ontology learning

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Oct 15, 2013 (3 years and 9 months ago)

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Ontology Learning

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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


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

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




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


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


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


Semantic Information Integration

Ontology Engineering


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


Ontology learning



Process (1/2)

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 ontology’s 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


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


Ontology learning



Architecture (1/5)

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.

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.

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.


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.

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


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

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

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


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


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 Mo’K workbench

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.


Learnin
g

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

Methods for learning

ontologies (3/8)


Information Extraction Rules

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 exemp
l
arily
represent (informally) substantial parts of the
knowledge represented in the seed ontology


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 projec
t.

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



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 <= 3.4
)
AND
(
Triglyceride

is NORMAL)AND(Max(LDL)>3.4)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

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


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


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

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.

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 expert’s
contribution is necessary.

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.

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.

Ontology learning tools



ASIUM (6/8)


The interface

This window allows the
expert to validate the
concepts learned by
Asium.

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.

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).

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

Ontology learning tools



TEXT
-
TO
-
ONTO (2/8)

Ontology learning tools



TEXT
-
TO
-
ONTO (3/8)


Architecture

Ontology learning tools



TEXT
-
TO
-
ONTO (4/8)


Architecture
-

Main components


Text & Processing Management Co
m
ponent


The ontol
o
gy 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

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 result
s

are stored in annotations using XML
-
tagged text

Ontology learning tools



TEXT
-
TO
-
ONTO (6/8)


Architecture
-

Main components


Lexical DB & Domain Lexicon


SMES accesses a lexical database with more
t
han 120.000 stem entries and more
than

12.000
subcategorization frames that are used for lexical
an
a
lysis 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
b
e
further refined in the ontology

Ontology learning tools



TEXT
-
TO
-
ONTO (7/8)


Architecture
-

Main components


Learning & Discovering comp
o
nent


Uses various discovering methods on the annotated
texts e.g. term extraction methods for concept
acquisition.

Ontology learning tools



TEXT
-
TO
-
ONTO (8/8)


Architecture
-

Main components


Ontology Engineering Enviroment
-
ONTOEDIT


Supports the ontol
o
gy engineer in semi
-
automatically
adding newly discovered conceptual structures to the
ontol
o
gy


Internally stores modeled ontologies using an XML
serialization


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


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)

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.

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.

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

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.

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

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


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/Demonstration.UK/Presentation_Demo.html