Commonsense knowledge is paramount to enable intelligent systems. Typically, it is characterized as being implicit and ambiguous, hindering thereby the automation of its acquisition. To address these challenges, this paper presents semantically enhanced models to enable reasoning through resolving part of commonsense ambiguity. The proposed models enhance in a knowledge graph embedding (KGE) framework for knowledge base completion. Experimental results show the effectiveness of the new semantic models in commonsense reasoning.
Our work aligns closely with neural network like methods. We re-cast the problem of commonsense knowledge acquisition as knowledge base completion in which we rely on vector space representations to perform reasoning. @cite_22 devised two models, bilinear and deep neural network, to embed concepts and provide scores to arbitrary triple. They considered concepts as phrasal terms and learned their representations through word embeddings trained over a limited training set, i.e., using the sentences underlying training triples. In contrast, we rely on a border set of semantic information resources that we import into knowledge base representation learning model via joint objective function to combine their local relational structure with their richer global semantics.
@cite_22
2509019445
We enrich a curated resource of commonsense knowledge by formulating the problem as one of knowledge base completion (KBC). Most work in KBC focuses on knowledge bases like Freebase that relate entities drawn from a fixed set. However, the tuples in ConceptNet (Speer and Havasi, 2012) define relations between an unbounded set of phrases. We develop neural network models for scoring tuples on arbitrary phrases and evaluate them by their ability to distinguish true held-out tuples from false ones. We find strong performance from a bilinear model using a simple additive architecture to model phrases. We manually evaluate our trained model’s ability to assign quality scores to novel tuples, finding that it can propose tuples at the same quality level as mediumconfidence tuples from ConceptNet.