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Ontology Learning

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Title: Ontology Learning


1
Ontology Learning
  • ?pa??fa ??ss?
  • ??asta?? ?ate???a

2
Contents
  • Introduction Ontologies, Ontology learning
  • Technical description
  • Ontology learning in the Semantic Information
    description
  • Ontology Learning Process
  • Ontology Learning - Architecture
  • Ontology Learning data sources
  • Methods used in ontology learning
  • Tools of ontology learning
  • Uses of ontology learning

3
Ontologies
  • Provide a formal, explicit specification of a
    shared conceptualization of a domain that can be
    communicated between people and heterogeneous and
    widely spreads application systems.
  • They have been developed in Artificial
    Intelligent and Machine Learning to facilitate
    knowledge sharing and reuse.
  • Unlike knowledge bases ontologies have all in
    one
  • formal or machine readable representation
  • full and explicitly described vocabulary
  • full model of some domain
  • consensus knowledge common understanding of a
    domain
  • easy to share and reuse

4
Ontology learning - General
  • Machine learning of ontologies
  • Main task to automatically learn complicated
    domain ontologies
  • Explores techniques for applying knowledge
    discovery techniques to different data sources (
    html documents, dictionaries, free text, legacy
    ontologies etc.) in order to support the task of
    engineering and maintaining ontologies

5
  • Introduction Ontologies, Ontology learning
  • Technical description
  • Ontology learning in the Semantic Information
    descritpion
  • Ontology Learning Process
  • Ontology Learning - Architecture
  • Ontology Learning data sources
  • Methods used in ontology learning
  • Tools of ontology learning
  • Uses of ontology learning

6
Ontology learning Technical description
  • The manual building of ontologies is a tedious
    task, which can easily result in a knowledge
    acquisition bottleneck. In addition, human expert
    modeling by hand is biased, error prone and
    expensive
  • Fully automatic machine knowledge acquisition
    remains in the distant future
  • Most systems are semi-automatic and require human
    (expert) intervention and balanced cooperative
    modeling for constructing ontologies

7
  • Introduction Ontologies, Ontology learning
  • Technical description
  • Ontology learning in the Semantic Information
    descritpion
  • Ontology Learning Process
  • Ontology Learning - Architecture
  • Ontology Learning data sources
  • Methods used in ontology learning
  • Tools of ontology learning
  • Uses of ontology learning

8
Semantic Information Integration
9
Ontology Engineering
10
  • Introduction Ontologies, Ontology learning
  • Technical description
  • Ontology learning in the Semantic Information
    descritpion
  • Ontology Learning Process
  • Ontology Learning - Architecture
  • Ontology Learning data sources
  • Methods used in ontology learning
  • Tools of ontology learning
  • Uses of ontology learning

11
Ontology learning Process (1/2)
12
Ontology learning Process (2/2)
  • Stages analysis
  • Merging existing structures or defining mapping
    rules between these structures allows importing
    and reusing existing ontologies
  • Ontology extraction models major parts of the
    target ontology, with learning support fed from
    various input sources
  • The target ontologys rough outline, which
    results from import, reuse and extraction is
    pruned to better fit the ontology to its primary
    purpose
  • Ontology refinement profits from the pruned
    ontology but completes the ontology at a fine
    granularity (in contrast to extraction)
  • The target application serves as a measure for
    validating the resulting ontology
  • The ontology engineer can begin this cycle again-
    for example, to include new domains in the
    constructing ontology or to maintain and update
    its scope

13
  • Introduction Ontologies, Ontology learning
  • Technical description
  • Ontology learning in the Semantic Information
    descritpion
  • Ontology Learning Process
  • Ontology Learning - Architecture
  • Ontology Learning data sources
  • Methods used in ontology learning
  • Tools of ontology learning
  • Uses of ontology learning

14
Ontology learning Architecture (1/5)
15
Ontology learning Architecture (2/5)
  • Ontology Engineering Workbench A sophisticated
    means for manual modeling and refining of the
    final ontology. The ontology engineer can browse
    the resulting ontology from the ontology learning
    process and decide to follow, delete or modify
    the proposals as the task requires.

