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Data Analysis and Knowledge Discovery  2024, Vol. 8 Issue (3): 29-40    DOI: 10.11925/infotech.2096-3467.2022.1226
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Reviewing Research on Semantic Novelty in Sci-Tech Literature
Wu Xinyu1,2,Li Hanyu1,2(),Zhang Zhixiong1,2,Wu Zhenxin1,2
1National Science Library, Chinese Academy of Sciences, Beijing 100190, China
2Department of Information Resources Management, School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, China
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Abstract  

[Objective] This paper reviews the research progress on semantic novelty in China and abroad. It explores relevant techniques and provides references for future studies. [Coverage] We used keywords such as “novelty of the literature”, “semantic novelty”, “literature novelty”, and search expressions like “semantic novelty and literature evaluation” to retrieve literature from Web of Science, Elsevier, Springer, Google Scholar, as well as Chinese databases like CNKI, Wanfang, and VIP. A total of 70 representative literature were selected for review. [Methods] We summarized research on semantic novelty, focusing on the definition of novelty, evaluation indicators, and different evaluation methods. We also discussed the current development status and future trends of evaluating semantic novelty in scientific literature. [Results] Semantic novelty evaluation has gradually received widespread attention from the academic community. Related studies have evaluated semantic content without establishing a unified measurement index. [Limitations] Existing evaluation of literature novelty mainly focuses on external features. Fewer research papers directly addressed semantic novelty, limiting support for reviews. [Conclusions] The evaluation of semantic novelty in scientific literature fundamentally lies in the novelty of semantic content. Quantitative research has become the mainstream method, but the calculation method of evaluation indicators needs to be clarified. Future studies on novelty evaluation should combine qualitative and quantitative methods for more comprehensive evaluations.

Key wordsNovelty Evaluation      Semantic Evaluation      Bibliometrics      Semantic Novelty     
Received: 18 November 2022      Published: 12 April 2024
ZTFLH:  G250  
Fund:National Social Science Fund of China(21&ZD329);National Key R&D Program of China(2022YFF0711900)
Corresponding Authors: Li Hanyu,ORCID:0000-0003-1426-3242,E-mail:lihy@mail.las.ac.cn。   

Cite this article:

Wu Xinyu, Li Hanyu, Zhang Zhixiong, Wu Zhenxin. Reviewing Research on Semantic Novelty in Sci-Tech Literature. Data Analysis and Knowledge Discovery, 2024, 8(3): 29-40.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2022.1226     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2024/V8/I3/29

