Metaphor is a figure of speech that describes one thing (a target) by mentioning another thing (a source) in a way that is not literally true. Metaphor understanding is an interesting but challenging problem in natural language processing. This paper presents a novel method for metaphor processing based on knowledge graph (KG) embedding. Conceptually, we abstract the structure of a metaphor as an attribute-dependent relation between the target and the source. Each specific metaphor can be represented as a metaphor triple (target, attribute, source). Therefore, we can model metaphor triples just like modeling fact triples in a KG and exploit KG embedding techniques to learn better representations of concepts, attributes and concept relations. In this way, metaphor interpretation and generation could be seen as KG completion, while metaphor detection could be viewed as a representation learning enhanced concept pair classification problem. Technically, we build a Chinese metaphor KG in the form of metaphor triples based on simile recognition, and also extract concept-attribute collocations to help describe concepts and measure concept relations. We extend the translation-based and the rotation-based KG embedding models to jointly optimize metaphor KG embedding and concept-attribute collocation embedding. Experimental results demonstrate the effectiveness of our method. Simile recognition is feasible for building the metaphor triple resource. The proposed models improve the performance on metaphor interpretation and generation, and the learned representations also benefit nominal metaphor detection compared with strong baselines.

The Maximum Clique is the most compact cohesive subgroup in Social Network. Finding the maximum clique in the Social Network has become an important aspect of social network analysis, such as privacy protection, citation and co-citation analysis, cohesive subgroup analysis et al. With the development of big data, the mass of nodes in the graph and complexity of analysis set a higher requirement for solving the maximum clique problem (MCP). Therefore, we propose an improved ant colony algorithm. Particularly, the strategy of the ant to select the nodes is improved so that the search space can be expanded and the variety of the solution is increased, with this approach local optimal solution can be avoided. Local improvement of the clique is also adopted to improve the accuracy and convergence speed of the proposed algorithm. The proposed algorithm has been tested on the DIMACS benchmark dataset and several typical social networks. Experimental results show the effectiveness and feasibility of the proposed algorithm.