Cross-Lingual Type Inference

Xu, Bo; Zhang, Yi; Liang, Jiaqing; Hwang, Seung Won; Wang, Wei

doi:10.1007/978-3-319-32025-0_28

Cited by 21 publications

(18 citation statements)

References 13 publications

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“…• CUTE: (Xu et al, 2016) utilizes three kinds of entity features: category, property and property-value pair, and employs a hierarchical multi-label classification method.…”

Section: Methods For Comparisonmentioning

confidence: 99%

“…(Neelakantan and Chang, 2015) use text description as entity feature representation, then design a global objective function to predict missing entity types in a KB. (Xu et al, 2016) use property and category information, along with a multi-label hierarchical classifier, to assign DBpedia types to Chinese entities. (Yaghoobzadeh and Schütze, 2015)first propose FIGMENT to address this problem.…”

Section: Related Workmentioning

confidence: 99%

“…State-of-the-art methods normally learn distributed representation for each entity, and apply a multi-label classification model to make type inference. For example, (Neelakantan and Chang, 2015) and (Xu et al, 2016) exploit various kinds of information to construct the feature representation of an entity, such as entity textual description, property and category. After that, a predict function is learned to infer whether entity e is an instance of type t. focus on the names of entities and the context of entity mentions (in text), and design two scoring models for pairs of entities and types.…”

Section: Introductionmentioning

confidence: 99%

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Fine-Grained Entity Typing via Hierarchical Multi Graph Convolutional Networks

Jin

Hou

et al. 2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

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This paper addresses the problem of inferring the fine-grained type of an entity from a knowledge base. We convert this problem into the task of graph-based semi-supervised classification, and propose Hierarchical Multi Graph Convolutional Network (HMGCN), a novel Deep Learning architecture to tackle this problem. We construct three kinds of connectivity matrices to capture different kinds of semantic correlations between entities. A recursive regularization is proposed to model the subClassOf relations between types in given type hierarchy. Extensive experiments with two large-scale public datasets show that our proposed method significantly outperforms four state-of-the-art methods.

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“…• CUTE: (Xu et al, 2016) utilizes three kinds of entity features: category, property and property-value pair, and employs a hierarchical multi-label classification method.…”

Section: Methods For Comparisonmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Fine-Grained Entity Typing via Hierarchical Multi Graph Convolutional Networks

Jin

Hou

et al. 2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

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“…Cross-Lingual Type Inference: Xu et al [27] proposed a cross-lingual type inference approach which types Chinese entities with some widely accepted taxonomies in English, such as the taxonomy of DBpedia. This is a new task which does not exist in constructing English knowledge graphs, because many non-English knowledge graphs fail to type their entities due to the absence of a reasonable local hierarchical taxonomy.…”

Section: Type Inferencementioning

confidence: 99%

A Survey of Techniques for Constructing Chinese Knowledge Graphs and Their Applications

et al. 2018

Sustainability

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With the continuous development of intelligent technologies, knowledge graph, the backbone of artificial intelligence, has attracted much attention from both academic and industrial communities due to its powerful capability of knowledge representation and reasoning. In recent years, knowledge graph has been widely applied in different kinds of applications, such as semantic search, question answering, knowledge management and so on. Techniques for building Chinese knowledge graphs are also developing rapidly and different Chinese knowledge graphs have been constructed to support various applications. Under the background of the "One Belt One Road (OBOR)" initiative, cooperating with the countries along OBOR on studying knowledge graph techniques and applications will greatly promote the development of artificial intelligence. At the same time, the accumulated experience of China in developing knowledge graphs is also a good reference to develop non-English knowledge graphs. In this paper, we aim to introduce the techniques of constructing Chinese knowledge graphs and their applications, as well as analyse the impact of knowledge graph on OBOR. We first describe the background of OBOR, and then introduce the concept and development history of knowledge graph and typical Chinese knowledge graphs. Afterwards, we present the details of techniques for constructing Chinese knowledge graphs, and demonstrate several applications of Chinese knowledge graphs. Finally, we list some examples to explain the potential impacts of knowledge graph on OBOR.Sustainability 2018, 10, 3245 The Development of Knowledge GraphKnowledge graph is essentially originated from semantic network [3]. Semantic network was proposed in late 1950s and early 1960s, and it is a graph-based data structure to store knowledge, where the graph can be directed or undirected. It is quite convenient to utilize semantic network to represent and store natural language sentences, which can be further applied in machine translation, question answering and natural language understanding. In 1970s, lots of works began to study the relationship between semantic network and first-order predicate. For example, Simmons et al. [4] presented an algorithm to convert semantic network to predicate logic, while Schubert [5] proposed a method leveraging semantic network to represent the conjunctions and quantifiers in first-order predicate logic. In 1980s, the mainstream of artificial intelligence research had become knowledge engineering and expert system, especially rule-based expert system. During this period, the theory of semantic network became more mature and lots of research worked on reasoning based on semantic network [6]. More importantly, the research on semantic network started to turn to knowledge representation and reasoning with strict logical semantics. From late 1980s to 1990s, the research on semantic network focused on modeling the relationship between concepts. Based on this, terminology logic and description logic were proposed. The representative wor...

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“…For example, propose a dynamic adaptive boosting model to learn taxonomic prediction functions for English and Chinese. Xu et al (2016b) link Chinese entities with DBpedia types based on cross-lingual links between Chinese and English entities. Other approaches can be found in Wu et al (2016); Mahdisoltani et al (2015).…”

Section: Is-a Relation Extractionmentioning

confidence: 99%

Learning Fine-grained Relations from Chinese User Generated Categories

Wang

Fan

et al. 2017

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

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User generated categories (UGCs) are short texts that reflect how people describe and organize entities, expressing rich semantic relations implicitly. While most methods on UGC relation extraction are based on pattern matching in English circumstances, learning relations from Chinese UGCs poses different challenges due to the flexibility of expressions. In this paper, we present a weakly supervised learning framework to harvest relations from Chinese UGCs. We identify is-a relations via word embedding based projection and inference, extract non-taxonomic relations and their category patterns by graph mining. We conduct experiments on Chinese Wikipedia and achieve high accuracy, outperforming state-of-the-art methods.

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Cross-Lingual Type Inference

Cited by 21 publications

References 13 publications

Fine-Grained Entity Typing via Hierarchical Multi Graph Convolutional Networks

Fine-Grained Entity Typing via Hierarchical Multi Graph Convolutional Networks

A Survey of Techniques for Constructing Chinese Knowledge Graphs and Their Applications

Learning Fine-grained Relations from Chinese User Generated Categories

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