Ontology Generation and Applications

1
Ontology Generation and Applications

• Dr. A.C.M. Fong, CEng
• Professor of Computer Engineering
• School of Computing and Mathematical Sciences
• Faculty of Design and Creative Technologies
• Auckland University of Technology
• afong_at_aut.ac.nz

2
Contents

• Introduction Semantic Web and Ontology
• Related Work Ontology Generation
• Toward Automated Ontology Generation
• Fuzzy Ontology Generation Framework
• Application 1 Scholarly Info
• Application 2 Service Helpdesk

3
IntroductionSemantic Web

• The basis for the Semantic Web is on its ability
  to represent real-life domains accurately so that
  it enables programs to completely understand the
  environment in which they operate.
• In summary, Semantic Web provides the following
  benefits
• SWeb offers an expressive metadata model to
  represent data, so that data can be managed
  effectively.
• Programs can understand the semantic concepts
  described in metadata used on Semantic Web.
  Hence, knowledge carried on the Semantic Web can
  be shared and reused among different programs.
• Users can interact with programs using a semantic
  query language to specify their requests and
  thereby improving the retrieval performance.
• Deductive mechanism that is used to derive new
  information from existing information can be
  described clearly, so that knowledge can be
  reasoned with efficiently.

4
IntroductionSemantic Web Architecture
5
IntroductionSemantic Web Architecture - Layers

• Foundation Layer. Semantic Web uses Uniform
  Resource Identifier URI to identify resources and
  uses Unicode to encode the documents.
• Schema Layer. This layer comprises XML NS
  (Namespace) xmlschema layer and the RDF
  rdfschema layer.
• This layer defines objects and classes, their
  relations and constrains. The XML Schema (XMLS)
  and RDF Schema (RDFS), which are based on XML and
  RDF respectively, are used for these layers.
• RDFS has widely been used to describe classes at
  the Schema Layers.

6
IntroductionSemantic Web Architecture - Layers

• Ontology Layer. This layer provides constructs on
  using meta-information to represent domain
  knowledge.
• In this layer, information is represented as
  ontology, which is adopted by the Semantic Web to
  define knowledge.
• Logic Layer. This layer infers more knowledge
  from the existing knowledge. It can be integrated
  with the Ontology Layer.
• In this layer, concepts and relationships defined
  in lower layers are converted into
  Turing-complete logic languages in order to
  generate new knowledge.

7
IntroductionSemantic Web Architecture - Layers

• Proof Layer. This layer provides a mechanism to
  check whether a statement is true or not.
• Trust Layer. This Layer provides a mechanism
  which resolves conflicts between knowledge
  carried by the Semantic Web to form the "Web of
  Trust"
• Digital Signature Layer. This layer uses public
  key cryptography to secure documents.

8
IntroductionOntology Definition

• Ontology has different definitions. A commonly
  cited definition defines ontology as a formal,
  explicit specification of a shared
  conceptualization.
• Conceptualization refers to an abstract model of
  phenomena in the world by having identified the
  relevant concepts of those phenomena.
• Explicit means that the type of concepts used,
  and the constraints on their use are explicitly
  defined.
• Formal should be machine readable.
• Shared should capture consensual knowledge
  accepted by the communities.

9
IntroductionOntology Research

• Ontology is regarded as a standard conceptual
  model for knowledge representation, especially on
  Semantic Web.
• The term ontology engineering has been proposed
  to imply ontology-related research in computer
  science
• Current interesting issues on ontology
  engineering include ontology generation, ontology
  mapping, ontology integration and ontology
  versioning.
• This presentation focuses on ontology generation.

10
IntroductionOntology Description Languages

• Ontology is described using an ontology
  description language.
• Ontology description languages are based on Web
  metadata description languages, which can be
  classified into the following three groups
• HTML-based
• XML-based
• RDF- based

11
IntroductionHTML-based Ontology Description
Languages

• The tags supported by traditional Web are
  sufficient to represent some semantic knowledge.
• Simple HTML Extension (SHOE) and Ontobroker have
  embedded additional tags into HTML to represent
  knowledge.
• However, HTML does not support self-defined tags.
  Therefore, HTML-based approach is difficult to
  define classes for ontology.
• Hence, XML-based ontology description languages
  have been proposed to overcome this limitation.

12
IntroductionXML-based Ontology Description
Languages

• These languages are usually based on XML Schema
  (XMLS) or Document Type Definition (DTD).
• DTD allows users to define new markup types to
  describe information. Therefore, users can define
  ontology classes using DTD.
• Moreover, XMLS supports the definition of
  relations between classes.
• Thus, XMLS and DTD can be used directly to embed
  semantic information.
• However, since XML actually only renders
  syntactic support for knowledge representation,
  XML-based ontology description languages face the
  following problems when representing knowledge

13
IntroductionXML-based Ontology Description
Languages

• A mechanism to define some relationships that are
  usually central in ontologies such as is-a or
  element-of relationships is lacking in XML.
• XML does not support any notion of inheritance,
  which is an important attribute in ontologies.
• In XML, concepts are defined through tags, which
  can be either a string or a combination of other
  nested tags. Such mechanism may not be sufficient
  for defining concepts in ontology, which may
  require richer data structures to be represented.
• In XML, the order of tags appearing in a document
  must be previously defined. In contrast, the
  ordering of attribute description does not matter
  on ontology.

14
IntroductionRDF-based Ontology Description
Languages

• RDF extends XML to become a standard for
  knowledge representation.
• In addition, RDF Schema (RDFS) can be used to
  define classes and class hierarchies in a domain.
  
• The standardization supported by RDF provides two
  important contributions
• A standard set of modeling primitives (e.g.
  class, instance, etc.) and their relationships
  (e.g. subclass) are provided.
• A standardized syntax for writing ontologies is
  supported.
• Popular RDF-based ontology description languages
  include DARPA Agent Markup Language (DAML),
  Ontology Inference Language (OIL), DAMLOIL and
  Web Ontology Language (OWL)

15
Introduction DARPA Agent Markup Language

• DAML or DAML-ONT extends RDFS to represent
  ontology using the object-oriented approach.
• It embeds some object-oriented concepts to
  represent classes. Thus, the class representation
  of DMAL-ONT is better than RDF.
• Example of DAML-ONT to represent the class
  "Journal", which is a subclass of the class
  "Publication Medium", but is disjoint with
  classes "Conference" and "Workshop" (i.e. an
  object which belongs to class "Journal" can not
  belong to classes "Conference" or "Workshop"
• ltClass ID"Journal"gt
• ltsubClassOf resource"Publication Medium" gt
• ltdisjointFrom resource"Conference" gt
• ltdisjointFrom resource"Workshop" gt
• lt Classgt

16
IntroductionOntology Inference Language

• OIL extends RDFS to represent ontology.
• It is designed based on three criteria
• Frame-based. It supports frames to define classes
  and properties of classes. Thus, class contents
  can be described more informatively (e.g.
  constraints can be used for class properties)
• Description Logic. It describes knowledge using
  logic rules. Thus, knowledge is represented
  mathematically and can be processed by programs.
• Uses Web Standard. It is based on XML and RDFS.