16
Ontology learning Architecture (3/5)
  • Management component The ontology engineer uses
    the management component to select input data
    that is relevant resources such as HTML and XML
    documents, DTDs, databases or existing ontologies
    that the discovery process can further exploit.
    Then, using the management component the engineer
    chooses of a set of resource-processing methods
    available in the resource-processing component
    and from a set of algorithms available in the
    algorithm library.

17
Ontology learning Architecture (4/5)
  • Resource processing Component Depending on the
    available data the engineer can choose various
    strategies for resource processing
  • Index and reduce HTML documents to free text
  • Transform semi-structured documents such as
    dictionaries into predefined relational structure
  • Handle semi-structured and structured schema data
    by following different strategies for import
  • Process free natural text
  • After first preprocessing data according to one
    of
  • these or similar strategies the resource
    processing
  • module transforms the data into an algorithm
    specific
  • relational representation.

18
Ontology learning Architecture (5/5)
  • Algorithm library A collection of various
    algorithms that work on the ontology definition
    and the preprocess input data. Although specific
    algorithms can vary greatly from one type of
    input to the next, a considerable overlap exists
    for underlying learning approaches such as
    associations rules, formal concept analysis or
    clustering.

19
Contents
  • Introduction Ontologies, Ontology learning
  • Technical description
  • Ontology learning in the Semantic Information
    descritpion
  • Ontology Learning Process
  • Ontology Learning - Architecture
  • Ontology Learning data sources
  • Methods used in ontology learning
  • Tools of ontology learning
  • Uses of ontology learning

20
Ontology Learning from Natural Language
  • Natural language texts exhibit morphological,
    syntactic, semantic, pragmatic and conceptual
    constraints that interact in order to convey a
    particular meaning to the reader. Thus, the text
    transports information to the reader and the
    reader embeds this information into his
    background knowledge
  • Through the understanding of the text, data is
    associated with conceptual structures and new
    conceptual structures are learned from the
    interacting constraints given through language
  • Tools that learn ontologies from natural language
    exploit the interacting constraints on the
    various language levels (from morphology to
    pragmatics and background knowledge) in order to
    discover new concepts and stipulate relationships
    between concepts

21
Ontology Learning from Semi-structured Data
  • HTML data, XML data, XML DTDs, XML-Schemata and
    their likes add - more or less expressive -
    semantic information to documents
  • A number of approaches understand ontologies as a
    common generalizing level that may communicate
    between the various data types and data
    descriptions. Ontologies play a major role for
    allowing semantic access to these vast resources
    of semi-structured data
  • Learning of ontologies from these data and data
    descriptions may considerably enforce the
    application of ontologies and, thus, facilitate
    the access to these data

22
Ontology Learning from Structured Data
  • The learning of ontologies from metadata, such as
    database schemata, in order to derive a common
    high-level abstraction of underlying data
    descriptions can be an important precondition for
    data warehousing or intelligent information
    agents

23
  • Introduction Ontologies, Ontology learning
  • Technical description
  • Ontology learning in the Semantic Information
    descritpion
  • Ontology Learning Process
  • Ontology Learning - Architecture
  • Ontology Learning data sources
  • Methods used in ontology learning
  • Tools of ontology learning
  • Uses of ontology learning

24
Methods for learning ontologies (1/8)
  • Clustering
  • The elaboration of any clustering method involves
    the definition of two main elements- a distance
    metrics and a classification algorithm
  • A workbench that supports the development of
    conceptual clustering methods for the (semi-)
    automatic construction of ontologies of a
    conceptual hierarchy type from parsed corpora is
    the MoK workbench

25
Methods for learning ontologies (2/8)
  • Clustering
  • Ontologies are organized as multiple hierarchies
    that form an acyclic graph where nodes are term
    categories described by intention and links
    represent inclusion.
  • Learning though hierarchical classification of a
    set of objects can be performed in two main ways
    top down, by incremental specialization of
    classes and bottom-up by incremental
    generalization

26
Methods for learning ontologies (3/8)
  • Information Extraction Rules

27
Methods for learning ontologies (4/8)
  • Information Extraction Rules
  • We start with
  • An initial hand crafted seed ontology of
    reasonable quality which contains already the
    relevant types of relationships between ontology
    concepts in the given domain
  • An initial set of documents which exemplarily
    represent (informally) substantial parts of the
    knowledge represented in the seed ontology

28
Methods for learning ontologies (5/8)
  • Information Extraction Rules
  • Compared to other ontology learning approaches
    this technique is not restricted to learning
    taxonomy relationships, but arbitary
    relationships in an application domain.
  • A project that uses this technique is the FRODO
    project.