作者 新颖性概念阐述
张京辉等[12] 新颖性指在国内外的研究中未曾出现过,或者已有类似研究,但因研究进展变化需要进行补充、修订和完善
陈永胜等[13] 科技成果的新颖性主要取决于公开发表的时间先后顺序
徐华[14] 科技文献的新颖性主要体现在对新的概念、数据、假设、定理等进行增删、修改、补充等,或对已有的应用范围和方法对策进行修正、改进,或提出新的思想和新发明
魏绪秋等[15] 科技文献的“新”在于是否提出新理论与新观念、新方法技巧和已有问题的新解释与结果
Mishra等[16] 科技文献的新颖性体现在引入新的观点,包括从未出现过的新观点和增加已有的确定观点
Heinze等[17] 具有新颖性的科技文献应具有革命性的新理论、发现新现象、创建新方法及从新角度整合的研究成果
Kaplan等[18] 从知识组合的角度出发,新颖性是由先前存在的知识的不寻常组合发展而来
The Concept of Novelty
维度 创新性 新颖性 语义新颖性
概念 提出新观点、理论、技术,或对已有理论和方法进行完善,改进或创造新事物,并能产生一定影响力或有益成果 率先提出新理论、方法;或从知识组合的角度,指对先前存在知识的新角度整合 提出不同于以往的研究语义内容;或对已有研究中的语义内容进行改进或完善
特点 兼具有用性、新颖性、价值性与变革性,重点在于带来积极的社会变化或创造价值 侧重于时间维度上提出的“首次”与“不同”,不考虑价值或后续的影响 侧重于科学研究中语义内容的“第一性”,一般具有实际的研究价值
主体 一般为学者、期刊或高校、研究院所等科研机构通过创新活动产生的创新成果 一般为科技文献或其他文本 一般为科技文献中的语义内容,包括研究问题、研究方法等语步
评价维度 新颖性维度、研究价值维度、影响力维度 时间维度、引文维度、内容维度 时间维度、语义内容维度
评价方式 多使用颠覆性指数、被引频次等指标衡量影响力,同时结合文本新颖性进行评价 多使用基于引文分析、基于内容分析方法进行文献外部或内部特征的新颖性计算 多使用相似度计算等指标衡量文章语义内容的新颖性程度
Analysis of Innovation, Novelty and Semantic Novelty
Flow Diagram of Semantic Novelty Evaluation of Scientific and Technological Literature
指标类型 优势 劣势
传统科技文献新颖性评价指标 时间维度 时间维度较为直观,易于计算 评价维度单一,无法反映论文对后续科研成果的影响和科学价值
引文维度 较好地评价了学术成果的引文新颖性和后续影响力 过于依赖对文章参考文献的分析,需要较多的不同类型施引文献的数量才能满足计算;新颖性识别缺乏及时性
语义新颖性评价指标 忽略了外部特征的影响,真正深入文章内容层次进行新颖性评价 由于针对内容的文本挖掘方式和新颖性计算方式不同,无法形成统一的度量指标
Novelty Evaluation Indexes of S&T Literature
类型 作者 研究方法
相似度计算方法 逯万辉等[45] 使用Doc2Vec和隐马尔可夫模型,通过构建相似度转移矩阵、计算特征因子计算文档集中各个文档的相似程度,进一步判断文档的新颖性程度
Tsai等[46] 使用对称性度量(余弦相似性和Jaccard相似性)和不对称度量(新词数和重叠数),并基于两种度量方式构建综合新颖性评测框架
王平等[47] 使用Doc2Vec语言模型构建文本向量,并基于神经张量网络模型(Neural Tensor Network, NTN)训练求解,量化评估文章的新颖性
Luo等[48] 使用BERT(Bidirectional Encoder Representations from Transformers)模型训练词向量,通过向量之间的相似性反映语义间的相似性
褚婧丹[49] 构建科技词向量,使用文本语义相关二分类模型判断当前词与候选文献集中的相似程度
陈娜[50] 根据语义层面的相似度,使用链路预测技术,结合知识元的共现次数及未来链接概率综合评价技术文本的新颖性
基于距离的方法 Sendhilkumar等[51] 在隐含狄利克雷分布(Latent Dirichlet Allocation, LDA)主题模型的基础上,使用余弦相似度计算两个文档主题分布之间的差异,以此分析特定科学领域的论文新颖性
秦岩等[52] 将会议论文的新颖性分为吸收新颖性和产出新颖性,使用TF-IDF计算文本概念向量的余弦相似度,再由相似度计算得到会议论文的产出新颖性指标,以此计算会议论文的新颖性程度
Hautamaki等[53] 使用分类器将旧文档划分为K个聚簇,然后衡量目标文档与每个聚簇中心点的位置判断文档类别,最后将该文档与聚簇之间距离最近的点进行相似度计算[54]
Zhang等[55] 使用基于Hellinger距离的索引树聚类方法,计算新闻话题文本的相似度,同时引入时间参数衡量文本的新颖性
The Semantic Novelty of the Vector Space Model Method
类型 作者 研究方法
研究主题共现法 Amplayo等[56] 将神经网络算法模型与主题新颖性探测相结合,通过构建主题共现图的方法,提取研究论文主题背景图的变化特征,使用自编码神经网络进行新颖性识别
丁芳媛[57] 在Amplayo等[56]研究的基础上设计基于主题共现图的论文新颖性评价与推荐算法
任海英等[58] 在文献中抽取主题词并构建领域主题词共现网络,设计新颖组合率、中等组合率和常规组合率三个指标,评估论文主题的新颖组合和常规组合对论文新颖性类型的影响
主题词语计算法 Matsumoto等[59] 使用有序逻辑和普通最小二乘回归模型,提出一种新颖性指标量化焦点论文主题与同领域论文之间的相似程度
许丹等[60] 使用自然语言法,基于共现原则、时间点原则和自然语言词对逆文档频率原则计算文档主题新颖度
杨京等[61] 利用KeyGraph算法提取代表论文研究主题的关键词,与科学研究前沿主题进行计算比较,结合期刊影响因子和Altmetrics两项外在指标进行评价
语义信息抽取法 张吉玉等[62] 将文献按照时间序列构成索引,并投影在问题-方法矩阵中,以此评价研究内容在时间方面的新颖性
钱佳佳等[63] 通过对问题和方法短语进行识别,基于词频原则分别计算短语新颖度和问题-方法组合新颖度,再对其赋予不同的权重进行文章的新颖性计算,证明基于问题和方法短语进行新颖性度量的科学性
王艳艳等[64] 使用LDA主题模型对论文的问题和方法短语进行分类,构建问题-方法矩阵并实现论文新颖性查询
曹树金等[65] 通过语义角色标注法,使用BERT模型识别创新句并匹配到文章中的创新段,实现基于语义信息的完整段落新颖性挖掘
Semantic Novelty of Topic Detection and Tracking Method
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