17
IntroductionOntology Inference Language

• ltrdfsClass rdfID"animal" gt
• ltrdfsClass rdfID"plant"gt
• ltrdfssubClassOfgt
• ltoilNOTgt
• ltoilhasOperand rdfresource"animal" gt
• ltoilNOT gt
• lt rdfssubClassOfgt
• lt rdfsClassgt
• ltrdfsClass rdfID"tree"gt
• ltrdfssubClassOf rdfresource"plant"gt
• lt rdfsClassgt
• Class "animal" is defined, followed by class
  "plant", which is defined with the operator "NOT"
  used to state that it is strictly not identical
  with class "animal (i.e. objects which belong to
  class "animal" can not belong to class "plant"
  and vice-versa).
• Finally, class "tree" is defined as a subclass of
  "plant".

18
IntroductionDAML vs. OIL

• Compared with DAML, OIL can represent class
  properties better, but DAML can represent class
  relationships more clearly.
• Hence, they can be combined to form a better
  ontology description language
• DAML OIL
• It defines class relationships based on DAML.
• Class properties are defined in a similar way as
  OIL.
• Hence, DAMLOIL takes the advantages of both DAML
  and OIL.

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IntroductionWeb Ontology Language

• OWL is extended from DAMLOIL to allow users to
  define various types of relationships between
  classes.
• Properties can also be defined using additional
  constructs in OWL.
• OWL has three sublanguages
• OWL Lite
• OWL DL
• OWL Full.

20
IntroductionWeb Ontology Language

• Even though there is the same OWL syntax used
  among these sublanguages, they have a little
  difference in design aimed at various communities
  of implementers and users
• OWL Lite only primarily supports classification
  hierarchy and simple constrains when designing
  classes.
• OWL DL includes all OWL language constructs but
  they can be used only under certain restriction
  (e.g. a class cannot be an instance of another
  class).
• OWL Full allows all OWL language constructs to be
  used without any restriction.

21
IntroductionWeb Ontology Language

• ltrdfRDFgt
• xmlnsowl "http//www.w3.org/2002/07/owl"
• xmlnsrdf "http//www.w3.org/1999/02/22-rdf-synt
  ax-nsl"
• xmlnsrdfs"http//www.w3.org/2000/01/rdf-schema
  "
• xmlnsxsd "http//www.w3.org/2000/10/XMLSchema"
  
• xmlnsdaml"http//www.w3.org/2001/10/damloil"
• ltowlOntology rdfabout"Scholarly Information"gt
• ltowlversionInfogtv 1.0 2009-12-07
  190640lt/owlversionInfogt
• lt owlOntologygt
• ltowlClass rdfID"Concept1"gt
• ltowlrdfLabel"Data Mining"gt
• lt owlClassgt
• ltowlClass rdfID"Concept2"gt
• ltowlrdfLabel"Fuzzy Logic"gt
• lt owlClassgt
• lt owlClass rdfID"Concept2"gt
• lt owlClass rdfID"Concept3"gt
• ltowlrdfLabel"Data Mining, Fuzzy Logic" gt
• ltrdfsubClassOfgt

Header Info
Ontology Name and Version
3 classes Concept1 (labelled Data mining),
Concept2 (labelled Fuzzy Logic) and Concept3.
Concept3 is a subclass of both Concept 1 and
Concept2.
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2. Related WorkOntology Generation

• Ontology uses classes, which contain attributes,
  to represent concepts.
• Ontology also supports taxonomy and non-taxonomy
  relations between classes.
• Although editing tools such as Protege 1 and
  OilEd 2 have been developed to help users to
  create and edit ontology, it is a tedious task to
  manually derive ontology from data.

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2. Related WorkOntology Generation Approaches

• Ontology can be generated from various types of
  data, mostly textual.
• Large corpora 3,4 are considered as good
  sources for mining knowledge for constructing
  ontology, since the information in the corpus is
  usually well annotated. Therefore, it can be
  easily processed by other programs.
• Ontology can also be generated from a knowledge
  base of rules 5, which is represented as a tree
  with rules residing at tree nodes. Statistical
  approaches have been used to estimate the
  existence of relationships between entities
  involved in rules 6.

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2. Related WorkOntology Generation Approaches

• When knowledge is represented in semi-structured
  schemata such as XML and RDF, its contents can
  easily be parsed by programs techniques have
  been proposed to generate ontology from
  semi-structured schemata based on Graph Theory
  7 and statistical approaches 8.
• Learning Source Description (LSD) proposed 9 to
  generate ontology from any arbitrary formalisms
  of semi-structured schemata.
• Entity-Relationship model used in database schema
  has also been adopted as an information source
  for generating ontology 10,11.

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2. Related WorkOntology Generation Textual Data

• For textual data, ontology concepts can be
  extracted efficiently using Natural Language
  Processing (NLP) techniques 12,13.
• NLP for preprocessing the textual data in order
  to extract significant keywords.
• WordNet 14 can be used to improve accuracy of
  ontology generated by NLP-based techniques.
• However, the NLP techniques have difficulty in
  finding semantic relationships among the
  keywords.
• Data mining techniques can be combined with NLP
  to improve the efficiency of ontology generation.
  In Text-to-Onto 15, association rules are used
  to find associative relations between keywords,
  which are used to construct non-taxonomy
  relations for the ontology.

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2. Related WorkOntology Generation Textual Data

• Keywords' frequencies are often used in
  statistical approaches 16,17 to identify
  significant keywords that can be used to
  represent a certain concept.
• Clustering techniques have also been applied to
  generate ontology from textual data 18.
• Using significant keywords extracted from textual
  data, clustering techniques can cluster documents
  and interpret topics from the generated clusters.

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2. Related WorkOntology Generation Clustering

• Clustering can be used to mine hidden knowledge
  from data to construct an ontology. It can also
  be used to enrich existing ontology.
• Traditional clustering techniques are useful for
  generating non-taxonomy relations for ontology.
• In particular, conceptual clustering techniques
  are powerful clustering techniques that can
  conceptualize clusters and construct a concept
  hierarchy of clusters useful for generating
  taxonomy relations for ontology.
• E.g. approach based on COBWEB 18 that can
  generate taxonomy relations among concepts on a
  domain for ontology generation.
• Mo'K 19 is a system that can obtain taxonomy
  relations from tagged text using conceptual
  clustering.

