Knowledge graph (KG) completion aims at filling the missing facts in a KG, where a fact is typically represented as a triple in the form of (head, relation, tail). Traditional KG completion methods compel two- thirds ...Knowledge graph (KG) completion aims at filling the missing facts in a KG, where a fact is typically represented as a triple in the form of (head, relation, tail). Traditional KG completion methods compel two- thirds of a triple provided (e.g., head and relation) to predict the remaining one. In this paper, we propose a new method that extends multi-layer recurrent neural networks (RNNs) to model triples in a KG as sequences. It obtains state-of-the-art performance on the common entity prediction task, i.e., giving head (or tail) and relation to predict the tail (or the head), using two benchmark data sets. Furthermore, the deep sequential characteristic of our method enables it to predict the relations given head (or tail) only, and even predict the whole triples. Our experiments on these two new KG completion tasks demonstrate that our method achieves superior performance compared with several alternative methods.展开更多
Nowadays,with increasing open knowledge graphs(KGs)being published on the Web,users depend on open data portals and search engines to find KGs.However,existing systems provide search services and present results with ...Nowadays,with increasing open knowledge graphs(KGs)being published on the Web,users depend on open data portals and search engines to find KGs.However,existing systems provide search services and present results with only metadata while ignoring the contents of KGs,i.e.,triples.It brings difficulty for users’comprehension and relevance judgement.To overcome the limitation of metadata,in this paper we propose a content-based search engine for open KGs named CKGSE.Our system provides keyword search,KG snippet generation,KG profiling and browsing,all based on KGs’detailed,informative contents rather than their brief,limited metadata.To evaluate its usability,we implement a prototype with Chinese KGs crawled from Open KG.CN and report some preliminary results and findings.展开更多
基金the National Natural Science Foundation of China under Grant No.61872172.
文摘Knowledge graph (KG) completion aims at filling the missing facts in a KG, where a fact is typically represented as a triple in the form of (head, relation, tail). Traditional KG completion methods compel two- thirds of a triple provided (e.g., head and relation) to predict the remaining one. In this paper, we propose a new method that extends multi-layer recurrent neural networks (RNNs) to model triples in a KG as sequences. It obtains state-of-the-art performance on the common entity prediction task, i.e., giving head (or tail) and relation to predict the tail (or the head), using two benchmark data sets. Furthermore, the deep sequential characteristic of our method enables it to predict the relations given head (or tail) only, and even predict the whole triples. Our experiments on these two new KG completion tasks demonstrate that our method achieves superior performance compared with several alternative methods.
基金supported by the Nantional Science Foundation of Chnia(No.62072224)
文摘Nowadays,with increasing open knowledge graphs(KGs)being published on the Web,users depend on open data portals and search engines to find KGs.However,existing systems provide search services and present results with only metadata while ignoring the contents of KGs,i.e.,triples.It brings difficulty for users’comprehension and relevance judgement.To overcome the limitation of metadata,in this paper we propose a content-based search engine for open KGs named CKGSE.Our system provides keyword search,KG snippet generation,KG profiling and browsing,all based on KGs’detailed,informative contents rather than their brief,limited metadata.To evaluate its usability,we implement a prototype with Chinese KGs crawled from Open KG.CN and report some preliminary results and findings.
