Computer Science ›› 2017, Vol. 44 ›› Issue (12): 17-22.doi: 10.11896/j.issn.1002-137X.2017.12.003

Previous Articles     Next Articles

Multi-dimensional Quantitative Evaluation Method of Open Knowledge Base Construction Technology

CHEN Xin-lei, JIA Yan-tao, WANG Yuan-zhuo, JIN Xiao-long and CHENG Xue-qi   

  • Online:2018-12-01 Published:2018-12-01

Abstract: With the coming of the era of network big data,the construction of open knowledge base attracts more and more attention from both academia and industry.In recent years,the applications based on open knowledge base construction technologies are emerging in an endless stream.However,there is no unified and comprehensive multi-dimensional quantitative evaluation method which evaluates the construction technique of the open knowledge base.Based on existing work,the multi-dimensional specification system of open knowledge base construction technique was put forward,which combines three dimensions,i.e.,the accuracy,the construction time,and the scale of a knowledge base.Furthermore,a multi-dimensional quantitative evaluation method was proposed to evaluate a group of open knowledge base construction techniques.Experiments show the rationality and comprehensiveness of the proposed evaluation me-thod,and it can deduce different results to meet the demand of different applications according to the importance of the dimensions.

Key words: Open knowledge base evaluation,Multi-dimension,Quantification

[1] LI G J,CHENG X Q.Research Status and Scientific Thinking of Big Data[J].Bulletin of Chinese Academy of Sciences,2012,27(6):647-657.(in Chinese) 李国杰,程学旗.大数据研究:未来科技及经济社会发展的重大战略领域[J].中国科学院院刊,2012,7(6):647-657.
[2] WU W T,LI H S,WANG H X,et al.Probase:a probabilistic taxonomy for text understanding[C]∥Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data.Scottsdale,2012:481-492.
[3] DONG X,GABRILOVICH E,HEITZ G,et al.Knowledgevault:A web-scale approach to probabilistic knowledge fusion[C]∥Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.New York,2014:601-610.
[4] CARLSON A,BETTERIDGE J,KISIEL B,et al.Toward anArchitecture for Never-Ending Language Learning[C]∥AAAI.Atlanta,2010.
[5] AUER S,BIZER C,KOBILAROV G,et al.Dbpedia:A nucleus for a web of open data[M].The Semantic Web.Berlin Heidelberg:Springer,2007:722-735.
[6] BIEGA J,KUZEY E,SUCHANEK F M.Inside YAGO2s:Atransparent information extraction architecture[C]∥Procee-dings of the 22nd International Conference on World Wide Web.Riode Janeiro,2013:325-328.
[7] WANG Y Z,JIA Y T,LIU D W,et al.Open Web Knowledge Aided Information Search and Data Mining[J].Journal of Computer Research and Development,2015,2(2):456-474.(in Chinese) 王元卓,贾岩涛,刘大伟,等.基于开放网络知识的信息检索与数据挖掘[J].计算机研究与发展,2015,2(2):456-474.
[8] JIA P,LI X B,WANG J X.Comparison of several kinds of typical comprehensive evaluation methods[J].Chinese Journal of Hospital Statistics,2008,5(4):351-353.(in Chinese) 贾品,李晓斌,王金秀.几种典型综合评价方法的比较[J].中国医院统计,2008,15(4):351-353.
[9] Cold Start Knowledge Base Population at TAC 2015 Task Description.[2015-7-14].
[10] LIU X M,DAO K Q.Construction of Evaluation Model for Institutional Repository Basing on Fuzzy Comprehensive Evaluation[J].Information Research,2015(5):22-24,28.(in Chinese) 刘雪梅,刀克群.基于模糊综合评价法的机构知识库评价模型构建[J].情报探索,2015(5):22-24,28.
[11] YAO K.The comprehensive evaluation system for agricultural knowledge base based on multiple dimension QoS[D].Xianyang:Northwest A&F University,2015.(in Chinese) 姚坤.基于多维QoS的农业知识库综合评价系统[D].咸阳:西北农林科技大学,2015.
[12] ADELMAN L,RIEDEL S L.Handbook for evaluating know-ledge-based systems:Conceptual framework and compendium of methods[M].New York:Springer Science & Business Media,2012:317-318.
[13] SUN P S,FAN Z P,CHEN X,et al.Framework and Process for Evaluating the Construction Effectiveness of Knowledge Base[J].Journal of Northeastern University (Natural Science),2010,1(9):1361-1364.(in Chinese) 孙培山,樊治平,陈曦,等.评价知识库构建效果的框架与流程[J].东北大学学报(自然科学版),2010,1(9):1361-1364.
[14] TJONG K,ERIK F,FIEN D.Introduction to the CoNLL-2003 shared task:Language-independent named entity recognition[C]∥Proceedings of the Seventh Conference on Natural Language Learning at HLT-NAACL 2003.Edmonton,2003:142-147.
[15] PRADHAN S,LUO X,RECASENS M,et al.Scoring Corefe-rence Partitions of Predicted Mentions:A Reference Implementation[J].ACL,2014(2):30-35.
[16] TAC 2014 Cold Start.[2014-11-17].
[17] CHEN X L,PANG L,JIA Y T,et al.Word Vector-based Re-cognition for Unstructured Text Domain Concepts[J].Journal of Shanxi University (Natural Science Edition),2015,8(4):553-559.(in Chinese) 陈新蕾,庞琳,贾岩涛,等.基于词向量的开放文本领域概念识别方法[J].山西大学学报(自然科学版),2015,8(4):553-559.
[18] ZHAO Z Y,JIA Y T,WANG Y Z,et al.Link Inference in Large Scale Evolutionable Knowledge Network[J].Journal of Computer Research and Development,2016,3(2):492-502.(in Chinese) 赵泽亚,贾岩涛,王元卓,等.大规模演化知识网络中的关联推理[J].计算机研究与发展,2016,3(2):492-502.

No related articles found!
Full text



No Suggested Reading articles found!