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2. Related WorkOntology Applications Scholarly
Info

• In E-Scholar Knowledge Inference MOdel (ESKIMO)
  20, knowledge on scholarly publications is
  represented as a simple ontology, known as
  OntoPortal, which is manually developed and
  maintained.
• OntoPortal describes and provides links to other
  external research pages on the Web. Hypertext
  links between the web pages are also described in
  the OntoPortal ontology.
• ESKIMO allows users to retrieve scholarly
  information from the constructed ontology by
  using queries represented as Prolog-like rules.

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2. Related WorkOntology Applications Scholarly
Info

• In the Scholarly Ontology Project 21, a digital
  library Web server is constructed using Semantic
  Web technologies in order to support scholarly
  retrieval.
• Developed using a collaborative approach in which
  researchers will submit their documents in a
  specifically structured format.
• As such, the contents of the submitted documents
  can be further processed in the system and
  converted into scholarly ontology accordingly.

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2. Related WorkOntology Applications Scholarly
Info

• In the Research in Semantic Scholarly Publishing
  (RSSP) project, scientific publications are
  collected from online archives such as the Open
  Archive Initiative (OAI) 22.
• Information of the documents (e.g. their authors,
  titles, citations, publishers, etc.) is
  extracted, indexed and converted into ontology
  formalism.
• DAMLOIL is used to annotate the ontology as
  Semantic Web pages to support scholarly retrieval

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2. Related WorkSummary

• Many techniques to construct ontology from
  various data types/sources mainly textual data
• Traditionally, NLP techniques are used to analyze
  textual data.
• Recently, data mining techniques have been
  incorporated into NLP to further discover hidden
  knowledge from textual data.
• Conceptual clustering is an advanced data mining
  technique that can organize data in a
  hierarchical conceptual structure.
• Thus, conceptual clustering is a useful technique
  to discover knowledge for generating ontology
  from textual data.

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3. Toward Automated Ontology GenerationBasics

• Initial focus on Scholarly info
• Scholarly ontology generated directly from
  explicit information on scientific publications
  (e.g. their titles, authors, citations, etc.).
• Other advanced scholarly knowledge such as
  research experts and areas are usually inferred
  manually by human experts.

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3. Toward Automated Ontology GenerationBasics

• To construct scholarly ontology from citation
  database, we use data mining techniques to
  discover hidden knowledge in the database.
• Data mining techniques include Context-based
  Cluster Analysis (CCA) and Fuzzy Concept
  Hierarchy Generation (FCHG)
• Discovered knowledge then converted and
  integrated into the ontology formalism.
• As such, apart from the implicit information
  available on scientific publications, Scholarly
  Ontology can also support other useful scholarly
  retrieval functions such as research experts
  finding and trends detection

34
3. Toward Automated Ontology GenerationContext-ba
sed Cluster Analysis

• CCA is based on Formal Concept Analysis (FCA)
  23 technique.
• FCA provides a formal model, known as formal
  context, to represent relations between objects
  and attributes in a data set.
• We use formal contexts to represent multiple
  resultant clustering data.
• Then, relations between the formal contexts are
  analyzed to find the relations between the
  corresponding resultant clustering data

35
3. Toward Automated Ontology GenerationFuzzy
Concept Hierarchy Generation

• Concept hierarchy is a data structure useful for
  knowledge presentation.
• Widely used in data mining applications.
• Size of a concept hierarchy may be large to
  reflect the knowledge in a domain precisely.
• Manual construction may be difficult and tedious.
  
• Need conceptual clustering

36
3. Toward Automated Ontology GenerationFuzzy
Concept Hierarchy Generation

• Many conceptual clustering techniques organize
  knowledge as a concept hierarchy. It may not be
  sufficient for representing information in a real
  domain.
• FCA, which is a data exploratory technique,
  supports concept lattice that provides a more
  informative conceptual model for representing
  knowledge.
• FCA-based conceptual clustering techniques are
  potentially useful for constructing taxonomy
  knowledge of ontology.
• However, the typical FCA-based conceptual
  clustering techniques do not support uncertainty
  information.

37
3. Toward Automated Ontology GenerationFuzzy
Concept Hierarchy Generation

• Traditional FCA-based conceptual clustering
  approaches cant represent vague information
  Need fuzziness
• L-Fuzzy context uses linguistic variables to
  represent uncertainty in the context.
• But needs human interpretation to define
  linguistic variables.
• Fuzzy concept lattice generated from L-fuzzy
  context usually causes a combinatorial explosion
  of concepts (compared to traditional concept
  lattice)

38
3. Toward Automated Ontology GenerationFuzzy
Concept Hierarchy Generation

• We combine fuzzy logic and FCA as Fuzzy Formal
  Concept Analysis (FFCA).
• In FFCA, uncertainty information is directly
  represented by a real number of membership value
  in the range of 0,1.
• Linguistic variables are no longer needed.
• Compared to fuzzy concept lattice generated from
  L-fuzzy context, the fuzzy concept lattice
  generated using FFCA will be simpler in terms of
  the number of formal concepts.
• It also supports a formal mechanism for
  calculating concept similarities.
• Based on FFCA, we propose the Fuzzy Conceptual
  Clustering technique in FCHG to generate fuzzy
  concept hierarchy.

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4. Fuzzy Ontology Generation FrameworkFuzzy
Ontology

• Application of fuzzy logic offers a possible
  solution for dealing with uncertainty information
• Fuzzy ontology is generated and used in text
  retrieval and search engines, where membership
  values are used to evaluate the similarities
  between the concepts in a concept hierarchy
• Manual generation of fuzzy ontology from a
  predefined concept hierarchy is a difficult and
  tedious task that often requires expert
  interpretation.

