Choosing appropriate sense of a word from wordnet

Question

I am using Wordnet for finding synonyms of ontology concepts. How can i find choose the appropriate sense for my ontology concept. e.g there is an ontlogy concept "conference" it has following synsets in wordnet The noun conference has 3 senses (first 3 from tagged texts)

1. (12) conference -- (a prearranged meeting for consultation or exchange of information or discussion (especially one with a formal agenda))
2. (2) league, conference -- (an association of sports teams that organizes matches for its members)
3. (2) conference, group discussion -- (a discussion among participants who have an agreed (serious) topic) now 1st and 3rd synsets have apprpriate sense for my ontology concept. How can i choose only these two from wordnet?

Answer 1

The technology you're looking for is in the direction of semantic disambiguation / representation.

The most "traditional approach" is Word Sense Disambiguation (WSD), take a look at

• https://en.wikipedia.org/wiki/Word-sense_disambiguation
• https://stackoverflow.com/questions/tagged/word-sense-disambiguation
• Anyone know of some good Word Sense Disambiguation software?

Then comes the next generation of Word Sense induction / Topic modelling / Knowledge representation:

• https://en.wikipedia.org/wiki/Word-sense_induction
• https://en.wikipedia.org/wiki/Topic_model
• https://en.wikipedia.org/wiki/Knowledge_representation_and_reasoning

Then comes the most recent hype:

• Word embeddings, vector space models, neural nets

Sometimes people skip the semantic representation and goes directly to do text similarity and by comparing pairs of sentences, the differences/similarities before getting to the ultimate aim of the text processing.

Take a look at Normalize ranking score with weights for a list of STS related work.

On the other direction, there's

• ontology creation (Cyc, Yago, Freebase, etc.)
• semantic web (https://en.wikipedia.org/wiki/Semantic_Web)
• semantic lexical resources (WordNet, Open Multilingual WordNet, etc.)
• Knowledge base population (http://www.nist.gov/tac/2014/KBP/)

There's also a recent task on ontology induction / expansion:

• http://alt.qcri.org/semeval2015/task17/
• http://alt.qcri.org/semeval2016/task13/
• http://alt.qcri.org/semeval2016/task14/

Depending on the ultimate task, maybe either of the above technology would help.