CN-DBpedia: A Never-Ending Chinese Knowledge Extraction System

Xu, Bo; Yang, Xu; Liang, Jiaqing; Xie, Chenhao; Liang, Bin; Cui, Wanyun; Xiao, Yanghua

doi:10.1007/978-3-319-60045-1_44

Cited by 179 publications

(80 citation statements)

References 7 publications

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“…Knowledge Retrieval Knowledge Retrieval is responsible for retrieving relevant knowledge for a product from the knowledge base W. Specifically, we obtain the relevant knowledge for our input products from CN-DBpedia [41], which is a large-scale structural knowledge graph in Chinese. The dataset that we construct is in Chinese to be introduced in detail in Section 4.1, and CN-DBpedia perfectly matches the requirements of our purpose.…”

Section: Knowledge Incorporationmentioning

confidence: 99%

“…For example, the user category attribute specifies the category of interest for potential readers. We also retrieve the relevant knowledge about the products from a large-scale knowledge base in Chinese, CN-DBpedia [41]. Models trained on the dataset are able to learn how to generate descriptions based on titles, attributes and relevant knowledge.…”

Section: Introductionmentioning

confidence: 99%

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Towards Knowledge-Based Personalized Product Description Generation in E-commerce

Chen

Lin

Zhang

et al. 2019

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

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Quality product descriptions are critical for providing competitive customer experience in an e-commerce platform. An accurate and attractive description not only helps customers make an informed decision but also improves the likelihood of purchase. However, crafting a successful product description is tedious and highly time-consuming. Due to its importance, automating the product description generation has attracted considerable interest from both research and industrial communities. Existing methods mainly use templates or statistical methods, and their performance could be rather limited. In this paper, we explore a new way to generate personalized product descriptions by combining the power of neural networks and knowledge base. Specifically, we propose a KnOwledge Based pErsonalized (or KOBE) product description generation model in the context of e-commerce. In KOBE, we extend the encoder-decoder framework, the Transformer, to a sequence modeling formulation using self-attention. In order to make the description both informative and personalized, KOBE considers a variety of important factors during text generation, including product aspects, user categories, and knowledge base. Experiments on real-world datasets demonstrate that the proposed method outperforms the baseline on various metrics. 1 KOBE can achieve an improvement of 9.7% over state-of-the-arts in terms of BLEU. We also present several case studies as the anecdotal evidence to further prove the effectiveness of the proposed approach. The framework has been deployed in Taobao, 2 the largest online e-commerce platform in China.

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Section: Knowledge Incorporationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Towards Knowledge-Based Personalized Product Description Generation in E-commerce

Chen

Lin

Zhang

et al. 2019

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

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“…Lack of real world practical Chinese knowledge graphs hinders the development of semantic applications, artificial intelligence and semantic Web itself in Chinese. This is why Chinese researchers should make great efforts to construct Chinese knowledge graphs, and the good news is that they have achieved very encouraging progress, such as the large-scale Chinese encyclopedic knowledge graphs Zhishi.me [10], CN-DBpedia [11], XLORE [12,13], PKU-PIE (http://openkg.cn/dataset/pku-pie), Belief-Engine (http://www.belief-engine.org) and the Chinese schema-level knowledge graphs cnSchema (http://cnschema.org) and Linked Open Schema [14][15][16].…”

Section: Typical Chinese Knowledge Graphsmentioning

confidence: 99%

“…Xu et al [11] presented a hybrid Long Short-Term Memory Recurrent Neural Network framework to extract knowledge from free text and this approach is used in CN-DBpedia for infobox completion. CN-DBpedia treats the infobox completion task as extracting the object for a given pair of subject (i.e., entity) and predicate (i.e., property) from the article corresponding to the given entity.…”

Section: Infobox Completionmentioning

confidence: 99%

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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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BERTBooster: A knowledge enhancement method jointing incremental training and gradient optimization

Jiang

Liang

et al. 2022

Int J of Intelligent Sys

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The knowledge-enhanced BERT model solves the problem of lacking knowledge in downstream tasks by injecting external expertize, and achieves higher accuracy compared with BERT model. However, owning to large-scale external knowledge is utilized into knowledge-enhanced BERT, some shortcomings comes such as information noise, lower accuracy and weak generalization ability, and so on. To solve this problem, a knowledge enhancement method BERTBooster which combines incremental learning and gradient optimization is proposed. BERTBooster disassembles the input text corpus into entity noun sets through entity noun recognition, and uses the incremental learning task denoising entity auto-encoder to create an incremental task set of entity nouns and external knowledge triples. Furthermore, BERTBooster introduces a new gradient optimization algorithm ChildTuningF into BERT model to improve the generalization ability. BERTBooster can effectively improve the factual knowledge cognition ability of CAGBERT model and improve the accuracy of the model in downstream tasks. Experiments are carried out on six public data sets such as Book_Review, LCQMC, XNLI, Law_QA, Insureace_QA, and NLPCC-DBQA. The experimental results show that the accuracy

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CN-DBpedia: A Never-Ending Chinese Knowledge Extraction System

Cited by 179 publications

References 7 publications

Towards Knowledge-Based Personalized Product Description Generation in E-commerce

Towards Knowledge-Based Personalized Product Description Generation in E-commerce

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

BERTBooster: A knowledge enhancement method jointing incremental training and gradient optimization

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