40
4. Fuzzy Ontology Generation FrameworkIntroductio
n

• Efficient method for generation of concept
  hierarchy and fuzzy ontology is highly desirable
• We propose a Fuzzy Ontology Generation Framework
  (FOGF) that can automate fuzzy ontology
  generation from uncertainty data based on Formal
  Concept Analysis (FCA) theory
• Generated fuzzy ontology is mapped to a semantic
  representation in OWL

41
4. Fuzzy Ontology Generation FrameworkOverview

• Fuzzy Formal Concept Analysis incorporates fuzzy
  logic into Formal Concept Analysis to represent
  vague information
• Concept Hierarchy Generation clusters the fuzzy
  concept lattice generated by FFCA to construct a
  concept hierarchy in two steps Fuzzy Conceptual
  Clustering and Hierarchical Relation Generation
• Fuzzy Ontology Generation constructs fuzzy
  ontology from a fuzzy context using the concept
  hierarchy created by fuzzy conceptual clustering
• Semantic Representation Conversion make
  knowledge accessible and sharable on the Web
  environment. Use OWL

42
4. Fuzzy Ontology Generation Framework Step 1
Fuzzy Formal Concept Analysis

• Definition (Fuzzy Formal Context)
• A fuzzy formal context is a triple
• K (G, M, I ?(G ? M))
• where G is a set of objects, M is a set of
  attributes, and I is a fuzzy set on domain G ? M.
  
• Each relation (g, m) ? I has a membership value
  ?(g,m) in 0,1.

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4. Fuzzy Ontology Generation Framework Step 1
Fuzzy Formal Concept Analysis

• Fuzzy formal context can be represented as a
  cross-table (Table 1)
• An a-cut can be set to eliminate relations with
  low membership values, e.g. a 0.5 (Table 2)
• The context has 3 objects representing 3
  documents, D1, D2 and D3. It also has 3
  attributes, Data Mining, Clustering and
  Fuzzy Logic representing 3 research topics. The
  relationship between an object and an attribute
  is represented by a membership value in 0, 1.

44
4. Fuzzy Ontology Generation Framework Step 1
Fuzzy Formal Concept Analysis

• Definition (Fuzzy Representation of Object)
• Each object O in a fuzzy formal context K can be
  represented by a fuzzy set ?(O) as where A1,
  A2,, Am is the set of attributes in K and µi is
  the membership of O with attribute Ai in K. ? (O)
  is called the fuzzy representation of O.

45
4. Fuzzy Ontology Generation Framework Step 1
Fuzzy Formal Concept Analysis

• Generally, we can consider the attributes of a
  formal concept as the description of the concept.
  
• Thus, the relationships between the object and
  the concept should be the intersection of the
  relationships between the objects and the
  attributes of the concept
• Since each relationship between the object and an
  attribute is represented as a membership value in
  fuzzy formal context, the intersection of these
  membership values should be the minimum of these
  membership values, hence

46
4. Fuzzy Ontology Generation Framework Step 1
Fuzzy Formal Concept Analysis

• Definition (Fuzzy Formal Concept)
• Given a fuzzy formal context K (G, M, I) and a
  confidence threshold T, we define A m ? M ?g
  ? A ?(g, m) ? T for A ? G and B g ? G ?m
  ? B ?(g,m) ? T for B ? M. A fuzzy formal
  concept (or fuzzy concept) of a fuzzy formal
  context (G, M, I) with a confidence threshold T
  is a pair (Af ?(A), B) where A ? G, B ? M, A
  B and B A. Each object g ? ?(A) has a
  membership ?g defined as
• ?g min (g,m)
• m ? B
• where ?(g,m) membership value between object g
  and attribute m defined in I. If B then ?g
  1 for every g. A and B are the extent and intent
  of the formal concept (?(A), B) respectively.

47
4. Fuzzy Ontology Generation Framework Step 1
Fuzzy Formal Concept Analysis

• This version of FFCA as presented in these
  Definitions preserves differently continuous
  values of objects memberships, crucial for
  calculating concepts similarities.
• In a formal context, a concept can have many
  superconcepts and subconcepts. However, the
  similarities of a concept to its superconcepts
  and subconcepts are different.
• With fuzzy concept lattice, we can make use of
  the fuzzy set theory to calculate the
  similarities between a concept and its
  subconcepts.

48
4. Fuzzy Ontology Generation Framework Step 1
Fuzzy Formal Concept Analysis

• Definition (Fuzzy Formal Concept Cardinality)
• Since the fuzziness of a fuzzy formal concept is
  represented by membership values of objects of
  the concept, the cardinality of a fuzzy formal
  concept Kf (?(A), B) is defined as Kf
  ?(A).

49
4. Fuzzy Ontology Generation Framework Step 1
Fuzzy Formal Concept Analysis

• Definition (Fuzzy Formal Concept Similarity)
• The similarity of a fuzzy formal concept Kf1
  (?(A1), B1) and its subconcept Kf2 (?(A2), B2)
  is defined as E(Kf1,Kf2) E(?(A1), ?(A2)).

50
4. Fuzzy Ontology Generation Framework Step 1
Fuzzy Formal Concept Analysis

• Fuzzy concept lattice generated from fuzzy formal
  context in Table 2 (similarities between concepts
  shown)

• Traditional concept lattice generated from Table
  1 without membership values

Fig. 3
Fig. 2
51
4. Fuzzy Ontology Generation Framework Overview
52
4. Fuzzy Ontology Generation Framework Step 2
Concept Hierarchy Generation

• Concept Hierarchy Generation clusters the fuzzy
  concept lattice generated by FFCA to construct a
  concept hierarchy in two steps Fuzzy Conceptual
  Clustering and Hierarchical Relation Generation

53
4. Fuzzy Ontology Generation Framework Step 2
a)Fuzzy Conceptual Clustering

• Compared to traditional clusters, the conceptual
  clusters generated have the following properties
• Each conceptual cluster is considered as a human
  interpretable concept in the domain of the fuzzy
  concept lattice
• Each conceptual cluster is a sublattice extracted
  from the fuzzy concept lattice
• A formal concept must belong to at least one
  conceptual cluster e.g. a scientific document can
  belong to more than one research area

54
4. Fuzzy Ontology Generation Framework Step 2
a)Fuzzy Conceptual Clustering

• Conceptual clusters are generated based on the
  idea at if a formal concept A belongs to a
  conceptual cluster R, then its subconcept B also
  belongs to R if B is similar to A. We can use a
  similarity confidence threshold Ts to determine
  whether two concepts are similar or not.

55
4. Fuzzy Ontology Generation Framework Step 2
a)Fuzzy Conceptual Clustering

• Definition (Conceptual Cluster).
• A conceptual cluster of a concept lattice K with
  a similarity confidence threshold Ts is a
  sublattice SK of K which has the following
  properties
• SK has a supremum concept CS that is not
  similar to any of its superconcepts.
• Any concept C ? CS in SK must have at least one
  superconcept C ? SK so that E(C,C) gt Ts.

