Table 1 Summary of semantic similarity approaches

Approach

Techniques

Pros & Cons

Edge-based

Distance; Common path

Pros: intuitive, easy to perform.

Cons: edge-based approaches assume all the nodes

and edges are uniformly distributed and treat them who are in the same depth equally, which is not applicable for real data.

Node-based

MICA^a; DCA^b

Pros: node-based approaches measure the terms independent of their depth in the ontology.

Cons: the common used term would make more contribution when calculating the similarity.

Approach

Techniques

Pros & Cons

Pairwise

All pairs

Pros: the contributions from every pair of terms are concerned.

Cons: over-reliance on the quality of data; time-consuming

Best Pairs

Group-wise

Set-based

Pros: group-wise approaches compare term combinations from a macro view instead of relying on integrating similarity between individual terms; time-saving.

Cons: excessive choices could be a trouble.

Graph-based

Vector-based