Generating Knowledge Graphs by Employing Natural Language Processing and Machine Learning Techniques within the Scholarly Domain
University of Cagliari（卡利亚里大学）；FIZ Karlsruhe - Leibniz Institute for Information Infrastructure（FIZ卡尔斯鲁厄——莱布尼茨信息基础设施研究所）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；The Open University（开放大学）；CNRS（法国国家科学研究中心）；University Paris 13（巴黎第十三大学）
Danilo Dessì, Francesco Osborne, Diego Reforgiato Recupero, Davide Buscaldi, Enrico Motta
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Accepted for publication in Future Generation Computer Systems journal - Special Issue on Machine Learning and Knowledge Graphs
The continuous growth of scientific literature brings innovations and, at the same time, raises new challenges. One of them is related to the fact that its analysis has become difficult due to the high volume of published papers for which manual effort for annotations and management is required. Novel technological infrastructures are needed to help researchers, research policy makers, and companies to time-efficiently browse, analyse, and forecast scientific research. Knowledge graphs i.e., large networks of entities and relationships, have proved to be effective solution in this space. Scientific knowledge graphs focus on the scholarly domain and typically contain metadata describing research publications such as authors, venues, organizations, research topics, and citations. However, the current generation of knowledge graphs lacks of an explicit representation of the knowledge presented in the research papers. As such, in this paper, we present a new architecture that takes advantage of Natural Language Processing and Machine Learning methods for extracting entities and relationships from research publications and integrates them in a large-scale knowledge graph. Within this research work, we i) tackle the challenge of knowledge extraction by employing several state-of-the-art Natural Language Processing and Text Mining tools, ii) describe an approach for integrating entities and relationships generated by these tools, iii) show the advantage of such an hybrid system over alternative approaches, and vi) as a chosen use case, we generated a scientific knowledge graph including 109,105 triples, extracted from 26,827 abstracts of papers within the Semantic Web domain. As our approach is general and can be applied to any domain, we expect that it can facilitate the management, analysis, dissemination, and processing of scientific knowledge.
L3S Research Center（L3S研究中心）；Leibniz University of Hannover（莱布尼茨汉诺威大学）；TIB Leibniz Information Centre for Science and Technology（莱布尼茨科学技术信息中心）
With the rapid growth of research publications, empowering scientists to keep oversight over the scientific progress is of paramount importance. In this regard, the Leaderboards facet of information organization provides an overview on the state-of-the-art by aggregating empirical results from various studies addressing the same research challenge. Crowdsourcing efforts like PapersWithCode among others are devoted to the construction of Leaderboards predominantly for various subdomains in Artificial Intelligence. Leaderboards provide machine-readable scholarly knowledge that has proven to be d…
This paper addresses the problem of generating table captions for scholarly documents, which often require additional information outside the table. To this end, we propose a method of retrieving relevant sentences from the paper body, and feeding the table content as well as the retrieved sentences into pre-trained language models (e.g. T5 and GPT-2) for generating table captions. The contributions of this paper are: (1) discussion on the challenges in table captioning for scholarly documents; (2) development of a dataset DocBank-TB, which is publicly available; and (3) comparison of caption …
Filament AI；University of Warwick（华威大学）；The Alan Turing Institute（艾伦·图灵研究所）；Queen Mary University of London（伦敦玛丽女王大学）
Question Answering (QA) is a growing area of research, often used to facilitate the extraction of information from within documents. State-of-the-art QA models are usually pre-trained on domain-general corpora like Wikipedia and thus tend to struggle on out-of-domain documents without fine-tuning. We demonstrate that synthetic domain-specific datasets can be generated easily using domain-general models, while still providing significant improvements to QA performance. We present two new tools for this task: A flexible pipeline for validating the synthetic QA data and training downstream models…
MemoNoveltyAgent: A Historical Research Memory-Aware Agent Workflow for Paper Novelty Assessment
Institute of Computing and Intelligence, Harbin Institute of Technology, Shenzhen, China（计算与智能研究院，哈尔滨工业大学深圳校区，中国）；Xiaohongshu Inc.（小红书公司）；Zhongguancun Academy, Beijing, China（中关村学院，北京，中国）；NLP 2 CT Lab, Department of Computer and Information Science, University of Macau, China（自然语言处理2实验室，计算机与信息科学系，澳门大学，中国）
The significance and abundance of data are increasing due to the growing digital data generated from social media, sensors, scholarly literature, patents, different forms of documents published online, databases, product manuals, etc. Various data sources can be used to generate ideas, yet, in addition to bias, the size of the available digital data is a major challenge when it comes to manual analysis. Hence, human-machine interaction is essential for generating valuable ideas where machine learning and data-driven techniques generate patterns from data and serve human sense-making. However, …
Do Large Multimodal Models Solve Caption Generation for Scientific Figures? Lessons Learned from SciCap Challenge 2023
Pennsylvania State University（宾夕法尼亚州立大学）；National Tsing Hua University（国立清华大学）；AllSci（奥尔赛公司）；MetaMetrics Inc.（美塔麦特里克公司）；Adobe Research（奥多比研究院）；Institute of Information Science, Academia Sinica（中央研究院资讯科学研究所）
Fondazione Bruno Kessler（布鲁诺·凯斯勒基金会）；University of Trento（特伦托大学）
Recently research has started focusing on avoiding undesired effects that come with content moderation, such as censorship and overblocking, when dealing with hatred online. The core idea is to directly intervene in the discussion with textual responses that are meant to counter the hate content and prevent it from further spreading. Accordingly, automation strategies, such as natural language generation, are beginning to be investigated. Still, they suffer from the lack of sufficient amount of quality data and tend to produce generic/repetitive responses. Being aware of the aforementioned lim…
Facebook AI Research（脸书人工智能研究院）；ETH Zürich（苏黎世联邦理工学院）
Language models (LMs) have recently been shown to generate more factual responses by employing modularity (Zhou et al., 2021) in combination with retrieval (Adolphs et al., 2021). We extend the recent approach of Adolphs et al. (2021) to include internet search as a module. Our SeeKeR (Search engine->Knowledge->Response) method thus applies a single LM to three modular tasks in succession: search, generating knowledge, and generating a final response. We show that, when using SeeKeR as a dialogue model, it outperforms the state-of-the-art model BlenderBot 2 (Chen et al., 2021) on open-domain k…
BioMNER: A Dataset for Biomedical Method Entity Recognition
University of Surrey（萨里大学）；The University of Manchester（曼彻斯特大学）；University of Pittsburgh（匹兹堡大学）；Chinese Academy of Sciences（中国科学院）；Durham University（杜伦大学）
CiteLLM: An Agentic Platform for Trustworthy Scientific Reference Discovery
Hong Kong Polytechnic University（香港理工大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；Swiss Federal Technology Institute of Lausanne (EPFL)（洛桑联邦理工学院）；Hong Kong University of Science and Technology (HKUST)（香港科学大学）
From human experts to machines: An LLM supported approach to ontology and knowledge graph construction
Heinz Nixdorf Chair for Distributed Information Systems, Friedrich Schiller University Jena（弗里德里希·席勒大学耶拿分校海因茨·尼克多夫分布式信息系统教席）；German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig（德国综合生物多样性研究中心（iDiv）哈雷-耶拿-莱比锡）；Max Planck Institute for Biogeochemistry（马克斯·普朗克生物地球化学研究所）；Michael Stifel Center Jena（耶拿迈克尔·斯蒂费尔中心）