56
4. Fuzzy Ontology Generation Framework Step 2
a)Fuzzy Conceptual Clustering

• Fig. 5 shows the conceptual clusters generated
  from the fuzzy concept lattice given in Fig. 3
  with similarity confidence threshold Ts 0.5

Fig. 5
57
4. Fuzzy Ontology Generation Framework Step 2
b)Hierarchical Relation Generation

• Fuzzy conceptual clustering generates a set of
  conceptual clusters SC. To construct a concept
  hierarchy from the conceptual clusters, we need
  to find the hierarchy relations from the
  clusters.
• We first define a concept hierarchy
• Definition (Concept Hierarchy)
• A concept hierarchy is a poset (partially ordered
  set) (H,?) where H is a finite set of concepts,
  and ? is a partial order on H.

58
4. Fuzzy Ontology Generation Framework Step 2
b)Hierarchical Relation Generation

• Definition of superconcept and subconcept
  relations on conceptual clusters assures that
  each conceptual cluster has at least one
  superconcept, unless it corresponds to the root
  node of the concept hierarchy generated. However,
  we must prove that the ? relation is a partial
  order.
• Definition (Subconcept and Superconcept on a
  Concept Hierarchy)
• Let C1 and C2 be two conceptual clusters
  corresponding to two sublattices L1 and L2 of a
  fuzzy concept lattice F (K). Let the fuzzy formal
  concept I be the supremum of L1, i.e. I
  sup(L1). C1 is the subconcept of C2, denoted as
  C1 ? C2 , if I is the subconcept of any concept
  C ? L2, or I ? C where ? is the partial order
  defined on F (K). Equivalently, C2 is the
  superconcept of C1.

59
4. Fuzzy Ontology Generation Framework Step 2
b)Hierarchical Relation Generation

• Figure 8(b) illustrates the hierarchical
  relations constructed from the conceptual
  clusters given in Figure 8(a). Each concept in
  the concept hierarchy is represented by a set of
  its attributes. The supremum and infimum of the
  lattice are considered as Thing and Nothing
  concepts, respectively.

60
4. Fuzzy Ontology Generation Framework Overview
61
4. Fuzzy Ontology Generation Framework Step 3
Fuzzy Ontology Generation

• This step constructs fuzzy ontology from a fuzzy
  context using the concept hierarchy created by
  fuzzy conceptual clustering.
• This is done based on the characteristic that
  both FCA and ontology support formal definitions
  of concepts.
• However, a concept defined in FCA has both
  extensional and intensional information in a
  balanced manner, whereas a concept in ontology
  emphasizes on its intensional aspect.
• To construct the fuzzy ontology, we need to
  convert both intensional and extensional
  information of FCA concepts into the
  corresponding classes and relations of the
  ontology.
• Thus, we define the fuzzy ontology as follows

62
4. Fuzzy Ontology Generation Framework Step 3
Fuzzy Ontology Generation

• Definition (Fuzzy Ontology).
• A fuzzy ontology FO consists of 4 elements
  (C,AC,R, X), where C set of concepts AC
  represents a collection of attributes sets, one
  for each concept R (RT, RN) represents a set
  of relationships, which consists of 2 elements
  RN is a set of non-taxonomy relationships and RT
  is a set of taxonomy relationships. Each concept
  ci in C represents a set of objects, or
  instances, of the same kind. Each object oij of a
  concept ci can be described by a set of
  attributes values denoted by AC(ci). Each
  relationship ri(cp,cq) in R represents a binary
  association between concepts cp and cq, and the
  instances of such a relationship are pairs of
  (cp,cq) concept objects. Each attribute value of
  an object or relationship instance is associated
  with a fuzzy membership value between 0,1
  implying the uncertainty degree of this attribute
  value or relationship. X is a set of axioms. Each
  axiom in X is a constraint on the concepts and
  relationships attribute values or a constraint
  on the relationships between concept objects

63
4. Fuzzy Ontology Generation Framework Step 3
Fuzzy Ontology Generation

• Example (Fuzzy Ontology).
• the Scholarly Ontology OS (C, AC, R, X) is a
  fuzzy ontology where its components are as
  follows.
• C Document, Research Area
• AC(Document) Name ,Author, Title,
  Keywords, Abstract, Body, Publisher,
  Publication Date
• AC(Research Area) Name,Keyword
• RN belong-to(Document, Research Area),
  consist-of(Research Area,Document)
• RT superarea-of(Research Area, Research
  Area), subarea-of(Research Area, Research
  Area)
• X Implies(Antecedent(consist-of(I-variable(x1)
  I-variable(x2)))
• Consequent(belong-to(I-variable(x2)
  I-variable(x1))))
• Implies(Antecedent(belong-to(I-variable(x1)
  I-variable(x2)))
• Consequent(consist-of(I-variable(x2)
  I-variable(x1))))
• Implies(Antecedent(superarea(I-variable(x1)
  I-variable(x2)))
• Consequent(subarea(I-variable(x2)
  I-variable(x1))))
• Implies(Antecedent(subarea(I-variable(x1)
  I-variable(x2)))
• Consequent(superarea(I-variable(x2)
  I-variable(x1))))

64
4. Fuzzy Ontology Generation Framework Step 3
Fuzzy Ontology Generation
Figure 9. Fuzzy ontology generation process.
65
4. Fuzzy Ontology Generation Framework Step 3
Fuzzy Ontology Generation

• Class Mapping furnishes C E, I in which E and
  I are classes corresponding to extent and intent
  of the fuzzy context. For example, the extent
  class mapped from the extent of the fuzzy context
  given in Table 1(b) can be labeled manually as
  Document. We can use appropriate names to
  represent keyword attributes and use them to
  label the intent class names as well. For
  example, the class Research Area can be used to
  label the initial intent class.

66
4. Fuzzy Ontology Generation Framework Step 3
Fuzzy Ontology Generation

• Taxonomy Relation Generation furnishes RT
  superclass(I,I), subclass(I,I). Thus, the
  hierarchical relations between instances of
  intent classes are defined. Also, two rules are
  added to X accordingly
• superclass(X,Y)-subclass(Y,X).
• subclass(X,Y)-superclass(Y,X).

67
4. Fuzzy Ontology Generation Framework Step 3
Fuzzy Ontology Generation

• Non-taxonomy Relation Generation furnishes RN
  RIE(I,E), REI(E,I), in which REI is the
  relation between the extent class and intent
  class. RIE is the reversed relation of REI.
  However, we still need to label the non-taxonomy
  relation. For example, the relation between class
  Document and class Research Area can be labeled
  as belong-to, which implies that a document can
  belong to one or more research areas. Also, two
  rules are added to X accordingly
• REI(X,Y)- RIE(Y,X).
• RIE (X,Y)- REI (Y,X).

