Ontology Learning and Population from Text: Algorithms, Evaluation and Applications
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Transcript of Ontology Learning and Population from Text: Algorithms, Evaluation and Applications
ONTOLOGY LEARNING AND POPULATION FROM TEXT: ALGORITHMS, EVALUATION
AND APPLICATIONS
Presented by Sole
Chapters 1 - 5
Introduction Artificial intelligence
Build systems that incorporate knowledge about a domain to reason on the basis of this knowledge and solve problems not encountered before Include explicit and symbolic representation of
knowledge about a domain Symbolic representation and procedural aspects
are separated so that it can be reused across systems
Which symbols to use and what they stand for?
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Introduction Ontology
Defines what is important in a domain and how concepts are related Knowledge-based system: determine which
symbols are needed and how they are interpreted Logical level: interpretation can be constraint
according to the ontology by axiomatizing symbols Issues
Costly to construct Time-consuming Significant coverage of domain is needed Meaning and consistent generalization are required
Knowledge
Acquisition
Bottleneck
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Introduction Solution
Automatically learn ontologies from data Goal: bridging the gap between
World of symbols (words used in natural language) World of concepts (abstractions of human thought)
Challenge Correctness and consistency of the model can not
be guaranteed Human post-processing definitely necessary
Automatically learned ontologies need to be inspected, validated, and modified by humans before they can be applied for applications relying on logical reasoning
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Ontologies Definition
Philosophical discipline Science of existence or the study of being
Computer Science Formal specifications of a conceptualization
Resources representing the conceptual model underlying a certain domain, describing it in a declarative fashion and thus cleanly separating it from procedural aspects
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Ontologies Example
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Learning from Text Ontology learning
Acquire a domain model from data Lifting : XML-DTDs, UML diagrams, databases Semi-structured sources: HTML, XML Unstructured sources: ontology learning from text
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Learning from Text Meaning triangle
Every language has symbols that evoke a concept that refers to a concrete individual in the world
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Learning from Text Ontology population
Learning concepts and relations Knowledge markup or annotation: select text
fragments and assign them to an ontological concept
Applications Several methods have been developed in
recent years Challenge
No consensus within ontology learning community on concrete tasks for ontology learning
Comparison between approaches is difficult
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Learning from Text10
Ontology learning tasks (layer cake)
Learning from Text11
Terms: Task: find a set of relevant concepts and
relations E.g., words, multi-word compounds
State-of-the-art IR methods NLP methods: POS tagger, statistical
approaches
Learning from Text12
Synonyms: Task: find words which denote the same
concept E.g., synsets on WordNet
State-of-the-art Semantically-similar words Sense disambiguation and synonym discovery Latent Semantic Indexing (LSI) Statistical information measures defined over
the Web to detect synonyms
Learning from Text13
Concepts: Task: find intentional definitions of concept,
their extension, and lexical signs used to refer to them
State-of-the-art Clusters of related terms LSI-based techniques Discovery of hierarchies of named entities Know-it-all system OntoLearn system
Learning from Text14
Hierarchies: Task: concept hierarchy induction,
refinement and lexical extension State-of-the-art
Lexico-syntactic patterns Clustering algorithm to automatically derive
concept hierarchies Analysis of term co-occurrence in same
sentence/document
Learning from Text15
Relations: Task: learn relations identifiers or labels as
well as their appropriate domain and range State-of-the-art
Association rules Syntactic-dependencies
Very few approaches address the issue of learning ontology relations from text
Learning from Text16
Axiom schemata instantiations: Task: learn which concepts, relations, or
pair of concepts the axioms in a given system apply to
General axioms Task: derive more complex relationships
and connections between concepts and relations Logical interpretations constraining the
interpretation of concepts and relations
Learning from Text17
Population: Task: learn instances of concepts and
relations State-of-the-art
Associated to well-known tasks for which a variety of approaches have been developed
Information extraction Named entity recognition
Basics18
Natural Language Processing
Basics19
Pre-processing steps
Chunking Syntactic analysis: parsing
NLP
Basics20
Pre-processing
Contextual features
Syntactic dependencies
Bank
River FinancialInstitution
The museum houses an impressive collection of medieval and modern art. The building combines geometric abstraction with classical references that allude to the Roman influence on the region.
NLP
Basics21
Similarity measures
NLP
Basics22
Similarity measures Binary similarity measures
Geometric similarity measures
NLP
Basics23
Similarity measures Measures based on probability distribution
Hypothesis testing
NLP
Basics24
Term relevance Weight the importance of a term in a
document
NLP
Basics25
WordNet Lexical database for the English language
NLP
Basics26
Formal concept analysis Formal objects: concepts+ Formal attributes: characteristics describing
objects+ Incidence relation: information about which
attributes hold for each object= Formal context
Basics27
Example
FCA
Basics28
Example
FCA
Basics29
Machine learning Automatic recognition/detection of patterns
and regularities within sample data Patterns can be used to understand/describe the
data or to make predictions Learning process
Supervised Predicts the appropriate category for an example
from a set of categories represented by a set of labels
Unsupervised Search for common and frequent structures within
the data (data exploration)
Basics30
Supervised learning Regression
Numeric prediction (labels are continue values) Classification
Assign proper category to a given example
ML
Target value
Feature vector
Basics31
Classifiers Bayesian Classifiers Decision Trees Instance-Based Learning Support Vector Machines Artificial Neural Networks
Tools WEKA RapidMiner
ML
Basics32
Examples
ML
Basics33
Unsupervised learning Clustering: find groups of similar objects in data
There is no labeled data to train from Classification
Hierarchical vs. non-hierarchical Non-hierarchical algorithms produce a set of groups Hierarchical algorithms order groups in a tree
structure Hard vs. soft
Hard: elements are assigned to distinct clusters Soft: elements are assigned to clusters with a
certain degree of membership
ML
Basics34
Algorithms K-means Hierarchical clustering Hierarchical Agglomerative (Bottom-Up)
Clustering Divisive (Top-Down) Clustering
ML
Datasets35
Corpus description