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Knowledge representation is a long-history topic in AI, which is veryimportant. A variety of models have been proposed for knowledge graphembedding, which projects symbolic entities and relations into continuousvector space. However, most related methods merely focus on the data-fitting ofknowledge graph, and ignore the interpretable semantic expression. Thus,traditional embedding methods are not friendly for applications that requiresemantic analysis, such as question answering and entity retrieval. To thisend, this paper proposes a semantic representation method for knowledge graph\textbf{(KSR)}, which imposes a two-level hierarchical generative process thatglobally extracts many aspects and then locally assigns a specific category ineach aspect for every triple. Since both aspects and categories aresemantics-relevant, the collection of categories in each aspect is treated asthe semantic representation of this triple. Extensive experiments show that ourmodel outperforms other state-of-the-art baselines substantially.

KSR: A Semantic Representation of Knowledge Graph within a Novel Unsupervised Paradigm