Potential Relationship Extension Based on EA-LDA Algorithm for Domain-Specific Knowledge Graph

doi:10.19678/j.issn.1000-3428.0058349

Abstract

Abstract: In the Internet that produces massive data, knowledge is mutually related.A rough domain-specific knowledge graph can display the structured information of the knowledge in this domain, but usually fails to present the potential relationships between entities.To implement smooth extension of relationships between entitites in domain-specific knowledge graphs, a relationship discovery method based on inter-entity association rule analysis and topic analysis is proposed.By utilizing the entity-related data in a specific domain, the potential relationships between domain-specific entities are obtained by analyzing the association rules and the similarity of topic distribution among entity-related data sets.Then the newly discovered relationships are merged into the roughly constructed knowledge graphs to realize the potential relationship extension of the domain-specific knowledge graphs.The experimental results show that the proposed method can discover the commonalities between entities of different sectors, and thus mine the potential relationships between these entities.It improves the efficiency of relationship discovery, and smoothes the extension of domain-specific knowledge graphs.

Key words: domain-specific knowledge graph, potential relationship extension, correlation analysis, topic analysis, knowledge mining

摘要： 在知识互联的大数据环境下，初步构建的领域知识图谱可展示该领域知识的结构化信息，但实体之间隐含的潜在关系并未在图谱中得到充分表达。为解决领域知识图谱实体关系丰富和扩展问题，提出一种基于实体间关联规则分析与主题分析的关系发现方法。应用与领域实体相关的数据，通过实体间关联规则分析与实体相关数据集间主题分布相似度分析获取领域实体间潜在关系，将新发现的关系融合到初步构建的知识图谱中，实现领域知识图谱的潜在关系扩展。实验结果表明，该方法能够发现部门实体间的共性，挖掘出隐藏在领域实体间的关系，可有效地应用于领域实体间关系发现，丰富领域知识图谱。

关键词: 领域知识图谱, 潜在关系扩展, 关联分析, 主题分析, 知识挖掘

CLC Number:

TP391.1

LIU Xin, BAI Tingting, ZHANG Yushu, QIAN Gennan, HE Xuli, XI Yongke. Potential Relationship Extension Based on EA-LDA Algorithm for Domain-Specific Knowledge Graph[J]. Computer Engineering, 2021, 47(10): 89-96,102.

刘昕, 白婷婷, 张淯舒, 钱茛南, 何旭莉, 席永轲. 基于EA-LDA算法的领域知识图谱潜在关系扩展[J]. 计算机工程, 2021, 47(10): 89-96,102.

