This work proposes an unsupervised topological features based entity disambiguation solution. Most existing studies leverage semantic information to resolve ambiguous references. However, the semantic information is n...This work proposes an unsupervised topological features based entity disambiguation solution. Most existing studies leverage semantic information to resolve ambiguous references. However, the semantic information is not always accessible because of privacy or is too expensive to access. We consider the problem in a setting that only relationships between references are available. A structure similarity algorithm via random walk with restarts is proposed to measure the similarity of references. The disambiguation is regarded as a clustering problem and a family of graph walk based clustering algorithms are brought to group ambiguous references. We evaluate our solution extensively on two real datasets and show its advantage over two state-of-the-art approaches in accuracy.展开更多
Entity Linking(EL)aims to automatically link the mentions in unstructured documents to corresponding entities in a knowledge base(KB),which has recently been dominated by global models.Although many global EL methods ...Entity Linking(EL)aims to automatically link the mentions in unstructured documents to corresponding entities in a knowledge base(KB),which has recently been dominated by global models.Although many global EL methods attempt to model the topical coherence among all linked entities,most of them failed in exploiting the correlations among manifold knowledge helpful for linking,such as the semantics of mentions and their candidates,the neighborhood information of candidate entities in KB and the fine-grained type information of entities.As we will show in the paper,interactions among these types of information are very useful for better characterizing the topic features of entities and more accurately estimating the topical coherence among all the referred entities within the same document.In this paper,we present a novel HEterogeneous Graph-based Entity Linker(HEGEL)for global entity linking,which builds an informative heterogeneous graph for every document to collect various linking clues.Then HEGEL utilizes a novel heterogeneous graph neural network(HGNN)to integrate the different types of manifold information and model the interactions among them.Experiments on the standard benchmark datasets demonstrate that HEGEL can well capture the global coherence and outperforms the prior state-of-the-art EL methods.展开更多
基金This work is supported by the National Basic Research 973 Program of China under Grant No. 2012CB316201, the Fundamental Research Funds for the Central Universities of China under Grant No. N120816001, and the National Natural Science Foundation of China under Grant Nos. 61472070 and 61402213.
文摘This work proposes an unsupervised topological features based entity disambiguation solution. Most existing studies leverage semantic information to resolve ambiguous references. However, the semantic information is not always accessible because of privacy or is too expensive to access. We consider the problem in a setting that only relationships between references are available. A structure similarity algorithm via random walk with restarts is proposed to measure the similarity of references. The disambiguation is regarded as a clustering problem and a family of graph walk based clustering algorithms are brought to group ambiguous references. We evaluate our solution extensively on two real datasets and show its advantage over two state-of-the-art approaches in accuracy.
基金supported in part by the National Key R&D Program of China(No.2020AAA0106600)the Key Laboratory of Science,Technology and Standard in Press Industry(Key Laboratory of Intelligent Press Media Technology)
文摘Entity Linking(EL)aims to automatically link the mentions in unstructured documents to corresponding entities in a knowledge base(KB),which has recently been dominated by global models.Although many global EL methods attempt to model the topical coherence among all linked entities,most of them failed in exploiting the correlations among manifold knowledge helpful for linking,such as the semantics of mentions and their candidates,the neighborhood information of candidate entities in KB and the fine-grained type information of entities.As we will show in the paper,interactions among these types of information are very useful for better characterizing the topic features of entities and more accurately estimating the topical coherence among all the referred entities within the same document.In this paper,we present a novel HEterogeneous Graph-based Entity Linker(HEGEL)for global entity linking,which builds an informative heterogeneous graph for every document to collect various linking clues.Then HEGEL utilizes a novel heterogeneous graph neural network(HGNN)to integrate the different types of manifold information and model the interactions among them.Experiments on the standard benchmark datasets demonstrate that HEGEL can well capture the global coherence and outperforms the prior state-of-the-art EL methods.
