ontology learning and population from text algorithms evaluation and applications
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Chapters 1 - 5. Ontology Learning and Population from Text: Algorithms, Evaluation and Applications. Presented by Sole. Introduction. Artificial intelligence

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introduction
Introduction
  • Artificial intelligence
    • Build systems that incorporate knowledge about a domain to reasonon 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?

introduction1
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

introduction2
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
ontologies
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
ontologies1
Ontologies
  • Example
learning from text
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
learning from text1
Learning from Text
  • Meaning triangle
    • Every language has symbols that evoke a concept that refers to a concrete individual in the world
learning from text2
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
learning from text3
Learning from Text
  • Ontology learning tasks (layer cake)
learning from text4
Learning from Text
  • 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 text5
Learning from Text
  • 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 text6
Learning from Text
  • 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 text7
Learning from Text
  • 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 text8
Learning from Text
  • 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 text9
Learning from Text
  • 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 text10
Learning from Text
  • 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
basics
Basics
  • Natural Language Processing
basics1
Basics

NLP

  • Pre-processing steps

Chunking

Syntactic analysis: parsing

basics2
Basics

NLP

  • Pre-processing

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.

Bank

River

Financial

Institution

Contextual features

Syntactic dependencies

basics3
Basics

NLP

  • Similarity measures
basics4
Basics

NLP

  • Similarity measures
    • Binary similarity measures
  • Geometric similarity measures
basics5
Basics

NLP

  • Similarity measures
    • Measures based on probability distribution
  • Hypothesis testing
basics6
Basics

NLP

  • Term relevance
    • Weight the importance of a term in a document
basics7
Basics

NLP

  • WordNet
    • Lexical database for the English language
basics8
Basics
  • Formal concept analysis
    • Formal objects: concepts

+

    • Formal attributes: characteristics describing objects

+

    • Incidence relation: information about which attributes hold for each object

=

    • Formal context
basics9
Basics

FCA

  • Example
basics10
Basics

FCA

  • Example
basics11
Basics
  • 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)
basics12
Basics

ML

  • Supervised learning
    • Regression
      • Numeric prediction (labels are continue values)
    • Classification
      • Assign proper category to a given example

Target value

Feature vector

basics13
Basics

ML

  • Classifiers
    • Bayesian Classifiers
    • Decision Trees
    • Instance-Based Learning
    • Support Vector Machines
    • Artificial Neural Networks
  • Tools
    • WEKA
    • RapidMiner
basics14
Basics

ML

  • Examples
basics15
Basics

ML

  • 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
basics16
Basics

ML

  • Algorithms
    • K-means
    • Hierarchical clustering
    • Hierarchical Agglomerative (Bottom-Up) Clustering
    • Divisive (Top-Down) Clustering
datasets
Datasets
  • Corpus description
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