Computer Science, building 42.1 Roskilde University Universitetsvej 1 P.O. Box 260 DK-4000 Roskilde Denmark Phone: +45 4674 2000 Fax: +45 4674 3072

Computer Science, building 42.1Roskilde University

Universitetsvej 1 P.O. Box 260

DK-4000 RoskildeDenmark

Phone: +45 4674 2000Fax: +45 4674 3072

www.dat.ruc.dk

Ontology-based Information Retrieval

PhD. Defense

Henrik Bulskov

Henrik Bulskov 2November 3th, 2006

Research Question

• How do we recognize concepts in information objects and queries, represent these in the information retrieval system, and use the knowledge about relations between concepts captured by ontologies in the querying process?

How to:

• Describe Recognize and map information in documents and queries into the ontologies,

• CompareImprove the retrieval process by use of similarity measures derived from knowledge about relations between concepts in ontologies, and

• Retrieve Introduce ontological indexing and ontological similarity into a realistic information retrieval scenario.


Outline

• Introduction• Ontologies

• Information Retrieval

• Knowledge Representation

• Ontological Indexing• Instantiated Ontologies• Ontological Similarity• Query Evaluation• Prototype• Conclusion and Further Work


Ontologies

• In Philosophy:“A science or study of being”“First philosophy”

• In Knowledge Engineering:“A formal, explicit specification of a shared conceptualization”


Information Retrieval Models

• Boolean Model• Vector Model• Probabilistic Model• Fuzzy Retrieval Model• Fuzzy Sets

classical set d = {t1, t2, t3}

fuzzy set dfuzzy = {1.0/t1, 0.5/t2, 0.1/t3, 0.0/t4}


Knowledge Representation

• A knowledge representation is a surrogate. Most of the things that we want to represent cannot be stored in a computer, e.g. bicycles, birthdays, motherhood, etc.

• A knowledge representation is a set of ontological commitments. Representations are imperfect approximations of the world, each attending to some things and ignoring others.

• A knowledge representation is a fragmentary theory of intelligent reasoning. To be able to reason about the things represented, the representation should also describe their behavior and intentions.

• A knowledge representation is a medium for efficient computation. Remarks on useful ways to organize information are given.

“What is a knowledge representation?” [Davis et al., 1993]


A Graphical Representation

• We aim at reasoning by means of a “nearness” principle, where increased nearness entails increased degree of similarity

• Can be based on any formalism that has a suitable network or graphical representation encompassing the semantics expressed


Representation Formalisms

• Semantic Networks• Frames• Descriptions Logic• Lattice Algebra• OntoLog

A lattice-algebra with “attribution” (by means of Peirce Product)


OntoLog - A Lattice-algebraic Approach

• Compound concepts are built from • Atomic concepts of the ontology, and• Attribution:

• Attribute features using semantic relations like• WRT: with respect to • CHR: characterized by (property ascription) • CBY: caused by • PNT: patient of act or process • LOC: location, position • ...

• Concept Examples: • “The black cat”

• cat[CHR: black]

• “The noise caused by the black dog”• noise[CBY: dog[CHR: black]]


Mapping OntoLog Expressions into the Ontology


Ontological Resource• WordNet• A large lexical database organized in terms of

meanings.

• Nouns, Adjectives, Adverbs, and Verbs

• Synonym words are grouped into synset{car, auto, automobile, machine, motorcar}

{food, nutrient}{police, police force, constabulary, law}

• Number of words, synsets, and senses


Ontological Indexing

• Describing Content• Conventional approach: bag of keywords• Aim: Moving from keywords Concepts

• Description Properties• Fidelity

Ability to represent the content

• ExhaustivityDegree of recognized concepts

• SpecificityGeneric level of the concepts

• Level of abstractionComplexity of descriptions


Descriptions Example

“physical well-being caused by a balanced diet”

{{well-being[CHR:physical]}, {diet[CHR:balanced]}}

well-being[CHR:physical, CBY:diet[CHR:balanced]]

{{“physical”, “well-being”}, {“balanced”, “diet”}}

{“physical”, “well-being”, “balanced”, “diet”}


Simple Natural Language Processing

• Part-of-speech tagging

Example: “The black book on the small table”

The/DT black/JJ book/NN on/IN the/DT small/JJ table/NN

• Noun phrase recognition

<PP [NP The/DT black/JJ book/NN] on/IN [NP the/DT small/JJ table/NN]

>

• Extracting Descriptions

book[CHR:black, LOC:table[CHR:small]]


Word Sence Disambiguation

• Guessing (about 45% correct senses)

