citation recommendation for research papers via knowledge graphs

• ACRIS instructions

Global citation recommendation using knowledge graphs

• Helsinki Institute for Information Technology (HIIT)
• Department of Computer Science
• Hungarian Academy of Sciences
• Eötvös Loránd University
• University of Lyon

Research output : Contribution to journal › Article › Scientific › peer-review

Scholarly search engines, reference management tools, and academic social networks enable modern researchers to organize their scientific libraries. Moreover, they often provide recommendations for scientific publications that might be of interest to researchers. Because of the exponentially increasing volume of publications, effective citation recommendation is of great importance to researchers, as it reduces the time and effort spent on retrieving, understanding, and selecting research papers. In this context, we address the problem of citation recommendation, i.e., the task of recommending citations for a new paper. Current research investigates this task in different settings, including cases where rich user metadata is available (e.g., user profile, publications, citations). This work focus on a setting where the user provides only the abstract of a new paper as input. Our proposed approach is to expand the semantic features of the given abstract using knowledge graphs - and, combine them with other features (e.g., indegree, recency) to fit a learning to rank model. This model is used to generate the citation recommendations. By evaluating on real data, we show that the expanded semantic features lead to improving the quality of the recommendations measured by nDCG@10.

• Citation recommendations
• knowledge graphs
• recommender systems

Access to Document

• 10.3233/JIFS-169493

OpenUrl availability

Fingerprint.

• User Computer Science 100%
• Models Computer Science 66%
• Semantic Feature Computer Science 66%
• Contexts Computer Science 33%
• Research Paper Computer Science 33%
• Social Networks Computer Science 33%
• References Computer Science 33%
• Metadata Computer Science 33%

Projects per year

Network structure from group response

Xiao, H., Gionis, A. , Garimella, K., Vitale, F., Parotsidis, N., Zhang, G., Rozenshtein, P., Galbrun, E., Tatti, N., Scepanovic, S., Matakos, A. & Muniyappa, S.

01/09/2015 → 31/08/2019

Project : Academy of Finland: Other research funding

• Models 100%
• Algorithms 88%
• Classifier 70%
• Application 70%
• Networks 70%

T1 - Global citation recommendation using knowledge graphs

AU - Ayala-Gomez, Frederick

AU - Daroczy, Balint

AU - Benczur, Andras

AU - Mathioudakis, Michael

AU - Gionis, Aristides

N1 - | openaire: EC/H2020/654024/EU//SoBigData

N2 - Scholarly search engines, reference management tools, and academic social networks enable modern researchers to organize their scientific libraries. Moreover, they often provide recommendations for scientific publications that might be of interest to researchers. Because of the exponentially increasing volume of publications, effective citation recommendation is of great importance to researchers, as it reduces the time and effort spent on retrieving, understanding, and selecting research papers. In this context, we address the problem of citation recommendation, i.e., the task of recommending citations for a new paper. Current research investigates this task in different settings, including cases where rich user metadata is available (e.g., user profile, publications, citations). This work focus on a setting where the user provides only the abstract of a new paper as input. Our proposed approach is to expand the semantic features of the given abstract using knowledge graphs - and, combine them with other features (e.g., indegree, recency) to fit a learning to rank model. This model is used to generate the citation recommendations. By evaluating on real data, we show that the expanded semantic features lead to improving the quality of the recommendations measured by nDCG@10.

AB - Scholarly search engines, reference management tools, and academic social networks enable modern researchers to organize their scientific libraries. Moreover, they often provide recommendations for scientific publications that might be of interest to researchers. Because of the exponentially increasing volume of publications, effective citation recommendation is of great importance to researchers, as it reduces the time and effort spent on retrieving, understanding, and selecting research papers. In this context, we address the problem of citation recommendation, i.e., the task of recommending citations for a new paper. Current research investigates this task in different settings, including cases where rich user metadata is available (e.g., user profile, publications, citations). This work focus on a setting where the user provides only the abstract of a new paper as input. Our proposed approach is to expand the semantic features of the given abstract using knowledge graphs - and, combine them with other features (e.g., indegree, recency) to fit a learning to rank model. This model is used to generate the citation recommendations. By evaluating on real data, we show that the expanded semantic features lead to improving the quality of the recommendations measured by nDCG@10.

KW - Citation recommendations

KW - knowledge graphs

KW - recommender systems

U2 - 10.3233/JIFS-169493

DO - 10.3233/JIFS-169493

M3 - Article

SN - 1064-1246

JO - Journal of Intelligent and Fuzzy Systems

JF - Journal of Intelligent and Fuzzy Systems

Scholarly Paper Recommendation via Related Path Analysis in Knowledge Graph

Ieee account.