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References

[1] TORRA V, KARLSSON A, STEINHAUER H J.Artificial intelligence[M].Berlin, Germany:Springer, 2019.
[2] 廖建新.大数据技术的应用现状与展望[J].电信科学, 2015, 31(7):7-18. LIAO J X.Big data technology:current applications and prospects[J].Telecommunications Science, 2015, 31(7):7-18.(in Chinese)
[3] 刘峤, 李杨, 段宏, 等.知识图谱构建技术综述[J].计算机研究与发展, 2016, 53(3):582-600. LIU Q, LI Y, DUAN H, et al.Knowledge graph construction techniques[J].Journal of Computer Research & Development, 2016, 53(3):582-600.(in Chinese)
[4] 刘知远, 孙茂松, 林衍凯, 等.知识表示学习研究进展[J].计算机研究与发展2016, 53(2):247-261. LIU Z Y, SUN M S, LIN Y K, et al.Knowledge representation learning:a review[J].Journal of Computer Research and Development, 2016, 53(2):247-261.(in Chinese)
[5] NIKOLAEV F, KOYOV A, ZHILTSOV N.Parameterized fielded term dependence models for ad-hoc entity retrieval from knowledge graph[C]//Proceedings of the 39th Annual ACM SIGIR International Conference on Research and Development in Information Retrieval.New York, USA:ACM Press, 2016:356-367.
[6] KALYANPUR A, BOGURAEV B K, PATWARDHAN S, et al.Structured data and inference in DeepQA[J].IBM Journal of Research & Development, 2012, 56(3/4):1-14.
[7] QIAN J, LI X Y, ZHANG C, et al.De-anonymizing social networks and inferring private attributes using knowledge graphs[C]//Proceedings of IEEE International Conference on Computer Communications.Washington D.C., USA:IEEE Press, 2016:125-136.
[8] SZEKELY P, KNOBLOCK C A, SLEPICKA J, et al.Building and using a knowledge graph to combat human trafficking[EB/OL].[2020-04-10].https://www.docin.com/p-1704559880.html.
[9] 王辉, 郁波, 洪宇, 等.基于知识图谱的Web信息抽取系统[J].计算机工程, 2017, 43(6):118-124. WANG H, YU B, HONG Y, et al.Web information extraction system based on knowledge graph[J].Computer Engineering, 2017, 43(6):118-124.(in Chinese)
[10] 杨玉基, 许斌, 胡家威, 等.一种准确而高效的领域知识图谱构建方法[J].软件学报, 2018, 29(10):39-55. YANG Y J, XU B, HU J W, et al.Accurate and efficient method for constructing domain knowledge graph[J].Journal of Software, 2018, 29(10):39-55.(in Chinese)
[11] WANG Q Y, QIN X P, CAO W, et al.Entity relationship search over extended knowledge graph[J].Journal of Computer Applications, 2016, 36(4):985-991.
[12] 蒋婷, 孙建军.领域学术本体概念等级关系抽取研究[J].情报学报, 2017, 36(10):1080-1092. JIANG T, SUN J J.Learning concept hierarchies from scientific articles for ontology construction[J].Journal of the China Society for Scientific and Technical Information, 2017, 36(10):1080-1092.(in Chinese)
[13] GRAINGER T, ALJADDA K, KORAYEM M, et al.The semantic knowledge graph:a compact, auto-generated model for real-time traversal and ranking of any relationship within a domain[EB/OL].[2020-04-10].https://arxiv.org/abs/1609.00464.
[14] 张宁豫, 陈曦, 陈矫彦, 等.基于位置的知识图谱链接预测[J].中文信息学报, 2018, 32(4):80-86, 129. ZHANG N Y, CHEN X, CHEN J Y, et al.Location based link prediction for knowledge graph[J].Journal of Chinese Information Processing, 2018, 32(4):80-86, 129.(in Chinese)
[15] QIAN J, LI X Y, ZHANG C, et al.Social network de-anonymization and privacy inference with knowledge graph mode[EB/OL].[2020-04-10].https://arxiv.org/abs/1710.10998.
[16] 方阳, 赵翔, 谭真, 等.一种改进的基于翻译的知识图谱表示方法[J].计算机研究与发展, 2018, 55(1):139-150. FANG Y, ZHAO X, TAN Z, et al.A revised translation-based method for knowledge graph representation[J].Journal of Computer Research and Development, 2018, 55(1):139-150.(in Chinese)
[17] ZHU Y R, WANG P, FAN Y Q, et al.Research of learning path recommendation algorithm based on Knowledge Graph[C]//Proceedings of the 6th ACM International Conference.New York, USA:ACM Press, 2017:343-356.
[18] 赵泽亚, 贾岩涛, 王元卓, 等.基于动态异构信息网络的时序关系预测[J].计算机研究与发展, 2015, 52(8):1735-1741. ZHAO Z Y, JIA Y T, WANG Y Z, et al.Temporal link prediction based on dynamic heterogeneous information network[J].Journal of Computer Research and Development, 2015, 52(8):1735-1741.(in Chinese)
[19] BORDES A, USUNIER N, GARCIADURAN A, et al.Translating embeddings for modeling multi-relational data[C]//Proceedings of IEEE International Conference on Neural Information Processing Systems.Washington D.C., USA:IEEE Press, 2013:259-268.
[20] 王子涵, 邵明光, 刘国军, 等.基于实体相似度信息的知识图谱补全算法[J].计算机应用, 2018, 38(11):43-47. WANG Z H, SHAO M G, LIU G J, et al.Knowledge graph completion algorithm based on similarity between entities[J].Journal of Computer Applications, 2018, 38(11):3089-3093.(in Chinese)
[21] WANG Q, WANG B.Knowledge base completion using embeddings and rules[C]//Proceedings of the 24th International Joint Conference on Artificial Intelligence.Palo Alto, USA:AAAI Press, 2015:1859-1865.
[22] GUU K, MILLER J, LIANG P.Traversing knowledge graphs in vector space[C]//Proceedings of Conference on Empirical Methods in Natural Language Processing.Stroudsburg, USA:ACL, 2015:318-327.
[23] WANG Q, WANG B, GUO L.Knowledge base completion using embeddings and rules[C]//Proceedings of IEEE International Joint Conference on Artificial Intelligence.Washington D.C., USA:IEEE Press, 2015:1859-1866.
[24] 夏维, 王珊蕾, 尹子都, 等.基于互信息的知识图谱实体关联关系建模与补全[J].计算机科学与探索, 2018, 12(7):49-59. XIA W, WANG S L, YIN Z D, et al.Mutual information based modeling and completion of correlations in knowledge graphs[J].Journal of Frontiers of Computer Science & Technology, 2018, 12(7):49-59.(in Chinese)
[25] GALÁRRAGA L, TEFLIOUDI C, HOSE K, et al.Fast rule mining in ontological knowledge bases with AMIE+[J].The International Journal on Very Large Databases, 2015, 24(6):707-730.
[26] 黄风华, 晏路明.基于Jena的台风灾害领域本体模型推理[J].计算机应用, 2013, 33(3):771-775. HUANG F H, YAN L M.Reasoning of ontology model for typhoon disasters domain based on Jena[J].Journal of Computer Applications, 2013, 33(3):771-775.(in Chinese)

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