arXiv subjects

Zhentao Liang

Publications and source records attributed to Zhentao Liang.

4 recordsLinked to original sources

arXiv2026

From citation intent to knowledge contribution: Classifying what cited papers actually contribute

Understanding the flow and evolution of scientific knowledge is essential for assessing research impact. Existing citation analysis methods mainly focus on citing authors' subjective intents, failing to consistently characterize cited papers' knowledge contributions. This study proposes the Knowledge Contribution Taxonomy (KCT), derived from the Scientific Research Logic Model, which identifies the type of knowledge a cited paper contributes based on the citation context. KCT classifies citations into Method, Resource Tool, Empirical Finding, and Background, further distinguishing core from non-core contributions. We propose a Dual-Path Fusion model for the classification task, which achieves an accuracy of 85.5%, outperforming mainstream large language models. An analysis of 802,202 citations from the ACL Anthology reveals that core knowledge contributions account for only 39.09% of all citations. The core knowledge contribution citation count achieves higher hit rates for award-winning papers than the traditional citation count at all ranking cutoffs, reflecting the value of differentiating knowledge contributions for research evaluation and impact prediction. In dissemination prediction experiments, KCT outperforms citation intent classification, demonstrating its stronger predictive validity for scholarly dissemination. By focusing on the knowledge contributions of cited papers, the KCT can support differentiated research evaluation.

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arXiv2026

A large-scale dataset of sub-institution name disambiguation and hierarchical structures from OpenAlex

Accurate attribution of scholarly work to specific sub-institutional units, such as schools or departments of a university, is crucial for granular research assessment and policymaking. While robust identifiers exist for top-level institutions, standardized data for sub-level units remains scarce due to the linguistic and structural variability of affiliation strings. In this study, we introduce OpenSubAffil, a large-scale dataset mapping raw affiliation strings from OpenAlex to disambiguated sub-institutional entities and their hierarchical structures. We developed a pipeline integrating named entity recognition (NER) with embedding-based clustering. Furthermore, we proposed a multi-signal scoring function that synthesizes lexical and co-occurrence evidence to reconstruct the sub-institutional hierarchy. OpenSubAffil comprises mappings for 40 million affiliation strings to 638,843 disambiguated sub-units across 18,635 top-level institutions, together with their hierarchical relationships. Validation against Wikidata benchmarks and manual investigation show that our method achieves promising performance. Overall, this dataset bridges the granularity gap between individual researchers and top-level institutions, enabling high-resolution analyses of scholarly output and communication at the sub-institutional level. The OpenSubAffil dataset is publicly available at https://doi.org/10.5281/zenodo.19602782.

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arXiv2025

Citation importance-aware document representation learning for large-scale science mapping

Effective science mapping relies on high-quality representations of scientific documents. As an important task in scientometrics and information studies, science mapping is often challenged by the complex and heterogeneous nature of citations. While previous studies have attempted to improve document representations by integrating citation and semantic information, the heterogeneity of citations is often overlooked. To address this problem, this study proposes a citation importance-aware contrastive learning framework that refines the supervisory signal. We first develop a scalable measurement of citation importance based on location, frequency, and self-citation characteristics. Citation importance is then integrated into the contrastive learning process through an importance-aware sampling strategy, which selects low-importance citations as hard negatives. This forces the model to learn finer-grained representations that distinguish between important and perfunctory citations. To validate the effectiveness of the proposed framework, we fine-tune a SciBERT model and perform extensive evaluations on SciDocs and PubMed benchmark datasets. Results show consistent improvements in both document representation quality and science mapping accuracy. Furthermore, we apply the trained model to over 33 million documents from Web of Science. The resulting map of science accurately visualizes the global and local intellectual structure of science and reveals interdisciplinary research fronts. By operationalizing citation heterogeneity into a scalable computational framework, this study demonstrates how differentiating citations by their importance can be effectively leveraged to improve document representation and science mapping.

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arXiv2021

Finding citations for PubMed: A large-scale comparison between five freely available bibliographic data sources

As an important biomedical database, PubMed provides users with free access to abstracts of its documents. However, citations between these documents need to be collected from external data sources. Although previous studies have investigated the coverage of various data sources, the quality of citations is underexplored. In response, this study compares the coverage and citation quality of five freely available data sources on 30 million PubMed documents, including OpenCitations Index of CrossRef open DOI-to-DOI citations (COCI), Dimensions, Microsoft Academic Graph (MAG), National Institutes of Health Open Citation Collection (NIH-OCC), and Semantic Scholar Open Research Corpus (S2ORC). Three gold standards and five metrics are introduced to evaluate the correctness and completeness of citations. Our results indicate that Dimensions is the most comprehensive data source that provides references for 62.4% of PubMed documents, outperforming the official NIH-OCC dataset (56.7%). Over 90% of citation links in other data sources can also be found in Dimensions. The coverage of MAG, COCI, and S2ORC is 59.6%, 34.7%, and 23.5%, respectively. Regarding the citation quality, Dimensions and NIH-OCC achieve the best overall results. Almost all data sources have a precision higher than 90%, but their recall is much lower. All databases have better performances on recent publications than earlier ones. Meanwhile, the gaps between different data sources have diminished for the documents published in recent years. This study provides evidence for researchers to choose suitable PubMed citation sources, which is also helpful for evaluating the citation quality of free bibliographic databases.

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