• Change Username/Password
• Update Address

Purchase Details

• Payment Options
• Order History
• View Purchased Documents

Profile Information

• Communications Preferences
• Profession and Education
• Technical Interests
• US & Canada: +1 800 678 4333
• Worldwide: +1 732 981 0060
• Contact & Support
• About IEEE Xplore
• Accessibility
• Terms of Use
• Nondiscrimination Policy
• Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. © Copyright 2024 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.

Help | Advanced Search

Computer Science > Information Retrieval

Title: sequential recommendation with latent relations based on large language model.

Abstract: Sequential recommender systems predict items that may interest users by modeling their preferences based on historical interactions. Traditional sequential recommendation methods rely on capturing implicit collaborative filtering signals among items. Recent relation-aware sequential recommendation models have achieved promising performance by explicitly incorporating item relations into the modeling of user historical sequences, where most relations are extracted from knowledge graphs. However, existing methods rely on manually predefined relations and suffer the sparsity issue, limiting the generalization ability in diverse scenarios with varied item relations. In this paper, we propose a novel relation-aware sequential recommendation framework with Latent Relation Discovery (LRD). Different from previous relation-aware models that rely on predefined rules, we propose to leverage the Large Language Model (LLM) to provide new types of relations and connections between items. The motivation is that LLM contains abundant world knowledge, which can be adopted to mine latent relations of items for recommendation. Specifically, inspired by that humans can describe relations between items using natural language, LRD harnesses the LLM that has demonstrated human-like knowledge to obtain language knowledge representations of items. These representations are fed into a latent relation discovery module based on the discrete state variational autoencoder (DVAE). Then the self-supervised relation discovery tasks and recommendation tasks are jointly optimized. Experimental results on multiple public datasets demonstrate our proposed latent relations discovery method can be incorporated with existing relation-aware sequential recommendation models and significantly improve the performance. Further analysis experiments indicate the effectiveness and reliability of the discovered latent relations.

Submission history

Access paper:.

• HTML (experimental)
• Other Formats

References & Citations

• Google Scholar
• Semantic Scholar

BibTeX formatted citation

Bibliographic and Citation Tools

Code, data and media associated with this article, recommenders and search tools.

• Institution

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs .

Knowledge graph enhanced citation recommendation model for patent examiners

• Published: 11 March 2024

Cite this article

• Yonghe Lu 1 , 2 ,
• Xinyu Tong 1 ,
• Xin Xiong 1 &
• Hou Zhu   ORCID: orcid.org/0000-0002-6843-9795 1  

67 Accesses

Explore all metrics

In the face of a growing volume of patents, patent examiners grapple with prolonged examination cycles, prompting the need for more effective citation recommendations. To address this, we introduce the patent knowledge graph embedded in Bert (PK-Bert) model. This innovation combines a patent knowledge graph with semantic information in an advanced Transformer framework, outperforming conventional common-sense knowledge graph embedding. PK-Bert exhibits substantial improvements, boosting the recall of accurate citation recommendations by 2.15% over the benchmark model Bert and 1.25% over K-Bert with CnDBpedia. Ablation experiments highlight the significance of knowledge graph elements, with the inventor proving most influential, followed by the IPC number and assignee. At the same time, publication time and title information have a minor impact. Moreover, PK-Bert excels when trained with earlier data and evaluated for patents issued post-November 2023. Our study not only advances patent examiner recommendations but also presents an efficient integration method for knowledge graph-enhanced semantic patent characterization.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Similar content being viewed by others

Interpretable patent recommendation with knowledge graph and deep learning

Han Chen & Weiwei Deng

PatentMiner: Patent Vacancy Mining via Context-Enhanced and Knowledge-Guided Graph Attention

A knowledge graph approach for recommending patents to companies

Weiwei Deng & Jian Ma

https://github.com/google-research/bert .

https://github.com/autoliuweijie/K-BERT .

https://github.com/dbiir/UER-py .

http://kw.fudan.edu.cn/cndbpedia/intro/ .

https://dbpedia.org/page/DBpedia .

Alcácer, J., & Gittelman, M. (2006). Patent citations as a measure of knowledge flows: The influence of examiner citations. The Review of Economics and Statistics, 88 (4), 774–779.

