Abstract:
Semantic networks connect concepts with semantic relations. It has binary and multiplex semantic networks. The associated fact prediction link prediction task mines the high-level network representation to infer implicitly connected facts.
Previous associated fact prediction methods focused on network topology but ignored semantic expression. SemNE, a semantic network encoder, learns a feature mapping function from binary semantic networks and can be pre-trained on multiplex semantic networks. An embedding encoder and prediction decoder make up SemNE.
It enriches network representation by modeling semantic information and topology. A factual boundary-based word self-organization method unifies topological and semantic feature representations. Experimental results on binary and multiplex semantic networks show that SemNE achieves state-of-the-art results in associated fact prediction and is scalable and can improve existing models.
Note: Please discuss with our team before submitting this abstract to the college. This Abstract or Synopsis varies based on student project requirements.
Did you like this final year project?
To download this project Code with thesis report and project training... Click Here