29
Methods for learning ontologies (6/8)
  • Association Rules
  • Association-rule-learning algorithms are used for
    prototypical applications of data mining and for
    finding associations that occur between items in
    order to construct ontologies (extraction stage)
  • Classes are expressed by the expert as a free
    text conclusion to a rule. Relations between
    these classes may be discovered from existing
    knowledge bases and a model of the classes is
    constructed (ontology) based on user-selected
    patterns in the class relations
  • This approach is useful for solving
    classification problems by creating
    classification taxonomies (ontologies) from rules

30
Methods for learning ontologies (7/8)
  • Association Rules Example
  • A classification knowledge based system with
    experimental results based on medical data
    (Suryanto Compton Australia)
  • Ripple Down Rules (RDR) were used to describe
    classes and their attributes
  • ?Satisfactory lipid profile previous raised LDL
    noted ?
  • (LDL lt 3.4)AND(Triglyceride is
    NORMAL)AND(Max(LDL)gt3.4)OR
  • ((LDL is NORMAL)AND(Triglyceride is
    NORMAL)AND(Max(LDL) is HIGH)
  • Experts were allowed to modify or add conclusions
    in order to correct errors
  • The conclusions of the rules formed the classes
    of the classification ontology

31
Methods for learning ontologies (8/8)
  • Association Rules Example
  • Ontology learning methodology used
  • Firstly, class relations between rules were
    discovered. There were three basic relations
    subsumption/ intersection, mutual exclusivity and
    similarity
  • Secondly, more compound relations which appeared
    interesting using the three basic relations were
    specified
  • Finally, instances of these compound relations or
    patterns were extracted and the class model was
    assembled
  • Problems that occurred
  • Very similar conclusions were sometimes
    identified as mutually exclusive in cases where
    there different values for the same attribute
  • The method did not consider any other information
    about the classes themselves

32
  • Introduction Ontologies, Ontology learning
  • Technical description
  • Ontology learning in the Semantic Information
    descritpion
  • Ontology Learning Process
  • Ontology Learning - Architecture
  • Ontology Learning data sources
  • Methods used in ontology learning
  • Tools of ontology learning
  • Uses of ontology learning

33
Ontology learning tools ASIUM (1/8)
  • Acronym for "Acquisition of Semantic knowledge
    Using Machine learning method"
  • The main aim of Asium is to help the expert in
    the acquisition of semantic knowledge from texts
    and to generalize the knowledge of the corpus
  • Asium provides the expert with an interface which
    will first help him or her to explore the texts
    and then to learn knowledge which are not in the
    texts
  • During the learning step, Asium helps the expert
    to acquire semantic knowledge from the texts,
    like subcategorization frames and an ontology.
    The ontology represents an acyclic graph of the
    concepts of the studied domain. The
    subcategorization frames represent the use of the
    verbs in these texts

34
Ontology learning tools ASIUM (2/8)
  • Methodology

The input for Asium are syntactically parsed
texts from a specific domain. It then extracts
these triplets verb, preposition/function (if
there is no preposition), lemmatized head noun of
the complement. Next, using factorization, Asium
will group together all the head nouns occurring
with the same couple verb, preposition/function.
These lists of nouns are called basic clusters.
They are linked with the couples
verb,preposition/ function they are coming from.
35
Ontology learning tools ASIUM (3/8)
  • Methodology

Asium then computes the similarity among all the
basic clusters together. The nearest ones will be
aggregated and this aggregation is suggested to
the expert for creating a new concept. The expert
defines a minimum threshold for gathering
clusters into concepts. Any learned concepts can
contain noise (e.g. mistakes in the parsing), any
sub-concepts the expert wants to identify or
over-generalization due to aggre- gations may
occur,so the experts contribution is necessary.
36
Ontology learning tools ASIUM (4/8)
  • Methodology

