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Data Analysis and Knowledge Discovery  2020, Vol. 4 Issue (9): 15-25    DOI: 10.11925/infotech.2096-3467.2020.0382
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Developments of Named Entity Disambiguation
Wen Pingmei1,Ye Zhiwei1,Ding Wenjian1,Liu Ying2(),Xu Jian1
1School of Information Management, Sun Yat-Sen University, Guangzhou 510006, China
2Sun Yat-Sen University Library, Guangzhou 510275, China
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Abstract  

[Objective] This paper reviews research and resources in the field of named entity disambiguation(NED) with a focus on the NED methods.[Coverage] We retrieved 57 representative papers and electronic resources from CNKI, Wanfang Data Knowledge Service Platform, and EBSCO.[Methods] First, we summarized the NED principles and methods from the perspectives of entity prominence, context similarity, entity relationship, deep learning and special identification resources. Then, we explored useful knowledge bases, open source tools as well as international conferences on NED evaluation.[Results] Traditional and classic methods were easy to use, while the new ones (e.g., deep learning) significantly improved the results of NED. Effective models often integrated various methods to yield the optimal results.[Limitations] There are subjectivity factors in comparing different methods from the literature.[Conclusions] The NED methods are still developing and could be further improved by artificial intelligence and domain resources.
Key words： Named Entity Disambiguation      Knowledge Base      Entity Linking      Clustering     
Received: 04 May 2020      Published: 14 October 2020
ZTFLH:  TP393  
Corresponding Authors: Liu Ying     E-mail: pusly@mail.sysu.edu.cn
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Pingmei Wen
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Cite this article:

Wen Pingmei,Ye Zhiwei,Ding Wenjian,Liu Ying,Xu Jian. Developments of Named Entity Disambiguation. Data Analysis and Knowledge Discovery, 2020, 4(9): 15-25.

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https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2020.0382     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2020/V4/I9/15

Named Entity Disambiguation Results Based on Knowledge Base and Text Similarity
消歧特征 特征来源 消歧方法 消歧思路
实体显著性 字符串、流行度、共性 规则匹配、先验概率 计算实体显著性,将候选实体列表中显著性最高的候选实体作为歧义实体的消歧结果
上下文相似度 整篇文本、部分名词、实体 分类、聚类、基于主题、概率语言模型 比较实体指称上下文和候选实体上下文的相似度,并将相似度最高的候选实体作为消歧结果,比较过程可选择借助外部知识库
实体关联度 文本描述和文本分类信息、实体注释与实体共现和实体分布、实体关联图 聚类、图关系推理 同一文本中共现的实体往往属于同一个主题或具有某种相关性,利用同一文本中实体之间的语义联系进行协同消歧
深度学习 词向量 神经网络模型 用具有语义特征的分布式向量代表消歧任务中的指称、文本和实体,并将该向量用于深度学习模型中,通过向量相似度完成消歧
特殊标识资源 IPC、ORCID 特殊标识、唯一标识符 通过领域内通用标识资源,降低消歧难度
Comparative Analysis Based on Different Disambiguation Characteristics
Entity Page
Disambiguation Page
知识库 研发机构 实体数量 知识源 介绍
DBpedia[35] 德国莱比锡大学与曼海姆大学 458万 Wikipedia 大规模跨语言的知识库,支持多达125种语言,包括人物、地点、唱片、游戏、组织、疾病、物种等领域
Freebase[36] MetaWeb公司,后被谷歌收购 6 800万 Wikipedia、
IMDB、Flickr		 大规模开放结构数据库,包含三层结构：Domain、Type、Topic
YAGO[37] 德国马普研究所 1 000万 Wikipedia、WordNet、GeoNames 大规模跨语言的语义知识库,包含人物、组织、城市等领域
WordNet[39] 普林斯顿大学 15万 专家人工构建 人工编辑,英文词典,按词义组织,名词同义词集合及上下位为消歧提供帮助
Examples of General Knowledge Bases
Web Interface of Text Analytics
服务 研发机构 是否开源 链接源 说明
Text Analytics[40] 微软 提供API Wikipedia 该API提供情感分析、关键词提取、命名实体识别等功能
AGDISTIS[41,48] 莱比锡大学 开源 DBpedia 开源实体链接框架
TAGME[43] 比萨大学 开源API Wikipedia 开源实体链接和注释工具
FEL[44] 雅虎 开源工具包 Wikipedia 多语言轻量级实体链接工具包
Examples of Entity Linking Service
测评会议 测评语言 消歧要求 链接源 说明
TAC KBP[49,50] 英文 链接并聚类 Wikipedia抽取的知识库 官网提供开放训练语料集
WePS[51,52,53] 英文 聚类 无 无命名实体知识库
CLP-2012[55,56] 中文 链接并聚类 知识库 链接到知识库中的定义
NLP&CC[57] 中文 链接 百科知识库 实现短文本实体的链接
International Conference on Named Entity Disambiguation
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