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Co-occurrence information between words is the basis of training word embeddings; besides, Chinese characters are composed of subcharacters, words made up by the same characters or subcharacters usually have similar semantics, but this internal substructure information is usually neglected in popular models. In this paper, we propose a novel method for learning Chinese word embeddings, which takes full use of external co-occurrence context information and internal substructure information. We represent each word as a bag of subcharacter n-grams, and our model learns the vector representation corresponding to the word and its subcharacter n-grams. The final word embeddings are represented as the sum of these two kinds of vector representation, which makes the learned word embeddings can take into account both the internal structure information and external co-occurrence information possible. The experiments show that our method outperforms state-of-the-art performance on benchmarks.

In order to solve the problem of lack of effective methods for ontology inconsistency, a user preferences-oriented ontology alignment repair model is proposed. This model uses 0-1 linear programming method to minimize the remove cost; the structure and source of ontology are used to measure the axiom of importance; Finally, by choosing the minimal conflict sets and the user preferences limit repair strategy, the purpose of eliminating inconsistency, reducing semantic loss and guaranteeing credibility is achieved. The experimental results show that this method can effectively solve the problem of ontology inconsistency, and the restored ontology is more suitable for user preferences than traditional methods.