After this, Asium will have learned the first
level of the ontology. Asium computes similarity
again but among all the clusters the old and the
new ones in order to learn the next level of the
ontology. The cooperative process runs until
there are no more possible aggregations. The
output of the learning process is an ontology and
subcategorization frames. The ontology represents
an acyclic graph of the concepts of the studied
domain. The subcategorization frames represent
the use of the verbs in these texts.
37
Ontology learning tools ASIUM (5/8)
  • Methodology
  • The advantages of this method are twofold
  • First, the similarity measure identifies all
    concepts of the domain and the expert can
    validate or split them. Next the learning process
    is, for one part, based on these new concepts and
    suggests more relevant and more general concepts.
  • Second, the similarity measure will offer the
    expert aggregations between already validated
    concepts and new basic clusters in order to get
    more knowledge from the corpus.

38
Ontology learning tools ASIUM (6/8)
  • The interface

This window allows the expert to validate the
concepts learned by Asium.
39
Ontology learning tools ASIUM (7/8)
  • The interface

This window displays the list of all the examples
covered for the learned concept. This display
allows the expert to visualize all the sentences
which will be allowed if this class is validated.
40
Ontology learning tools ASIUM (8/8)
  • The interface

This window displays the ontology like it
actually is in memory i.e. learned concepts and
concepts to be proposed for a level (each blue
circle represents a class).
41
Ontology learning tools TEXT-TO-ONTO (1/8)
  • It develops a semi-automatic ontology learning
    from text
  • It tries to overcome the knowledge acquisition
    bottleneck
  • It is based on a general architecture for
    discovering conceptual structures and engineering
    ontologies from text

42
Ontology learning tools TEXT-TO-ONTO (2/8)
43
Ontology learning tools TEXT-TO-ONTO (3/8)
  • Architecture

44
Ontology learning tools TEXT-TO-ONTO (4/8)
  • Architecture - Main components
  • Text Processing Management Component
  • The ontology engineer uses that component to
    select domain texts exploited in the further
    discovery process.Can choose among a set of text
    (pre-) processing methods available on the Text
    Processing Server and among a set of algorithms
    available at the Learning Discovering
    component.The former module returns text that is
    annotated by XML and XML-tagged is fed to the
    Learning Discovering component

45
Ontology learning tools TEXT-TO-ONTO (5/8)
  • Architecture - Main components
  • Text Processing Server
  • It contains a shallow text processor based on the
    core system SMES. SMES is a system that performs
    syntactic analysis on natural language documents
  • It organized in modules, such as tokenizer,
    morphological and lexical processing and chunk
    parsing that use lexical resources to produce a
    mixed syntactic/semantic information
  • The results are stored in annotations using
    XML-tagged text

46
Ontology learning tools TEXT-TO-ONTO (6/8)
  • Architecture - Main components
  • Lexical DB Domain Lexicon
  • SMES accesses a lexical database with more than
    120.000 stem entries and more than 12.000
    subcategorization frames that are used for
    lexical analysis and chunk parsing
  • The domain-specific part of the lexicon
    associates word stems with concepts available in
    the concept taxonomy and links syntactic
    information with semantic knowledge that may be
    further refined in the ontology

47
Ontology learning tools TEXT-TO-ONTO (7/8)
  • Architecture - Main components
  • Learning Discovering component
  • Uses various discovering methods on the annotated
    texts e.g. term extraction methods for concept
    acquisition.

48
Ontology learning tools TEXT-TO-ONTO (8/8)
  • Architecture - Main components
  • Ontology Engineering Enviroment-ONTOEDIT
  • Supports the ontology engineer in
    semi-automatically adding newly discovered
    conceptual structures to the ontology
  • Internally stores modeled ontologies using an XML
    serialization

49
  • Introduction Ontologies, Ontology learning
  • Technical description
  • Ontology learning in the Semantic Information
    descritpion
  • Ontology Learning Process
  • Ontology Learning - Architecture
  • Ontology Learning data sources
  • Methods used in ontology learning
  • Tools of ontology learning
  • Uses of ontology learning

50
Uses of ontology learning Knowledge sharing
(1/2)
  • Identifying candidate relations between
    expressive, diverse ontologies using concept
    cluster integration in multi-agent systems
  • Agents with diverse ontologies should be able to
    share knowledge by automated learning methods and
    agent communication strategies
  • Agents that do not know the relationships of
    their concepts to each other need to be able to
    teach each other these relationships (ontology
    learning)

51
Uses of ontology learning Knowledge sharing
(2/2)
  • Concept
    representation and
    learning on each
    agent
  • Process an agent sends a query to another agent
    and receives a response with new concepts. A new
    category is created from these concepts. The
    agent re-learns the ontology rules and if the new
    concept relation rules are verified, they are
    stored in the agent.