• Frequencies (increased from 45% to 69%)

• Selectional Restrictions

e,x,y(Drinking(e) ^ Agent(e,x) ^ Theme(e,y)) ISA(y, DrinkableThing)

Example: “He drank gin”

{gin} (strong liquor flavored with juniper berries) {snare, gin, noose} (a trap for birds or small mammals; … a slip noose) {cotton gin, gin} (a machine that separates the seeds … cotton fibers) {gin, gin rummy, knock rummy} (a form of rummy in which a player …)


Relational Connections

• Noun phrasesExample:

“Physical well-being caused by a balanced diet”

well-being[CHR:physical, CBY:diet[CHR:balanced]]

• Relations derived from verbsThe CBY relation between “well-being” and “diet”

• Relations inside simple NP’sThe CHR between “diet” and “balanced”


Relational Connections, cont.

• PremodifierExamples: ”black book“ and “criminal lawyer”book[CHR:black] is correct, butlawyer[CHR:criminal] is not.

Use knowledge from ontologeis to differentiate between descriptive and relational adjective, and use WRT relation for the latter

lawyer[WRT:criminal]

• Preposition“The black book on the small table”

book[CHR:black, LOC:table[CHR:small]]“The meeting on Monday”

meeting[TMP:Monday]


Compound Descriptions


General Ontologies

• Modeling of concepts in a generative ontology based on different conceptualization and dictionaries.

• We assume a set of atomic concepts A and a set of semantic relations R

• We define here the set of well-formed terms L of the OntoLog language is recursively defined as follows:

if x isa y then x ≤ yif x[…] ≤ y[…] then also

x[…, r:z] ≤ y[…], andx[…, r:z] ≤ y[…, r:z],

if x ≤ y then alsoz[…, r:x] ≤ z[…, r:y]

Definition: O = (L, ≤, R)


Ontological Similarity

• We aim at reasoning by means of a “nearness” principle, where increased nearness entails increased degree of similarity

• Properties• Commonality, Difference, Identity

• Generalization• sim(animal,poodle) ≠ sim(poodle,animal)

• Depth

• Multiple-Paths


Ontological Similarity Measures

• Knowledge-based• Shortest Path

• Corpus-based• Co-occurrences

• Integrated • Information Content

• probability of encountering an instance: p(c) = freq(c)/N

• Shared Nodes (on instantiated ontologies)


Shared Nodes

• A simplified “all-possible-paths” approach where shared nodes are nodes that are upwards reachable from both concepts, and where similarity is dependent on the number of shared nodes


Shared Nodes, cont.

• Nodes shared by grey animals


Shared Nodes, cont.

sim(dog[CHR:gray ], cat[CHR:gray ]) > sim(dog[CHR:gray ], dog[CHR:large ])

Counterintuitive: • concept-inclusion (ISA) should have higher importance than characterized-by (CHR) property


Weighted Shared Nodes• Not all nodes are equally important

sim(dog[CHR:gray ], cat[CHR:gray ]) > sim(dog[CHR:gray ], dog[CHR:large ])

solution: attach weights in [0,1] to relations so the nodes upwards reachable from x: (x) becomes a fuzzy

set


Query Evaluation - Simple Fuzzy Retrieval

• Assign to each pair (di,cj) a value which define the relevance of cj to di

• Compute the relevance of documents to a given query as

d1 = {1/c1 + 1/c2 + 0/c3}

d2 = {1/c1 + 0/c2 + 1/c3} andQ = {c1, c2}

d3 = {0/c1 + 0/c2 + 1/c3}

Qc Q

QdQcQQ c

cc

Q

QdddRSV

)(

))(),(min(

||

||)()(


Query Evaluation - Hierarchical Aggregation

<q1(d),

<q2(d), q3(d),

<q4(d), q5(d), q6(d) : M3 : K3>

: M2 : K2>,

: M1 : K1>

M1

K1

q1(d) M2

K2

M3

K3

q2(d) q3(d)

q4(d) q5(d) q6(d)


Prototype – Using WordNet

• Adjectives are not connected by ISA


Conclusion

• Describe• Properties of descriptions• Shallow NLP analysis • Instantiated Ontologies

• Compare• Shared Nodes• Weighted Shared Nodes

• Retrieve• Semantic Expansion• Hierarchical Query Evaluation

• Prototype• Theoretical foundation

Documents

Computer Science, building 42.1 Roskilde University Universitetsvej 1 P.O. Box 260 DK-4000 Roskilde Denmark Phone: +45 4674 2000 Fax: +45 4674 3072