Applications Chapter 9, Cimiano Ontology Learning Textbook Presented by Aaron Stewart

Typical Applications of Ontologies Agent communication Data integration Description of service capabilities for matching and composition purposes Formal verification of process descriptions Unification of terminology across communities

Text Applications of Ontologies Information Retrieval (IR) Clustering and Classification of Documents Semantic Annotation Natural Language Processing

Task-Based Evaluation (Porzel and Malaka 2005)

Task-Based Evaluation Requirements 1.Algorithm output can be quantified 2.Task can use background knowledge 3.Ontology is an additional parameter 4.Output can be traced to the ontology

Contents 1.Text Clustering and Classification 2.Information Highlighting for Supporting Search 3.Related Work

Text Clustering and Classification What is the difference?

Text Clustering

Text Classification ArrowsWeatherFlat shapes3-D formsSmile!

Dot Kom Project One of many competitions

Approaches Bag of words Manually engineered MeSH Tree Structures Automatically constructed ontologies

What is a “Bag of Words” anyway? the quick brown fox

Bag of Words thequickbrownfoxjumpsoverthelazydog (2)

Building Hierarchies

Note on Ontologies Our ontologies (“micro”) – Like a database record schema Their ontologies (“macro”) – Like WordNet

Clustering Hierarchical Agglomerative Clustering Bi-Section K-means “A Comparison of Document Clustering Techniques” –

Document Representations Bag of Words Certain words + ontology -> extended features Strategies: add, replace, only

Vectors and Cosine Similarity

Classification Results (Categories)

Classification Results (Documents)

Cluster Metrics P : computer-generated clusters L : human-created clusters P, L : sets of clusters (partitioning)

Clustering Results

Information Highlighting for Supporting Search Challenge: – 10 minute limit – KMi Planet News web site – Compile a list of important People Technologies

Information Highlighting for Supporting Search Tools: – Regular browser – Magpie – ESpotter – C-PANKOW

Teams A : web browser only B : web browser with AKT information C : web browser with AKT++ information

AKT++ Lexicon

Scores

Conclusions (for this section) Generated ontologies can be comparable to hand-crafted ontologies Humans can trust the computer too much! (Group C drop in score)

Related Work Query Expansion Information Retrieval Text Clustering and Classification Natural Language Processing

Ambiguity resolution – Bank Compounds – Headache medicine Vague words – With, of, has – Selectional restrictions Anaphora

More Applications Word sense disambiguation Classification of unknown words Named Entity Recognition (NER) Anaphora Resolution Question Answering – Who wrote the Hobbit? – Tolkien is the author of the Hobbit. Information Extraction – AUTOSLOG, ASIUM

Analysis/Conclusion Pro/con: – Focused on two systems – Passing survey of others