68
4. Fuzzy Ontology Generation Framework Step 3
Fuzzy Ontology Generation

• Instances Generation generates instances set I
  II, IE where II and IE are instances of the
  intent and extent class.
• Then, it furnishes membership values for the
  instances attributes and relationships

69
4. Fuzzy Ontology Generation Framework Overview
70
4. Fuzzy Ontology Generation Framework Step 4
Semantic Representation Conversion

• The generated fuzzy ontology provides a
  conceptual model of knowledge in the
  corresponding domain
• However, to make such knowledge accessible and
  sharable, we must convert it into a semantic
  representation that can be embedded into the
  contents of Web pages.
• In Semantic Web, ontology description language
  such as OWL can be used to annotate ontology.
• Therefore, the generated fuzzy ontology can be
  automatically converted into the corresponding
  semantic representation in OWL, in which each
  class and instance is annotated as shown on the
  next slide

71
4. Fuzzy Ontology Generation Framework Step 4
Semantic Representation Conversion

• Ontology for the concept hierarchy represented by
  OWL
• lt?xml version"1.0"?gtltrdfRDF
  xmlnsrdf"http//www.w3.org/1999/02/22-rdf-syntax
  -ns" xmlnsxsd"http//www.w3.org/2001/XMLSche
  ma" xmlnsrdfs"http//www.w3.org/2000/01/rdf-
  schema" xmlnsowl"http//www.w3.org/2002/07/o
  wl" xmlns"http//www.owl-ontologies.com/unnam
  ed.owl" xmlbase"http//www.owl-ontologies.com/
  unnamed.owl"gt ltowlOntology rdfabout""/gt
  ltowlClass rdfID"Concept_2"/gt ltowlClass
  rdfID"Concept_1"/gt ltowlClass
  rdfID"Concept_3"gt ltrdfssubClassOf
  rdfresource"Concept_1"/gt ltrdfssubClassOf
  rdfresource"Concept_2"/gt lt/owlClassgt
  ltowlDatatypeProperty rdfID"Data_Mining"/gt
  ltowlDatatypeProperty rdfID"DataMining"gt
  ltrdfsdomain rdfresource"Concept_1"/gt
  ltrdfsrange rdfresource"http//www.w3.org/2001/X
  MLSchemafloat"/gt lt/owlDatatypePropertygt
  ltowlDatatypeProperty rdfID"FuzzyLogic"gt
  ltrdfsrange rdfresource"http//www.w3.org/2001/X
  MLSchemafloat"/gt ltrdfsdomain
  rdfresource"Concept_2"/gt lt/owlDatatypePropert
  ygt ltConcept_2 rdfID"Document2"gt ltFuzzyLogic
  rdfdatatype"http//www.w3.org/2001/XMLSchemaflo
  at" gt0.87lt/FuzzyLogicgt lt/Concept_2gt
  lt/rdfRDFgt

72
5. Scholarly OntologyOntology Generation

• Collected scientific documents on the research
  area Information Retrieval published in
  1987-1997 from ISI
• Downloaded documents are preprocessed to extract
  related information such as the title, authors,
  citation keywords, and other citation information
  
• Extracted information then stored in a citation
  database

73
5. Scholarly OntologyOntology Generation

• First, we construct a fuzzy formal context Kf
  G,M,I, with G as the set of documents and M as
  the set of citation keywords. The membership
  value of a document D on a citation keyword CK in
  Kf is computed as
• where n1 is the number of documents that cite D
  and contain CK, and n2 is the number of documents
  that cite D
• This formula is based on the premise that the
  more frequent a keyword occurs in the citing
  paper, the more important the keyword is in the
  cited paper.

74
5. Scholarly OntologyOntology Generation

• Then, conceptual clustering is performed from the
  fuzzy formal context
• Each generated conceptual cluster represents a
  research area
• The generated conceptual clusters form a
  hierarchy of research areas of documents in the
  Citation Database, or Research Area Hierarchy

75
5. Scholarly Ontology Example of concept
hierarchy generated
Figure 11

• Each research area is represented by a set of
  most frequent keywords occurring in the documents
  that belong to that research area. In FFCA,
  sub-areas inherit keywords from their
  super-areas. Note that the inherited keywords are
  not shown in Figure 11 when labeling the
  concepts. Only keywords specific to the concepts
  are used for labeling.

76
5. Scholarly Ontology Ontology Generation

• The generated ontology contains scholarly
  information as a hierarchy of research areas as
  well as research areas for each document.
• Taking advantages of the Semantic Web, such
  knowledge can be easily shared and reused by
  other systems for browsing or retrieval.
• For example, we can use Protégé-2000 for browsing
  the scholarly ontology.

77
5. Scholarly Ontology Part of the generated
concept hierarchy of research areas
Fig. 12
We use the keyword that has the highest
membership value to label the research area.
Nevertheless, users can browse more information
of each research area.
78
5. Scholarly Ontology Performance Evaluation

• Performance of the ontology generation is
  evaluated based on the generated Research Area
  Hierarchy.
• Firstly, we measure the typical recall, precision
  and F-measure to evaluate the clustering results.
  
• Secondly, we use the relaxation error and the
  corresponding cluster goodness measure to
  evaluate the goodness of the conceptual clusters
  generated. We also show whether the use of fuzzy
  membership instead of crisp value can help
  improve cluster goodness.
• Finally, we use the Average Uninterpolated
  Precision (AUP), which is a typical measure for
  evaluating a hierarchical construct, to evaluate
  the goodness of the generated concept hierarchy.

79
5. Scholarly Ontology Performance Evaluation

• Keyword attributes are descriptors for the
  generated clusters, if more keywords are
  extracted and used, the more meaningful the
  cluster descriptors are constructed?
• To verify this, we vary the number of keywords N
  extracted from documents from 2 to 10, and the
  similarity threshold Ts from 0.2 to 0.9 when
  performing conceptual clustering
• We have classified the documents downloaded from
  ISI into classes based on their research themes.
  These classes are used as a benchmark to evaluate
  the clustering results in terms of recall,
  precision and F-measure.

