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Neural symbolic reasoning with knowledge graphs:knowledge extraction,relational reasoning,and inconsistency checking

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Fundamental Research 2021年第5期1卷 565-573页

作者： Huajun Chen Shumin Deng Wen Zhang Zezhong Xu Juan Li Evgeny Kharlamov College of Computer Science Zhejiang UniversityHangzhou310027China Hangzhou Innovation Center Zhejiang UniversityHangzhou310000China AZFT Joint Lab for knowledge Engine Hangzhou311121China Bosch Center for Artificial Intelligence Robert Bosch GmbHRenningen70465 StuttgartGermany SIRIUS Research Center University of OsloOsloN-0316Norway

knowledge graphs(KGs)express relationships between entity pairs,and many real-life problems can be formulated as knowledge graph reasoning(KGR).Conventional approaches to KGR have achieved promising performance but still have some drawbacks.On the one hand,most KGR methods focus only on one phase of the KG lifecycle,such as KG completion or refinement,while ignoring reasoning over other stages,such as KG extraction.On the other hand,traditional KGR methods,broadly categorized as symbolic and neural,are unable to balance both scalability and interpretability.To resolve these two problems,we take a more comprehensive perspective of KGR with regard to the whole KG lifecycle,including KG extraction,completion,and refinement,which correspond to three subtasks:knowledge extraction,relational reasoning,and inconsistency checking.In addition,we propose the implementation of KGR using a novel neural symbolic framework,with regard to both scalability and interpretability.Experimental results demonstrate that our proposed methods outperform traditional neural symbolic models.

关键词： Neural symbolic reasoning knowledge graph knowledge extraction Relational reasoning Inconsistency checking

knowledge Mining:A Cross-disciplinary Survey

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Machine Intelligence Research 2022年第2期19卷 89-114页

作者： Yong Rui Vicente Ivan Sanchez Carmona Mohsen Pourvali Yun Xing Wei-Wen Yi Hui-Bin Ruan Yu Zhang Lenovo Research Beijing 100094China

knowledge mining is a widely active research area across disciplines such as natural language processing(NLP), data mining(DM), and machine learning(ML). The overall objective of extracting knowledge from data source is to create a structured representation that allows researchers to better understand such data and operate upon it to build applications. Each mentioned discipline has come up with an ample body of research, proposing different methods that can be applied to different data types. A significant number of surveys have been carried out to summarize research works in each discipline. However, no survey has presented a cross-disciplinary review where traits from different fields were exposed to further stimulate research ideas and to try to build bridges among these fields.In this work, we present such a survey.

关键词： knowledge mining knowledge extraction information extraction association rule interpretability

Modified 2 Satisfiability Reverse Analysis Method via Logical Permutation Operator

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Computers, Materials & Continua 2023年第2期74卷 2853-2870页

作者： Siti Zulaikha Mohd Jamaludin MohdAsyraf Mansor Aslina Baharum Mohd Shareduwan Mohd Kasihmuddin Habibah A.Wahab Muhammad Fadhil Marsani School of Mathematical Sciences Universiti Sains MalaysiaMindenPenang11800Malaysia School of Distance Education Universiti Sains MalaysiaMindenPenang11800Malaysia Faculty of Computing and Informatics Universiti Malaysia SabahJalan UMSKota Kinabalu88450SabahMalaysia School of Pharmaceutical Sciences Universiti Sains MalaysiaMindenPenang11800Malaysia

The effectiveness of the logic mining approach is strongly correlated to the quality of the induced logical representation that represent the behaviour of the data.Specifically,the optimum induced logical representation indicates the capability of the logic mining approach in generalizing the real datasets of different variants and dimensions.The main issues with the logic extracted by the standard logic mining techniques are lack of interpretability and the weakness in terms of the structural and arrangement of the 2 Satisfiability logic causing lower accuracy.To address the issues,the logical permutation serves as an alternative mechanism that can enhance the probability of the 2 Satisfiability logical rule becoming true by utilizing the definitive finite arrangement of attributes.This work aims to examine and analyze the significant effect of logical permutation on the performance of data extraction ability of the logic mining approach incorporated with the recurrent discrete Hopfield Neural Network.Based on the theory,the effect of permutation and associate memories in recurrent Hopfield Neural Network will potentially improve the accuracy of the existing logic mining approach.To validate the impact of the logical permutation on the retrieval phase of the logic mining model,the proposed work is experimentally tested on a different class of the benchmark real datasets ranging from the multivariate and timeseries datasets.The experimental results show the significant improvement in the proposed logical permutation-based logic mining according to the domains such as compatibility,accuracy,and competitiveness as opposed to the plethora of standard 2 Satisfiability Reverse Analysis methods.

关键词： Logic mining logical permutation discrete hopfield neural network knowledge extraction

Microsoft Concept Graph:Mining Semantic Concepts for Short Text Understanding

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Data Intelligence 2019年第3期1卷 238-270页

作者： Lei Ji Yujing Wang Botian Shi Dawei Zhang Zhongyuan Wang Jun Yan Microsoft Research Asia Haidian DistrictBeijing 100080China Institute of Computing Technology Chinese Academy of SciencesHaidian DistrictBeijing 100049China Beijing Institute of Technology Haidian DistrictBeijing 100081China MIX Labs Haidian DistrictBeijing 100080China Meituan NLP Center Chaoyang DistrictBeijing 100020China AI Lab of Yiducloud Inc. Huayuan North RoadHaidian DistrictBeijing 100089China

Knowlege is important for text-related applications.In this paper,we introduce Microsoft Concept Graph,a knowledge graph engine that provides concept tagging APIs to facilitate the understanding of human languages.Microsoft Concept Graph is built upon Probase,a universal probabilistic taxonomy consisting of instances and concepts mined from the Web.We start by introducing the construction of the knowledge graph through iterative semantic extraction and taxonomy construction procedures,which extract 2.7 million concepts from 1.68 billion Web pages.We then use conceptualization models to represent text in the concept space to empower text-related applications,such as topic search,query recommendation,Web table understanding and Ads relevance.Since the release in 2016,Microsoft Concept Graph has received more than 100,000 pageviews,2 million API calls and 3,000 registered downloads from 50,000 visitors over 64 countries.