52
Uses of ontology learning Interest matching
(1/2)
  • Designing a general algorithm for interest
    matching is a major challenge in building online
    community and agent-based communication networks.
  • These algorithms can be applied in user
    categorization for an online community . Users
    behavior can be analyzed and matched against
    other users to provide collaborative
    categorization and recommendation services to
    tailor and enhance the online experience.
  • The process of finding similar users based on
    data from logged behavior in called interest
    matching.

53
Uses of ontology learning Interest matching
(2/2)
  • User interests can be described
    by ontologies as
    weighed tree- hierarchies
    of concepts
  • Each node has a weight attribute to represent the
    importance of the concept
  • These weights can be explored to calculate
    similarities between users
  • Learning process a standard ontology is used and
    the websites the user visits can be classified
    and entered into the standard ontology to
    personalize it if a user frequents websites of
    a category (instance of a class) it is likely he
    is interested in other instances of the class

54
Uses of ontology learning Web Directory
Classification
  • Ontologies and ontology learning can be used to
    create information extraction tools for
    collecting general information from the free text
    of web pages and classifying them in categories
  • The goal is to collect indicator terms from the
    web pages that may assist the classification
    process. This terms can be derived from directory
    headings of a web page as well as its content.
  • The indicator terms along with a collection of
    interpretation rules can result in a hierarchy
    (ontology) of web pages.

55
Uses of ontology learning E-mail classification
(1/2)
  • KMi Planet
  • A web-based news server for communication of
    stories between member in Knowledge Media
    Institute
  • Main goal To classify an incoming story, obtain
    the relevant objects within the story, deduce the
    relationships between them and to populate the
    ontology
  • Integrate a template-driven information
    extraction engine with an ontology engine to
    supply the necessary semantic content

56
Uses of ontology learning E-mail classification
(2/2)
  • KMi Planet
  • There are three tools
  • PlanetOnto
  • MyPlanet
  • an IE tool
  • PlanetOnto supports some activities.One of them
    is Ontology editing.In that point ontology
    learning is concerned.
  • A tool called WebOnto provides Web-based
    visualisation, browsing and editing support for
    the ontology. The Operational Conceptual
    Modelling Language, OCML, is a language designed
    for knowledge modeling. WebOnto uses OCML and
    allows the creation of classes and instances in
    the ontology, along with easier development and
    maintenance of the knowledge models

57
Bibliography
  • M.Sintek, M. Junker, Ludger van Est, A. Abecker,
    Using Information Extraction Rules for Extending
    Domain Ontologies, German Research Center for
    Artificial Intelligence (DFKI)
  • M.Vargas-Vera, J.Domingue, Y.Kalfoglou, E.Motta,
    S.Buckingham Shum, Template-Driven Information
    Extraction for Populating Ontologies, Knowledge
    Media Institute (UK)
  • G.Bisson, C.Nedellec, Designing clustering
    methods for ontology building, University of
    Paris
  • A.Maedche, S.Staab, The TEXT-TO-ONTO Ontology
    Learning Environment, University of Karlsruhe
  • A.Maedche, S.Staab, Ontology Learning for the
    Semantic Web, University of Karlsruhe
  • H.Suryanto,P.Compton, Learning classification
    taxonomies from a classification knowledge based
    system, University of New South Wales (Australia)
  • Proceedings of the First Workshop on Ontology
    Learning OL'2000 Berlin, Germany, August 25, 2000
  • Proceedings of the Second Workshop on Ontology
    Learning OL'2001 Seattle, USA, August 4, 2001
  • ASIUM web page http//www.lri.fr/faure/Demonstrat
    ion.UK/Presentation_Demo.html

58
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