Article   Google Scholar  

An, X., Li, J., Xu, S., Chen, L., & Sun, W. (2021). An improved patent similarity measurem-ent based on entities and semantic relations. Journal of Informetrics, 15 (2), 101135. https://doi.org/10.1016/j.joi.2021.101135

Arts, S., Cassiman, B., & Gomez, J. C. (2018). Text matching to measure patent similarity. Strategic Management Journal, 39 (1), 62–84. https://doi.org/10.1002/smj.2699

Arts, S., Hou, J., & Gomez, J. C. (2021). Natural language processing to identify the creation and impact of new technologies in patent text: Code, data, and new measures. Research Policy, 50 (2), 104144. https://doi.org/10.1016/j.respol.2020.104144

Brooks, T. A. (1985). Private acts and public objects: An investigation of citer motivations. Journal of the American Society for Information Science, 36 (4), 223–229. https://doi.org/10.1002/asi.4630360402

Chen, H., & Deng, W. (2023). Interpretable patent recommendation with knowledge graph and deep learning. Scientific Reports, 13 (1), 2586. https://doi.org/10.1038/s41598-023-28766-y

Article   ADS   CAS   PubMed   PubMed Central   Google Scholar  

Chen, J., Chen, J., Zhao, S., Zhang, Y., & Tang, J. (2020). Exploiting word embedding for he-erogeneous topic model towards patent recommendation. Scientometrics, 125 (3), 2091–2108. https://doi.org/10.1007/s11192-020-03666-4

Article   CAS   Google Scholar  

Chen, L. (2017). Do patent citations indicate knowledge linkage? The evidence from text simil-arities between patents and their citations. Journal of Informetrics, 11 (1), 63–79. https://doi.org/10.1016/j.joi.2016.04.018

China National Intellectual Property Office (CNIPO). (2010). Guidelines for patent examination 2010 . Intellectual Property Publishing House Co., Ltd.

Google Scholar  

China National Intellectual Property Office (CNIPO). (2020). Process of patent application examination and approval. Retrieved December 10, 2023, from https://www.cnipa.gov.cn/art/2020/6/5/art_1517_92471.html

Choi, J., & Yoon, J. (2022). Measuring knowledge exploration distance at the patent level: Application of network embedding and citation analysis. Journal of Informetrics, 16 (2), 101286. https://doi.org/10.1016/j.joi.2022.101286

Choi, J., Lee, J., Yoon, J., Jang, S., Kim, J., & Choi, S. (2022a). A two-stage deep learning- based system for patent citation recommendation. Scientometrics, 127 (11), 6615–6636. https://doi.org/10.1007/s11192-022-04301-0

Choi, S., Lee, H., Park, E., & Choi, S. (2022b). Deep learning for patent landscaping using transformer and graph embedding. Technological Forecasting and Social Change, 175 , 121413. https://doi.org/10.1016/j.techfore.2021.121413

DeGrazia, C. A. W., Pairolero, N. A., & Teodorescu, M. H. M. (2021). Examination incentives, learning, and patent office outcomes: The use of examiner’s amendments at the USPTO. Research Policy, 50 (10), 104360. https://doi.org/10.1016/j.respol.2021.104360

Deng, W., & Ma, J. (2022). A knowledge graph approach for recommending patents to companies. Electronic Commerce Research, 22 (4), 1435–1466. https://doi.org/10.1007/s10660-021-09471-2

Devlin, J., Chang, M.W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In J. Burstein, C. Doran, & T. Solorio (Eds.), Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, volume 1 (long and short papers) (pp. 4171–4186). https://doi.org/10.18653/v1/N19-1423

Dietz, L., Kotov, A., & Meij, E. (2018). Utilizing knowledge graphs for text-centric information retrieval. In: The 41st international ACM SIGIR conference on research & development in information retrieval (pp. 1387–1390). https://doi.org/10.1145/3209978.3210187

European Patent Office (EPO). (2023). Guidelines for examination. Retrieved December 10, 2023, from https://www.epo.org/en/legal/guidelines-epc/2023/index.html

Färber, M., & Jatowt, A. (2020). Citation recommendation: Approaches and datasets. International Journal on Digital Libraries, 21 , 375–405. https://doi.org/10.1007/s00799-020-00288-2

Fu, T.Y., Lei, Z., & Lee, W.C. (2015). Patent Citation Recommendation for Examiners. In Proceedings of the 2015 IEEE international conference on data mining (ICDM) (pp. 751–756). https://doi.org/10.1109/ICDM.2015.151