关键词： knowledge extraction Conceptualization Text understanding

Multi-agents modelling of EV purchase willingness based on questionnaires

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Journal of Modern Power Systems and Clean Energy 2015年第2期3卷 149-159页

作者： Yusheng XUE Juai WU Dongliang XIE Kang LI Yu ZHANG Fushuan WEN Bin CAI Qiuwei WU Guangya YANG State Grid Electric Power Research Institute(SGEPRI) Nanjing 210003China Nanjing University of Science and Technology(NJUST) Nanjing 210094China Queen’s University BelfastNorthern IrelandUK Y.ZHANGState Grid Shanghai Municipal Electric Power CompanyShanghai 200122China Zhejiang University Hangzhou 310027China Technical University of Denmark 2800 LyngbyDenmark

Traditional experimental economics methods often consume enormous resources of qualified human participants,and the inconsistence of a participant’s decisions among repeated trials prevents investigation from sensitivity analyses.The problem can be solved if computer agents are capable of generating similar behaviors as the given participants in experiments.An experimental economics based analysis method is presented to extract deep information from questionnaire data and emulate any number of participants.Taking the customers’willingness to purchase electric vehicles(EVs)as an example,multi-layer correlation information is extracted from a limited number of questionnaires.Multiagents mimicking the inquired potential customers are modelled through matching the probabilistic distributions of their willingness embedded in the questionnaires.The authenticity of both the model and the algorithmis validated by comparing the agent-based Monte Carlo simulation results with the questionnaire-based deduction results.With the aid of agent models,the effects of minority agents with specific preferences on the results are also discussed.

关键词： Behavioral analysis Experimental economics Human experimenters knowledge extraction Multi-agents EV purchase

基于知识元的万里茶道数字资源知识抽取与组织研究

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科学文献知识单元抽取及应用研究：梳理与展望

图书馆学情报学 2022年第2期

作者：曾刚赵雪芹

[目的/意义]基于知识元研究视角探索知识抽取与组织方法，提高万里茶道数字资源开发利用效率。[方法/过程]构建知识元语义描述模型，基于该模型实现万里茶道知识抽取，并利用深度学习实现对知识语义层面的细粒度组织。[结果/结论]实现... 详细信息

[目的/意义]基于知识元研究视角探索知识抽取与组织方法，提高万里茶道数字资源开发利用效率。[方法/过程]构建知识元语义描述模型，基于该模型实现万里茶道知识抽取，并利用深度学习实现对知识语义层面的细粒度组织。[结果/结论]实现对细粒度、多维关联知识的揭示与展示，推动万里茶道历史人文资源的活化和保护。

关键词：深度学习万里茶道数字资源知识元知识抽取 deep learning Wanli tea ceremony digital resources knowledge element knowledge extraction

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Intelligent Technology for Cigarette Recipe Design

图书馆学情报学 2023年第4期

作者：沈雪莹欧石燕

[目的/意义]知识单元作为人类控制和管理知识的基本单元，是科学文献内容的基本组成成分。通过对知识单元抽取技术和应用研究进行梳理与总结，探索知识单元相关研究未来发展方向，以便更好地实现对科学知识的发掘和利用。[方法/过程]采... 详细信息

[目的/意义]知识单元作为人类控制和管理知识的基本单元，是科学文献内容的基本组成成分。通过对知识单元抽取技术和应用研究进行梳理与总结，探索知识单元相关研究未来发展方向，以便更好地实现对科学知识的发掘和利用。[方法/过程]采用文献调研法，通过人工对相关文献进行阅读和分析，从知识单元抽取和应用两个视角对科学文献中知识单元相关研究进行梳理和总结。[结果/结论]目前，知识单元抽取的准确性和智能性还有待于进一步提高，未来研究可关注新兴深度学习技术在知识挖掘中的应用；当前知识单元的应用主要包括科学知识图谱构建、学术评价和知识演化分析，未来研究可进一步探索知识单元更多的应用场景，包括基于引文内容的知识单元评价、基于知识图谱的学术文献创新性评价和基于知识单元的跨学科知识流动分析等。

关键词：知识抽取知识单元知识实体科学文献 knowledge extraction knowledge unit knowledge entity Scientific literature

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Intelligent Technology for Cigarette Recipe Design

Computational Intelligence Methods for Information Understanding and Information Management

首届中国食品感官科学学术研讨会暨《食品感官科学》课程建...

作者：丁香乾 Information Engineering Center of Ocean University of China

A novel system based on intelligent technology for improvement of recipe-product design with sensory evaluation is proposed in this paper,taking cigarette recipe design for example.Analyses and tests have shown that the proposed intelligent system in combination with expert knowledge,genetic algorithms,neural networks,support vector machine and fuzzy set method are meaningful for cigarette quality management,recipe maintaining and new product designing.It involves knowledge extraction from ncuro-fuzzy models,mapping from tobacco chemical indexes to sensory-quality indexes,classification of tobaccos,analysis of the correlation between chemical ingredients and sensory-quality indexes,and recipe management and design of cigarettes.

关键词： recipe-product design sensory evaluation cigarette-recipe management computational intelligence knowledge extraction

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Computational Intelligence Methods for Information Understan...