Knowledge bases are employed in a variety of applications from natural language processing to semantic web search; alas, in practice their usefulness is hurt by their incompleteness. Embedding models attain state-of-the-art accuracy in knowledge base completion, but their predictions are notoriously hard to interpret. In this paper, we adapt "pedagogical approaches" (from the literature on neural networks) so as to interpret embedding models by extracting weighted Horn rules from them. We show how pedagogical approaches have to be adapted to take upon the large-scale relational aspects of knowledge bases and show experimentally their strengths and weaknesses.
There are also a variety of techniques that aim at obtaining interpretability in the context of KBs: take the interpretability of each dimension of a word embedding as the capacity one has of distinguishing an intrusive word, i.e., one which has a low value in that dimension in comparison to other words in a group. From that perspective, they proposed a variant of matrix factorization that is highly interpretable. proposed a technique for inducing interpretability in KB embeddings by incorporating additional entity co-occurence statistics from text, while still maintaining comparable performance in predictive tasks. proposed a stochastic topic model for link prediction that produces explanations based on the type of each predicted link (links can be "topical" or "social"). KSR @cite_6 learns semantic features for knowledge graphs. proposed an embedding model and designed a learning algorithm to induce interpretable sparse representations in it. developed an efficient and explainable technique for link prediction using topological features.
@cite_6
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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 , 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.