit agentagent
objectobject
causes
Military-Unit1
4
2
3
5
A
B
C D
E
F
G
H
I
Attack Block Delay
Advance
Artillery-Unit
Armor-Unit
agentagent
object
object
object
agent
agent
causes causes
Attack Delay
Military-Unit agentagent
objectobject
causes
Military-Unit1
4
2
3
5
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Example: Our Approach
Attack Block Delay
Advance
Artillery-Unit
Armor-Unit
agentagent
object
object
object
agent
agent
causes causes
Attack Delay
Military-Unit agentagent
objectobject
causes
Military-Unit
Transformations
Action Action Actioncauses causes
Action Actioncauses
Action Action Actioncauses causesAction Actioncauses
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Example: Our Approach
Transformations
Action Action Actioncauses causes
Action Actioncauses
Attack Block Delay
Advance
Artillery-Unit
Armor-Unit
agentagent
object
object
object
agent
agent
causes causes
Attack Delay
Military-Unit agentagent
objectobject
causes
Military-Unit
causes
Attack Block Delay
Advance
Artillery-Unit
Armor-Unit
agentagent
object
object
object
agent
agent
causes causes
Attack Delay
Military-Unit agentagent
objectobject
causes
Military-Unit
causes
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Initial Evaluation
• Used our matcher in an application in the domain of battle space planning (DARPA's RKF Project).
• The task is to analyze COAs.• Battle space ontology built by extending our upper
ontology.• Two military analysts used this ontology to build
KBs containing: – Patterns. – COAs.
• Our matcher matched the patterns to COAs.
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Example Output
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Experiment 1
• Evaluates our first hypothesis.– How significant is the improvement?
• Compared our matcher to: – Maximal Common Subgraph (MCS). – Semantic Search Lite (SSL).
• Methodology:– 300 domain-neutral transformations; 80 domain-specific
transformations.– Matched the patterns to the COAs. – A pattern matches a COA if the match score meets or
exceeds a pre-specified threshold. – Used metrics of precision and recall.
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Experiment 1: Precision
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Experiment 1: Recall
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Experiment 2
• Initial evaluation of our second hypothesis.– Assesses the domain independence of using
transformations.
• Limited - conducted in only one domain, but can still offer some insight.
• Methodology: – Divided transformations into 2 groups (domain-neutral
vs. domain-specific). – Used domain-neutral transformations to construct DN – Used domain-specific transformations to construct DS – Everything else is the same as Experiment 1.
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Experiment 2: Precision
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Experiment 2: Recall
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Proposed Work
• More Comprehensive Evaluation.
• Use background knowledge.
• Incorporate indexing to make matching more efficient.
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Comprehensive Evaluation
• Evaluate our approach in several applications in four domains.
• Four data sets:– Chemistry (Halo). – Biology (RKF). – Battle Space Planning (RKF). – Office Procedures (EPCA).
• Three Applications:– Elaboration: Chemistry and Office Procedures.– Question Answering: Biology and Battle Space.– Plan Evaluation: Battle Space and Office Procedures.
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Background Knowledge
• Background Knowledge.
• Can be used to normalize new knowledge at acquisition time via a join (Mineau et. al. 93).
• Idea can be applied to matching. – Increase similarity.
• Two problems:– When should a join be performed?
– How to better control the join?
Ontology
Block Move
object
object
prevents
Military-Unit
Block
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Background Knowledge
• Background Knowledge.
• Can be used to normalize new knowledge at acquisition time via a join (Mineau et. al. 93).
• Idea can be applied to matching. – Increase similarity.
• Two problems:– When should a join be performed?
– How to better control the join?
Block Move
object
object
prevents
Military-Unit
Attack Block
object
Military-Unit
causes
object
Attack Move
object
object
prevents
Military-Unit
Block Move
object
object
prevents
Military-Unit
Attack
object
Military-Unit
Attack
Military-Unitobject
Move
objectMilitary-Unit
Move
objectMilitary-Unit
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Indexing
• Need indexing to make matching more efficient.
• A common technique is a generalization hierarchy– Overhead for storage can be expensive.– Finding the MSS can also be expensive.
• We intend to study:– How to index knowledge by content?– Other index structures that are more
parsimonious.