Research on Quantitative Evaluation of Knowledge Fusion in News Knowledge Graph

doi:10.3778/j.issn.1002-8331.2104-0286

Abstract

Abstract: Most methods to judge the completeness and consistency of knowledge graph are manual methods, which are based on syntax checking and instance checking, or reasoning the logical rules in the knowledge base. But there is no specific method to judge the degree of fusion of knowledge graph. This paper presents a quantitative evaluation method based on power law, and the fusion results of same topic news knowledge graph are verified. It is found that with the increase of keyword node similarity, the degree of integration of nodes increases, the number of nodes after fusion drops sharply, the power law model fits good, and the adjusted coefficient of determination are all greater than 0.98. This test method can provide a new standard for testing the fusion degree of same topic news knowledge graph.

Key words: knowledge graph, knowledge fusion, semantic similarity, power law

摘要： 现阶段检验知识图谱融合程度的方法大多是基于句法检查和实例检验的人工方法，或对知识库中的逻辑规则进行推理，以此判断图谱是否具有完备性和一致性，但缺少对知识融合量化评估的具体方法。提出了一种基于幂律定律的量化评估方法，并用融合后的长江大保护新闻知识图谱节点数进行验证，发现随着节点相似度的升高，当前图谱节点的融合度增加，融合后节点数急剧下降，幂律模型拟合优度良好，调整决定系数均大于0.98。这一检验方法可为新闻知识图谱的融合度检验提供新的标准。

关键词: 知识图谱, 知识融合, 语义相似度, 幂律定律

XIE Tianyang, CHEN Ming, XI Xiaotao. Research on Quantitative Evaluation of Knowledge Fusion in News Knowledge Graph[J]. Computer Engineering and Applications, 2022, 58(21): 294-300.

解天扬, 陈明, 席晓桃. 新闻知识图谱中知识融合量化评估研究[J]. 计算机工程与应用, 2022, 58(21): 294-300.

References

[1] PAULHEIM H，CIMIANO P.Knowledge graph refinement：a survey of approaches and evaluation methods[J].Semantic Web，2017，8（3）：489-508.
[2] CORBY O，ZUCKER C F.The KGRAM abstract machine for knowledge graph querying[C]//IEEE/WIC/ACM International Conference on Web Intelligence & Intelligent Agent Technology，2010：338-341.
[3] ZHEN W，ZHANG J，FENG J，et al.Knowledge graph embedding by translating on hyperplanes[C]//28th AAAI Conference on Artificial Intelligence，2014：1112-1119.
[4] WFABC D，LING M E，PEDL D，et al.Knowledge graph for identifying hazards on construction sites：integrating computer vision with ontology[J].Automation in Construction，2020，119：103310.
[5] ZHAO Y，GUO J，BAO C，et al.Knowledge graph analysis of human health research related to climate change[J].International Journal of Environmental Research and Public Health，2020，17（20）：7395.
[6] XIU X，QIAN Q，WU S.Construction of a digestive system tumor knowledge graph based on Chinese electronic medical records：development and usability study[J].JMIR Medical Informatics，2020，8（10）：e18287.
[7] CHUN S，JUNG J，JIN X，et al.Designing an integrated knowledge graph for smart energy services[J].The Journal of Supercomputing，2020，76：8058-8085.
[8] TAO X H，PHAM T，ZHANG J，et al.Mining health knowledge graph for health risk prediction[J].World Wide Web，2020，23（5）：2341-2362.
[9] LI X，LIU H，ZHAO X，et al.Automatic approach for constructing a knowledge graph of knee osteoarthritis in Chinese[J].Health Information Science and Systems，2020，8（1）：1-8.
[10] 段华，曾庆田，贺国平.面向并行工程的知识库校验模型与方法研究[J].系统工程理论与实践，2004（3）：79-86.
DUAN H，ZENG Q T，HE G P.Knowledge verification model and method for the knowledge base of concurrent engineering[J].Systems Engineering-Theory & Practice，2004（3）：79-86.
[11] ZHAO B，RUBINSTEIN B，GEMMELL J，et al.A Bayesian approach to discovering truth from conflicting sources for data integration[J].Proceedings of the VLDB Endowment，2012，5（6）：550-561.
[12] SEGARCEANU S.Unifying logical and statistical AI with Markov logic[J].Computing Reviews，2019，60（12）：474-474.
[13] MEMORY A，KIMMIG A，BACH S H，et al.Graph summarization in annotated data using probabilistic soft logic[C]//International Workshop on Uncertainty Reasoning for the Semantic Web（URSW），2012：75-86.
[14] 罗燕，赵书良，李晓超，等.基于词频统计的文本关键词提取方法[J].计算机应用，2016，36（3）：718-725.
LUO Y，ZHAO S L，LI X C，et al.Text keyword extraction method based on word frequency statistics[J].Journal of Computer Applications，2016，36（3）：718-725.
[15] LILLEBERG J，YUN Z，ZHANG Y.Support vector machines and Word2vec for text classification with semantic features[C]//IEEE International Conference on Cognitive Informatics & Cognitive Computing，2015：136-140.
[16] GLEICH D F.PageRank beyond the Web[J].SIAM Review，2014，57（3）：321-363.
[17] YU S，SU J，LI P，et al.Towards high performance text mining：a textrank-based method for automatic text summarization[J].International Journal of Grid & High Performance Computing，2016，8（2）：58-75.
[18] 李保国.基于聚类与LDA的新闻评论主题挖掘研究[D].武汉：武汉纺织大学，2016.
LI B G.News review topic mining based on clustering and LDA[D].Wuhan：Wuhan Textile University，2016.
[19] 王源.一种基于Simhash的文本快速去重算法[D].长春：吉林大学，2014.
WANG Y.A fast text elimination algorithm based on simhash[D].Changchun：Jilin University，2014.
[20] PANT A，RAJPUT R S.Linear regression analysis using R for research and development[Z].2019.
[21] CLAUSET A，SHALIZI C R，NEWMAN M.Power-law distributions in empirical data[J].SIAM Review，2009，51（4）：661-703.