Venue-Influence Language Models for Expert Finding in Bibliometric Networks

Abdullah Al-Barakati; Ali Daud

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Venue-Influence Language Models for Expert Finding in Bibliometric Networks

机译：在伯格计量网络中专家查找的场地影响语言模型

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This article investigates the fundamental problem of traditional language models used for expert finding in bibliometric networks. It introduces novel Venue-Influence Language Modeling methods based on entropy, which can accommodate citation links based weights in an indirect way without using links information. Intuitively, an author publishing in topic-specific venues, either journals or for conferences, will be an expert on a topic as compared to an author publishing in multi-topic venues. The proposed methods are evaluated on real world data, the Digital Bibliography and Library Project (DBLP) dataset to test the performance. Experimental results show that their proposed venue influence language models (ViLMs) based methods outperform the traditional (non-venue based) language models (LM).

机译：本文调查了用于专家查找的传统语言模型的基本问题。它介绍了基于熵的新型场地影响语言建模方法，可以在不使用链接信息的情况下以间接方式容纳基于引用的权重的引用链接。直观地，在特定于主题的场地，期刊或会议中，将是一个主题的专家，与作者出版在多主题场地中的一个专题上。所提出的方法在真实世界数据，数字参考书目和库项目（DBLP）数据集上进行评估，以测试性能。实验结果表明，其提出的场地影响语言模型（Vilms）的方法优于传统（基于非场地）语言模型（LM）。

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来源
《International journal on Semantic Web and information systems》 |2018年第3期|共18页
作者
Abdullah Al-Barakati; Ali Daud;
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作者单位

King Abdulaziz University Jeddah Saudi Arabia;

King Abdulaziz University Jeddah Saudi Arabia &

International Islamic University Islamabad Pakistan;

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原文格式 PDF
正文语种 eng
中图分类计算机网络;
关键词
Bibliometric Networks (BNs); Entropy; Expert Finding; Venue Influence Language Models (ViLMs);

机译：Bibliometric Networks（BNS）;熵;专家发现;场地影响语言模型（Vilms）;

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专利

1. Venue-Influence Language Models for Expert Finding in Bibliometric Networks [J] . Abdullah Al-Barakati, Ali Daud International journal on Semantic Web and information systems . 2018,第3期

机译：在伯格计量网络中专家查找的场地影响语言模型
2. Formal language models for finding groups of experts [J] . Shangsong Liang, Maarten de Rijke Information Processing & Management . 2016,第4期

机译：寻找专家组的正式语言模型
3. Integrating multiple document features in language models for expert finding [J] . Jianhan Zhu, Xiangji Huang, Dawei Song, Knowledge and Information Systems . 2010,第1期

机译：在语言模型中集成多个文档功能，以供专家查找
4. Publication Venue Based Language Modeling for Expert Finding [C] . Ali Daud, Sabir Hussain 2012 international conference on future communication and computer technology . 2012

机译：基于出版物场地的语言建模，供专家查找
5. Rule-based social networking for expert finding. [D] . Li, Jie. 2007

机译：基于规则的社交网络，供专家查找。
6. Tracking Child Language Development With Neural Network Language Models [O] . Kenji Sagae 2021

机译：用神经网络语言模型跟踪儿童语言开发
7. We develop a model of scientific creativity and test it in the field of rare diseases. Our model is based on the results of an in-depth case study of the Rett syndrome. Archival analysis, bibliometric techniques and expert surveys are combined with network analysis to identify the most creative scientists. First, alternative measures of generative and combinatorial creativity are compared. Then, we generalize our results and present a stochastic model of socio-semantic network evolution. The model predictions are tested with multiple networks of rare disease specialties. We find that new scientific collaborations among experts in a field enhance combinatorial creativity. Instead, high entry rates of novices are negatively related to generative creativity. By extending the set of useful concepts, creative scientists gain in centrality. At the same time, by increasing their centrality in the scientific community, scientists can replicate and generalize their results, thus contributing to a scientific paradigm. [O] . Massimo Riccaboni, Maria Laura Frigotto 100

机译：我们开发了一种科学创造力模型，并在稀有疾病领域进行测试。我们的模型基于对Rett综合征的深入案例研究的结果。档案分析，文献计量技术和专家调查与网络分析相结合，以确定最具创造力的科学家。首先，比较生成和组合创造力的替代措施。然后，我们推广了我们的结果，并提出了社会语义网络演化的随机模型。模型预测用多个罕见疾病专业网络进行测试。我们发现，一个领域的专家之间的新的科学合作增强了组合创造力。相反，新手的高入门率与生成创造力负相关。通过扩展这组有用的概念，创造性的科学家获得了中心地位。同时，通过增加科学界的中心地位，科学家们可以复制和推广他们的结果，从而促进科学范式的发展。

Venue-Influence Language Models for Expert Finding in Bibliometric Networks

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