Hain, D. S., Jurowetzki, R., Buchmann, T., & Wolf, P. (2022). A text-embedding-based approach to measuring patent-to-patent technological similarity. Technological Forecasting and Social Change, 177 , 121559. https://doi.org/10.1016/j.techfore.2022.121559

Harhoff, D., & Wagner, S. (2009). The duration of patent examination at the European patent office. Management Science, 55 (12), 1969–1984. https://doi.org/10.1287/mnsc.1090.1069

Japan Patent Office (JPO). (2015). Examination guidelines for patent and utility model in Japan. Retrieved December 10, 2023, from https://www.jpo.go.jp/e/system/laws/rule/guideline/patent/tukujitu_kijun/index.html

Ji, S., Pan, S., Cambria, E., Marttinen, P., & Yu, P. S. (2022). A survey on knowledge graphs: Representation, acquisition, and applications. IEEE Transactions on Neural Networks and Learning Systems, 33 (2), 494–514. https://doi.org/10.1109/TNNLS.2021.3070843

Article   MathSciNet   PubMed   Google Scholar  

Kim, Y. K., & Oh, J. B. (2017). Examination workloads, grant decision bias and examination quality of patent office. Research Policy, 46 (5), 1005–1019. https://doi.org/10.1016/j.respol.2017.03.007

Krestel, R., Chikkamath, R., Hewel, C., & Risch, J. (2021). A survey on deep learning for patent analysis. World Patent Information, 65 , 102035. https://doi.org/10.1016/j.wpi.2021.102035

Kuhn, J. M. (2011). Information overload at the U.S. patent and trademark office: Reframing the duty of disclosure in patent law as a search and filter problem. Journal of Law and Technology, 13 (3), 90–139. https://doi.org/10.1016/j.wpi.2021.102035

Lee, J., & Sohn, S. Y. (2021). Recommendation system for technology convergence opportunit-ies based on self-supervised representation learning. Scientometrics, 126 (1), 1–25. https://doi.org/10.1007/s11192-020-03731-y

Lin, D., Sun, J., Hao, T., & Wang, C. (2016). Research on the applicability of patent citation in patent value evaluation. Journal of Intelligence, 35 (12), 150–154.

Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., & Wang, P. (2020). K-BERT: Enabling language representation with knowledge graph. Proceedings of the AAAI Conference on Artificial Intelligence, 34 (03), 2901–2908. https://doi.org/10.1609/aaai.v34i03.5681

Lu, Y., Xiong, X., Zhang, W., Liu, J., & Zhao, R. (2020). Research on classification and simi-larity of patent citation based on deep learning. Scientometrics, 123 , 813–839. https://doi.org/10.1007/s11192-020-03385-w

Meyer, M. (2000). What is special about patent citations? Differences between scientific and patent citations. Scientometrics, 49 (1), 93–123. https://doi.org/10.1023/A:1005613325648

Ou, G., Pang, N., & Wu, J. (2022). Influencing factors of patent examination cycle: Case study of artificial intelligence in China. Data Analysis and Knowledge Discovery, 8 (6), 20–30.

Ridho, R., Edgar, M., & Maarten, D. R. (2020). Knowledge graphs: An information retrieval perspective . Now Foundations and Trends.

Shalaby, W., & Zadrozny, W. (2019). Patent retrieval: A literature review. Knowledge and Information Systems, 61 (2), 631–660. https://doi.org/10.1007/s10115-018-1322-7

Shi, K., Cai, X., Yang, L., & Zhao, J. (2023). Enriched entity representation of knowledge graph for text generation. Complex & Intelligent Systems, 9 (2), 2019–2030. https://doi.org/10.1007/s40747-022-00898-0

Teng, H., Wang, N., Zhao, H., Hu, Y., & Jin, H. (2024). Enhancing semantic text similarity with functional semantic knowledge (FOP) in patents. Journal of Informetrics, 18 (1), 101467. https://doi.org/10.1016/j.joi.2023.101467

Tong, T. W., Zhang, K., He, Z., & Zhang, Y. (2018). What determines the duration of patent examination in China? An outcome-specific duration analysis of invention patent applications at SIPO. Research Policy, 47 (3), 583–591. https://doi.org/10.1016/j.respol.2018.01.002

United States Patent Office (USPTO). (2020). Understanding the patent examination process. Retrieved December 10, 2023, from https://www.uspto.gov/sites/default/files/documents/InventionCon2020_Understanding_the_Patent_Examination_Process.pdf