80
5. Scholarly Ontology Performance Evaluation -
Precision
Precision implies accuracy of the clustering
results. Table 6 shows that when N is small, the
precision is poor. It implies that noisy data
in clusters.
Table 6. Performance results using precision
measurement.
The precision is improved when the number of
extracted keywords is increased. However, this
will also cause the recall to decrease as shown
in Table 7.
81
5. Scholarly Ontology Performance Evaluation -
Recall
When the number of clusters is gradually
increased, the efficiency of the clustering
results will gradually be decreased.
Table 7. Performance results using recall
measurement.
82
5. Scholarly Ontology Performance Evaluation -
F-measure
When N is low, the F-measure is quite poor.
Nevertheless, the F-measure is stable and good
when a sufficient number of keywords are
extracted. The results also show that the
F-measure tends to have the best performance
when Ts 0.5.
Table 8. Performance results using F-measure
measurement.
83
5. Scholarly Ontology Performance Evaluation
Relaxation Error

• Relaxation error implies dissimilarities of items
  in a cluster based on attributes values.
• Since conceptual clustering techniques typically
  use a set of attributes for concept generation,
  relaxation error is quite commonly used for
  evaluating the goodness of conceptual clusters.

84
5. Scholarly Ontology Performance Evaluation
Relaxation Error

• The relaxation error RE of a cluster C is defined
  as
• where A is the set of the attributes of items in
  C, P(xi) is the probability of item xi occurring
  in C and da(xi,xj) is the distance of xi and xj
  on attribute a.
• The cluster goodness G of cluster C is defined as
  G(C) 1 - RE(C).

85
5. Scholarly Ontology Performance Evaluation
Relaxation Error

• Comparison of FFCA and COBWEB while the number of
  extracted keywords is varied from 2 to 10

we vary the number of keywords extracted to
observe the effect of the keyword generated on
cluster goodness. Besides, since COBWEB is
considered as one of the most popular techniques
for conceptual clustering, we also apply COBWEB
to the citation database to compare the
performance. It shows that FFCA achieves better
cluster goodness than COBWEB
86
5. Scholarly Ontology Performance Evaluation
AUP

• Average Uninterpolated Precision (AUP) is defined
  as the sum of the precision value at each point
  (or node) in a hierarchical structure where a
  relevant item appears, divided by the total
  number of relevant items
• Typically, AUP implies the goodness of a concept
  hierarchical structure.
• For evaluating AUP, we have manually classified
  the downloaded documents into classes based on
  their research themes.
• For each class, we extract 5 most frequent
  keywords from the documents in the class. Then,
  we use these keywords as inputs to form retrieval
  queries and evaluate the retrieval performance
  using AUP

87
5. Scholarly Ontology Performance Evaluation
AUP

• There are two ways to generate document keywords.
  The first is to use the set of keywords, known as
  attribute keywords, from each conceptual cluster
  as the document keywords. The second is to use
  the keywords from each document as the document
  keywords. Then, we vectorize the document
  keywords and the input query, and calculate the
  vectors distance for measuring the retrieval
  performance.

88
5. Scholarly Ontology Performance Evaluation
AUP

• Two methods
• AUP measured using attribute keywords
  Hierarchical Average Uninterpolated Precision
  (AUP(H)), as each concept inherits attribute
  keywords from its superconcepts.
• AUP measured using keywords from documents
  Unconnected Average Uninterpolated Precision
  (AUP(U)).

89
5. Scholarly Ontology Performance Evaluation
AUP

• Fig. 14 shows the results for AUP(H) and AUP(U)
  using different numbers of extracted keywords N.

It shows that when N gets larger, the
performance on AUP(H) and AUP(U) gets better. In
addition, performance on AUP(H) is generally
better than AUP(U). It means that the attribute
keywords generated for conceptual clusters are
appropriate
Fig. 14
90
6. Semantic Helpdesk Application Introduction

• Developed in collaboration with a multinational
  company, the Semantic Help-Desk Environment
  comprises the Web Service Requester, Matchmaking
  Agent and Web Service Provider.
• The focus is on the fuzzy ontology generation
  process that generates Machine Service Ontology
  from a customer service database.
• This approach enables individual machine service
  knowledge to be shared over the Semantic Web.
  Thus, machine service knowledge from different
  machines or models provided by different
  manufacturers can be shared and integrated. This
  is important as many customers may have different
  types of machines and models from different
  manufacturers.

91
6. Semantic Helpdesk Application Introduction -
Web Service Requester

• A kind of Web Service that enables access to
  customer support for machine services.
• Instances of the Web Service Requester can be
  created from a Web Requester Server where its
  address is accessible for all users through the
  Web.
• When encountering a problem, a user can use the
  Web to connect the Web Requester Server in order
  to create an instance of the Web Service
  Requester.
• The created instance runs as a web-based program.
  That is, it can use the Web to interact with the
  user and other programs.

92
6. Semantic Helpdesk Application Introduction -
Web Service Requester

• Through the Web, the Web Service Requester
  instance provides an interface for the user to
  enter their reported problem.
• Through the interface, the user can specify the
  encountered fault as a textual string. The user
  is also required to enter the code of the machine
  model. The given information is used to form a
  profile for the Web Service Requester.
• The profile is then sent as a request to the
  Matchmaking Agent to seek a potential Web Service
  Provider for solving the problem

93
6. Semantic Helpdesk Application Introduction -
Web Service Provider

• It offers its machine service support as a Web
  Service extended with ontology capabilities.
• There are probably many instances of a Web
  Service Provider existing concurrently on the
  Internet.
• An instance of the Web Service Provider can be
  considered as a program that can access the
  Machine Service Ontology to retrieve machine
  service knowledge for a given reported problem.
• An instance of the Web Service Provider can
  interact with other programs. That is, it can be
  called by other programs and return the outputs
  to the calling programs.
• Instances of the Web Service Provider must be
  registered with a specific agent known as the
  Matchmaking Agent that serves as a registry and
  look-up service.

94
6. Semantic Helpdesk Application Introduction -
Web Service Provider

• Each instance of the Web Service Provider also
  provides a profile file that describes its
  parameters and capabilities. XML is used in most
  Web Services to represent the information
  contained in the profiles.
• However, traditional XML lacks the capabilities
  of representing semantic information.
• To overcome this problem, the Web Service
  Provider uses ontology-based service description
  language OWL-S (formerly DAML-S) to describe
  information in its profile. Hence, we describe
  the service as OWL ontology and its intentional
  information can be fully understood by other
  programs.

