KSR: A Semantic Representation of Knowledge Graph within a Novel Unsupervised Paradigm
This addresses the need for semantic analysis in applications like question answering and entity retrieval, offering a novel approach to knowledge representation.
The paper tackles the problem of knowledge graph embedding lacking interpretable semantic expression by proposing KSR, a semantic representation method that uses a two-level hierarchical generative process to assign semantic categories to triples, achieving substantial performance improvements over state-of-the-art baselines.
Knowledge representation is a long-history topic in AI, which is very important. A variety of models have been proposed for knowledge graph embedding, which projects symbolic entities and relations into continuous vector space. However, most related methods merely focus on the data-fitting of knowledge graph, and ignore the interpretable semantic expression. Thus, traditional embedding methods are not friendly for applications that require semantic analysis, such as question answering and entity retrieval. To this end, this paper proposes a semantic representation method for knowledge graph \textbf{(KSR)}, which imposes a two-level hierarchical generative process that globally extracts many aspects and then locally assigns a specific category in each aspect for every triple. Since both aspects and categories are semantics-relevant, the collection of categories in each aspect is treated as the semantic representation of this triple. Extensive experiments show that our model outperforms other state-of-the-art baselines substantially.