Ontology Learning and Population from Text: Algorithms, Evaluation and Applications

1. Chapters 1 - 5 Ontology Learning and Population from Text: Algorithms, Evaluation and Applications Presented by Sole

2. 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?

3. 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

4. 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

5. 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

6. Ontologies • Example

7. 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

8. Learning from Text • Meaning triangle • Every language has symbols that evoke a concept that refers to a concrete individual in the world

9. 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

10. Learning from Text • Ontology learning tasks (layer cake)

11. 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

12. 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

13. 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

14. 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

15. 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

16. 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

17. 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

18. Basics • Natural Language Processing

19. Basics NLP • Pre-processing steps Chunking Syntactic analysis: parsing

20. 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

21. Basics NLP • Similarity measures

22. Basics NLP • Similarity measures • Binary similarity measures • Geometric similarity measures

23. Basics NLP • Similarity measures • Measures based on probability distribution • Hypothesis testing

24. Basics NLP • Term relevance • Weight the importance of a term in a document

25. Basics NLP • WordNet • Lexical database for the English language

26. Basics • Formal concept analysis • Formal objects: concepts + • Formal attributes: characteristics describing objects + • Incidence relation: information about which attributes hold for each object = • Formal context

27. Basics FCA • Example

28. Basics FCA • Example

29. 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)

30. Basics ML • Supervised learning • Regression • Numeric prediction (labels are continue values) • Classification • Assign proper category to a given example Target value Feature vector

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

32. Basics ML • Examples

33. 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

34. Basics ML • Algorithms • K-means • Hierarchical clustering • Hierarchical Agglomerative (Bottom-Up) Clustering • Divisive (Top-Down) Clustering

35. Datasets • Corpus description