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?

2

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

3

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

4

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

5

Ontologies

Example

6

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

7

Learning from Text

Meaning triangle Every language has symbols that evoke a

concept that refers to a concrete individual in the world

8

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

9

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