95
6. Semantic Helpdesk Application Introduction -
Matchmaking Agent

• When the Matchmaking Agent receives machine
  service requests from the Web Service Requester,
  it locates the appropriate Web Services that can
  fulfill the request

96
6. Semantic Helpdesk Application Overview

97
6. Semantic Helpdesk Application Customer
Service Database

• The customer service database contains 9000
  service records, each record consists of
  fault-condition and checkpoint information
• Fault-condition contains the service engineers
  description of the machine fault. Checkpoint
  information indicates the suggested actions to be
  carried out to repair the machine based on the
  occurred fault-condition given by the customer

98
6. Semantic Helpdesk Application Customer
Service Database
99
6. Semantic Helpdesk Application Machine
Service Ontology Generation

• Apply FOGF to obtain Fuzzy Fault Concept Lattice
  ? Fault Concept Hierarchy ? Machine Service
  Ontology

Part of the Fault Concept Hierarchy of the
machine model AV_2011
100
6. Semantic Helpdesk Application Machine
Service Ontology Generation

• The generation process creates classes, relations
  and instances for the service ontology.
• The machine fault service knowledge stored in the
  Customer Service Database is known as
  non-taxonomy knowledge, whereas the machine fault
  hierarchy knowledge from the Fault Concept
  Hierarchy is called taxonomy knowledge. These two
  types of knowledge are combined to form the
  Machine Service Ontology.

101
6. Semantic Helpdesk Application Machine
Service Ontology in OWL
102
6. Semantic Helpdesk Application Experiments

• Data stored in the database was divided into 10
  subsets. Each subset was sequentially used as a
  testing set while others were used for generating
  conceptual clustering.
• Keywords in fault conditions in each testing set
  were extracted and fuzzified as testing fuzzy
  queries.
• To verify whether fuzzy queries can improve the
  retrieval performance, the keywords extracted are
  also used for retrieving without membership as
  crisp queries for comparison.

103
6. Semantic Helpdesk Application Experiments

• Manually classified faults in each machine model
  into groups based on the machine components in
  which the fault occurred.
• Retrieval accuracy is evaluated based on the
  number of the retrieved faults that are in the
  same classified group with the query.

104
6. Semantic Helpdesk Application Performance
Measures

• Recall, Precision and F-measure

105
6. Semantic Helpdesk Application Retrieval
Performance
106
6. Semantic Helpdesk Application Performance
Comparison

• Retrieval accuracy compared with four other
  techniques
• Two variations of k-nearest neighbor (kNN)
  technique. The first variation (kNN1) is based on
  vectors normalized Euclidean distance to perform
  the retrieval. The second (kNN2) makes use of
  fuzzy-trigram technique to do so.
• Two kinds of artificial neural networks (ANN)
  the supervised learning vector quantization
  (LVQ3) neural network and the unsupervised
  Self-Organizing Maps (SOM).

107
6. Semantic Helpdesk Application Performance
Comparison
(Confidence Threshold 0.2)

• FFCA with fuzzy query outperformed kNN.
• LVQ3 performed marginally better, but requires
  prior expert knowledge
• for training, which would be a problem when
  dealing with large amounts
• of uncertainty information.
• The proposed technique can generate a concept
  hierarchy from the
• clusters, which is important information for
  generating a corresponding
• meaningful ontology.

108
7. Summary

• Proposed a framework for fuzzy ontology
  generation with uncertainty information
• FOGF consists of the following steps
• Fuzzy Formal Concept Analysis
• Fuzzy Conceptual Clustering
• Fuzzy Ontology Generation
• Semantic Representation Conversion

109
7. Summary

• FOGF can represent uncertainty information and
  construct a concept hierarchy from the
  uncertainty information
• Apart from constructing scholarly ontology from
  citation database, FOGF has also been used to
  generate Machine Service Ontology for Semantic
  Help-desk and Reuters News Topic Themes Ontology
• Also, the scholarly ontology has been partially
  used to construct a Scholarly Semantic Web, a
  Semantic Web-based information retrieval system
  to support scholarly activities in the Semantic
  Web environment

110
References (Not intended to be Exhaustive)

• Ontology Editors
• 1 http//protege.stanford.edu/
• 2 S. Bechhofer, I. Horrocks, P.
  Patel-Schneider, and S. Tessaris, "A proposal for
  a description logic interface," in Proceedings of
  the International Workshop on Description Logics,
  pp. 33-36, 1999.
• Large corpora
• 3 E. Morin, Automatic acquisition of semantic
  relations between terms from technical corpora,"
  in Proceedings of the Fifth International
  Congress on Terminology and Knowledge Engineering
  (TKE-99), (Vienna, Austria), 1999.
• 4 M. Hearst, Automatic acquisition of hyponyms
  from large text corpora," in Proceedings of the
  Fourteenth International Conference on
  Computational Linguistic, (France), 1992.
• Knowledge base of rules
• 5 P. Compton and A. Jansen, Knowledge
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• Statistical approaches
• 6 H. Suryanto and P. Compton, Discovery of
  ontologies from knowledge bases," in Proceedings
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  Victoria(, eds.), (Canada), pp. 171-178, 2001.
• Semi-structured schemata based on Graphs
• 7 A. Deitel, C. Faron, and R. Dieng, Learning
  ontologies from RDF annotations, in Proceedings
  of the IJCAI Workshop in Ontology Learning,
  (Seattle,USA), 2001.

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• Semi-structured schemata based on Statistics
• 8 C. Papatheodorou, A. Vassiliou, and B. Simon,
  Discovery of ontologies for learning resources
  using word-based clustering," in Proceedings of
  ED-MEDIA 2002, (Denver,USA), 2002.
• LSD
• 9 A. Doan, P. Domingos, and A. Levy, Learning
  source descriptions for data integration," in
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• Database schema
• 10 P. Johannesson, A method for transforming
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• 11 D. Rubin, M. Hewett, D. Oliver, T. Klein,
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  XML," in Proceedings of the Pacic Symposium on
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• NLP
• 12 D. Lonsdale, Y. Ding, D. Embley, and A.
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• Wordnet
• 14 http//wordnet.princeton.edu/wordnet/download
  /
• Text-to-Onto
• 15 A. Maedche and S. Staab, Ontology learning
  for the Semantic Web," IEEE Intelligent Systems,
  Special Issue on the Semantic Web, vol. 16, no.
  2, 2001.
• Keyword frequencies
• 16 A. Faatz and R. Steinmetz, Ontology
  enrichment with texts from the WWW, in In
  Proceedings of Semantic Web Mining 2nd Workshop
  at ECML/PKDD-2002, (Helsinki, Finland), 2002.
• 17 R. Navigli, P. Velardi, and A. Gangemi,
  Ontology learning and its application to
  automated terminology translation," IEEE
  Intelligent Systems, vol. 18, no. 1, 2003.
• Clustering / COBWEB
• 18 P. Clerkin, P. Cunningham, and C. Hayes,
  \Ontology discovery for the Semantic Web using
  hierarchical clustering," in Proceedings of
  Workshop at ECML/PKDD-2001, (Germany), 2001.
• Mo'K
• 19 G. Bisson and C. Nedellec, \Designing
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Slide 113