ZORE: A Syntax-based System for Chinese Open Relation Extraction

Qiu, Likun; Zhang, Yue

doi:10.3115/v1/d14-1201

Cited by 44 publications

(28 citation statements)

References 17 publications

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“…One problem here is that few works use the techniques of natural language processing based on Chinese linguistic characteristics to mine knowledge from text in real-word Chinese knowledge graph construction, but text is one of the most important mining sources for knowledge graph construction and should not be neglected. Thus, how to apply the techniques of Chinese open information extraction [31][32][33] from text to real-world Chinese knowledge graph construction worth further study. The rapid development of Chinese knowledge graphs and their techniques promote the development of related Chinese applications, including semantic search, QA, intelligence analysis and so on.…”

Section: Discussionmentioning

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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Section: Discussionmentioning

confidence: 99%

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

et al. 2018

Sustainability

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“…In order to reduce the errors introduced by syntactic or semantic analysis, Chen et al [10] proposed a model for Chinese relation extraction based on a deep belief network. A Chinese ORE system was also developed by Qiu et al [45], which filters relation candidates with a double propagation algorithm.…”

Section: Related Workmentioning

confidence: 99%

Feature assembly method for extracting relations in Chinese

Chen

2015

Artificial Intelligence

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Please cite this article in press as: Y. Chen et al., Feature assembly method for extracting relations in Chinese, Artificial Intelligence (2015), http://dx. AbstractThe goal of relation extraction is to detect relations between two entities in free text. In a sentence, a relation instance usually comprises a small number of words, which yields a sparse feature representation. To make better use of limited information in a relation instance, parsing trees and combined features are employed widely to capture the local dependencies of relation instances. However, the performance of parsing tree-based systems is often degraded by chunking or parsing errors. Combined features are used widely, but few studies have addressed how features can be combined to achieve optimal performance. Thus, in this study, we propose a feature assembly method for relation extraction. Six types of candidate features (head noun, POS tag, n-gram, omni-word, etc.) are employed as atomic features and six constraint conditions (singleton, position, syntax, etc.) are used to combine these features in different settings. Depending on the utilization of candidate features, different constraint conditions can be explored to achieve the optimal extraction performance. Our method is effective for capturing local dependencies and it reduces the errors caused by inaccurate parsing. We tested the proposed method using the ACE 2005 Chinese and English corpora, and it achieved state-of-the-art performance, where it was significantly superior to existing methods.

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“…ZORE [21] is a syntax-based Chinese open relation extraction system for extracting relations and semantic patterns from Chinese text. ZORE identifies relation candidates from automatically parsed dependency trees, and then extracts relations with their semantic patterns iteratively through a double propagation algorithm.…”

Section: Related Workmentioning

confidence: 99%

“…Other approaches such as Wanderlust [19], WOE parse [15], KrakeN [20], OLLIE [11], ZORE [21], DepOE [22], SRL-IE-Lund [23], SRL-IE-UIUC [23], the methods proposed in [24] and [25] additionally use deep syntactic and semantic analysis such as dependency parsing. These extractors are generally more expensive than the previous extractors; they trade efficiency for improved precision and recall.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Method for Open Information Extraction Based on Shallow and Deep Linguistic Analysis

Reshadat

Hoorali

Faili

2016

IIS

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Open Information Extraction is a relation-independent extraction paradigm that extracts assertions from massive and heterogeneous corpora such as the Web. Light relation extractors focus on efficiency by restricting analysis to some shallow linguistic tools such as part-of-speech tagging. Although these methods are fast and scalable, they are unable to deal with complex sentences (such as complicated and long distance relations) due to using only shallow syntactic features. This paper presents two novel hybrid methods, TextRunner-DepOE (TR-DOE) and ReVerb-DepOE (RV-DOE) which combine high-performance subset of shallow Open IE systems with the strengths of a deep Open IE system. We detect the best trade-off between precision and recall by tuning two combination parameters: sentence length and confidence measure. Since the focus is on using time efficiently, we used a fast and robust deep extractor. Experiments indicate that the proposed hybrid methods obtain significantly higher performance than their constituent systems. The best result was for TR-DOE which had an F-measure almost twice that of TextRunner.

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ZORE: A Syntax-based System for Chinese Open Relation Extraction

Cited by 44 publications

References 17 publications

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

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

Feature assembly method for extracting relations in Chinese

A Hybrid Method for Open Information Extraction Based on Shallow and Deep Linguistic Analysis

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