Improving Entity Linking in Chinese Domain by Sense Embedding Based on Graph Clustering

Entity linking refers to linking a string in a text to corresponding entities in a knowledge base through candidate entity generation and candidate entity ranking.It is of great significance to some NLP(natural language processing)tasks,such as question answering.Unlike English entity linking,Chinese entity linking requires more consideration due to the lack of spacing and capitalization in text sequences and the ambiguity of characters and words,which is more evident in certain scenarios.In Chinese domains,such as industry,the generated candidate entities are usually composed of long strings and are heavily nested.In addition,the meanings of the words that make up industrial entities are sometimes ambiguous.Their semantic space is a subspace of the general word embedding space,and thus each entity word needs to get its exact meanings.Therefore,we propose two schemes to achieve better Chinese entity linking.First,we implement an ngram based candidate entity generation method to increase the recall rate and reduce the nesting noise.Then,we enhance the corresponding candidate entity ranking mechanism by introducing sense embedding.Considering the contradiction between the ambiguity of word vectors and the single sense of the industrial domain,we design a sense embedding model based on graph clustering,which adopts an unsupervised approach for word sense induction and learns sense representation in conjunction with context.We test the embedding quality of our approach on classical datasets and demonstrate its disambiguation ability in general scenarios.We confirm that our method can better learn candidate entities’fundamental laws in the industrial domain and achieve better performance on entity linking through experiments.

Discovering Cohesive Temporal Subgraphs with Temporal Density Aware Exploration

Real-world networks,such as social networks,cryptocurrency networks,and e-commerce networks,always have occurrence time of interactions between nodes.Such networks are typically modeled as temporal graphs.Mining cohesive subgraphs from temporal graphs is practical and essential in numerous data mining applications,since mining cohesive subgraphs gets insights into the time-varying nature of temporal graphs.However,existing studies on mining cohesive subgraphs,such as Densest-Exact and k-truss,are mainly tailored for static graphs(whose edges have no temporal information).Therefore,those cohesive subgraph models cannot indicate both the temporal and the structural characteristics of subgraphs.To this end,we explore the model of cohesive temporal subgraphs by incorporating both the evolving and the structural characteristics of temporal subgraphs.Unfortunately,the volume of time intervals in a temporal network is quadratic.As a result,the time complexity of mining temporal cohesive subgraphs is high.To efficiently address the problem,we first mine the temporal density distribution of temporal graphs.Guided by the distribution,we can safely prune many unqualified time intervals with the linear time cost.Then,the remaining time intervals where cohesive temporal subgraphs fall in are examined using the greedy search.The results of the experiments on nine real-world temporal graphs indicate that our model outperforms state-of-the-art solutions in efficiency and quality.Specifically,our model only takes less than two minutes on a million-vertex DBLP and has the highest overall average ranking in EDB and TC metrics.