Maybe these links could be helpful to answer to your question.

http://arxiv.org/ftp/arxiv/papers/1310/1310.8059.pdf

http://www.sciencedirect.com/science/article/pii/S0306457315000023

Hello

There are a lot of approaches for concepts matching (logic, non logic and hybrid)

Logic approaches : based on measures like (Exact, Subsume, Fail)

Non Logic approaches : based on IR techniques for similarity measure (Jiang & Conrath, Lin , Resnik, etc.)

and Hybrid approaches that combines the both approaches for developing more efficient concepts mechanisms.

I hope that help.

If you are working with DBpedia or Wikipedia, you can use word2vec algorithm (https://code.google.com/p/word2vec/). It gives good results for finding semantic similarity between words (concepts).

Thank you guys for your valuable answers :) 

I recommend you to look random walk/graph- based approachs ( even though  most of the time used for word sense disambiguation ). Please get more detail : "From senses to texts: An all-in-one graph-based approach for Measuring Semantic Similarity , MT Pilehvar   (2015):  its URL of researchgate is  "www.researchgate.net/publication/280490105_From_Senses_to_Texts_An_All-in-one_Graph-based_Approach_for_Measuring_Semantic_Similarity"

Article From Senses to Texts: An All-in-one Graph-based Approach for...

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