FOCUS 1

Using Machine learning (and AI) in ontology matching

Problem:

Solutions:

Return value, benefiting fields, and applications:

i.e.

More details see:
[[research: engineering the problem]]



Reference:

[1] Ontology Matching: A Machine Learning Approach http://homes.cs.washington.edu/~pedrod/papers/hois.pdf
[2] Probabilistic Similarity Logic http://event.cwi.nl/uai2010/papers/UAI2010_0089.pdf

FOCUS 2

Ontology Learning

Automatic creation of ontology, includes extracting the corresponding domain’s terms and relationships from corpus. [1]

Problem:

Techniques:



References:

[1] http://en.wikipedia.org/wiki/Ontology_learning


FOCUS 3

Extracting Knowledge Base from Corpus

Problem:

Building an ontology is a manual, error-prone, and tedious process. Finding a tool

Idea: Automated ontology generation (domain specific) using NLP techniques.

Steps:

- Build a python module or framework (name it Pyonto) to transform natural language sentences into ontology concepts. [see, [2]]

Solution:
- Use facts (axioms in natural language) and the true KB from knowledge extraction engines (i.e. NELL, FreeBase .. ) and from structured semantic databases (i.e. DBpedia, LinkedData..) to automatically generate domain specific ontologies. See [9]

[1]
NLP for onto: Applying NLP FOR BUILDING DOMAIN ONTOLOGY:
FASHION COLLECTION

[2]

[3]

[4]
Also check out python-owl Seth: http://seth-scripting.sourceforge.net/

[5] IRBook.docset from Stanford’s Intro to Info Retrieval

[6] paper: Survey on clustering methods for ontological knowledge

[7] Chapter 5: Ontology Learning Using Word Net Lexical Expansion and Text Mining read it online: http://goo.gl/Gi0JRz

[8] Generating Ontologies from Linked Data GOLD

[9] Unsupervised learning of semantic relations for molecular biology ontologies, pdf