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Polosukhin, I. (2017). Attention is all you need. In Proceedings of the 31st international conference on neural information processing systems (pp. 6000–6010). Curran Associates Inc

Wada, T. (2018). The choice of examiner patent citations for refusals: Evidence from the trilateral offices. Scientometrics, 117 (2), 825–843. https://doi.org/10.1007/s11192-018-2885-5

Wang, X., Ren, H., Chen, Y., Liu, Y., Qiao, Y., & Huang, Y. (2019). Measuring patent similarity with SAO semantic analysis. Scientometrics, 121 (1), 1–23. https://doi.org/10.1007/s11192-019-03191-z

Wang, Z., & Liu, Y. (2022). SEA-PS: Semantic embedding with attention to measuring patent similarity by leveraging various text fields. Journal of Information Science . https://doi.org/10.1177/01655515221106651

Wu, J., Li, B., Ji, Y., Tian, J., & Xiang, Y. (2022). Text-enhanced knowledge graph representation model in hyperbolic space. In J. Jiang & W. Chen (Eds.), Advanced data mining and applications. Springer. https://doi.org/10.1007/978-3-030-95408-6_11

Chapter   Google Scholar  

Xiao, Y., Li, C., & Thürer, M. (2023). A patent recommendation method based on KG repres-entation learning. Engineering Applications of Artificial Intelligence, 126 , 106722. https://doi.org/10.1016/j.engappai.2023.106722

Xu, B., Xu, Y., Liang, J., Xie, C., Liang, B., Cui, W., Xu, B., & Xiao, Y. (2017). CN-DBpedia: A never-ending Chinese knowledge extraction system. In S. Benferhat, K. Tabia, & M. Ali (Eds.), Advances in artificial intelligence: From theory to practice. Springer.

Yamauchi, I., & Nagaoka, S. (2015). Does the outsourcing of prior art search increase the efficiency of patent examination? Evidence from Japan. Research Policy, 44 (8), 1601–1614. https://doi.org/10.1016/j.respol.2015.05.003

Yin, M. J., Wang, B., & Ling, C. (2024). A fast local citation recommendation algorithm scal-able to multi-topics. Expert Systems with Applications, 238 , 122031. https://doi.org/10.1016/j.eswa.2023.122031

Zhang, Y., Shang, L., Huang, L., Porter, A. L., Zhang, G., Lu, J., & Zhu, D. (2016). A hybrid similarity measure method for patent portfolio analysis. Journal of Informetrics, 10 (4), 1108–1130. https://doi.org/10.1016/j.joi.2016.09.006

Zhao, Z., Chen, H., Zhang, J., Zhao, X., Liu, T., Lu, W., Du, X. (2019). UER: An open-source toolkit for pre-training models. In Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP): System demonstrations (pp. 241–246). https://doi.org/10.18653/v1/D19-3041

Zhao, Y., & Wen, T. (2017). Motivation analysis of patent citation. Information Studies Theory & Application, 40 (7), 28–32.

Download references

Acknowledgements

The authors warmly thank reviewers for their valuable suggestions. This research was partly supported by Key-Area Research and Development Program of Guangdong Province (NO.2021B0101420004), National Natural Science Foundation of China (NO.71801229).

Funding was provided by Key-Area Research and Development Program of Guangdong Province (Grant No. 2021B0101420004), National Natural Science Foundation of China (Grant No. 71801229).

Author information

Authors and affiliations.

School of Information Management, Sun Yat-sen University, Guangzhou, China

Yonghe Lu, Xinyu Tong, Xin Xiong & Hou Zhu

School of Artificial Intelligence, Sun Yat-sen University, Zhuhai, China

You can also search for this author in PubMed   Google Scholar

Contributions

YL: Investigation, Writing—review and editing. XT: Methodology, Investigation, Writing—review and editing. XX: Methodology, Software, Writing—original draft. HZ: Supervision, Funding acquisition, Writing—review and editing.

Corresponding author

Correspondence to Hou Zhu .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

See Table  9 .

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Lu, Y., Tong, X., Xiong, X. et al. Knowledge graph enhanced citation recommendation model for patent examiners. Scientometrics (2024). https://doi.org/10.1007/s11192-024-04966-9

Download citation

Received : 23 April 2023

Accepted : 08 February 2024

Published : 11 March 2024

DOI : https://doi.org/10.1007/s11192-024-04966-9

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Knowledge graph
• Patent citation recommendation
• Patent examiner citation
• Deep learning
• Find a journal
• Publish with us
• Track your research