检索结果-内蒙古大学图书馆

A Novel Curated scholarly Graph Connecting Textual and Data Publications

ACM JOURNAL OF DATA AND INFORMATION QUALITY 2023年第3期15卷 1-24页

作者： Irrera, Ornella Mannocci, Andrea Manghi, Paolo Silvello, Gianmaria Univ Padua Dept Informat Engn Via Gradenigo 6-B I-35131 Padua Italy CNR ISTI Via Giuseppe Moruzzi 1 I-56124 Pisa Italy

In the last decade, scholarly graphs became fundamental to storing and managing scholarly knowledge in a structured and machine-readable way. Methods and tools for discovery and impact assessment of science rely on such graphs and their quality to serve scientists, policymakers, and publishers. Since research data became very important in scholarly communication, scholarly graphs started including dataset metadata and their relationships to publications. Such graphs are the foundations for Open Science investigations, data-article publishing workflows, discovery, and assessment indicators. However, due to the heterogeneity of practices (FAIRness is indeed in the making), they often lack the complete and reliable metadata necessary to perform accurate data analysis;e.g., dataset metadata is inaccurate, author names are not uniform, and the semantics of the relationships is unknown, ambiguous or incomplete. This work describes an open and curated scholarly graph we built and published as a training and test set for data discovery, data connection, author disambiguation, and link prediction tasks. Overall the graph contains 4,047 publications, 5,488 datasets, 22 software, 21,561 authors;9,692 edges interconnect publications to datasets and software and are labeled with semantics that outline whether a publication is citing, referencing, documenting, supplementing another product. To ensure high-quality metadata and semantics, we relied on the information extracted from PDFs of the publications and the datasets and software webpages to curate and enrich nodes metadata and edges semantics. To the best of our knowledge, this is the first ever published resource, including publications and datasets with manually validated and curated metadata.

关键词： scholarly knowledge graphs data curation data enrichment datasets open science

来源：评论

学校读者我要写书评

暂无评论

Leveraging Large Language Models for Realizing Truly Intelligent User Interfaces

Leveraging Large Language Models for Realizing Truly Intelli...

引用

CHI Conference on Human Factors in Computing Sytems (CHI)

作者： Oelen, Allard Auer, Soeren TIB Leibniz Informat Ctr Sci & Technol Hannover Germany

ISBN: (纸本)9798400703317

The number of published scholarly articles is growing at a significant rate, making scholarly knowledge organization increasingly important. Various approaches have been proposed to organize scholarly information, including describing scholarly knowledge semantically leveraging knowledge graphs. Transforming unstructured knowledge, presented within articles, to structured and semantically represented knowledge generally requires human intelligence and labor since natural language processing methods alone typically do not render sufficient precision and recall for many applications. With the recent developments of Large Language Models (LLMs), it becomes increasingly possible to provide truly intelligent user interfaces guiding humans in the transformation process. We present an approach to integrate non-intrusive LLMs guidance into existing user interfaces. More specifically, we integrate LLM-supported user interface components into an existing scholarly knowledge infrastructure. Additionally, we provide our experiences with LLM integration, detailing best practices and obstacles. Finally, we evaluate the approach using a small-scale user evaluation with domain experts.

关键词： Intelligent User Interface LLM Interface scholarly knowledge graphs

来源：评论

学校读者我要写书评

暂无评论

Creating and validating a scholarly knowledge graph using natural language processing and microtask crowdsourcing

引用

INTERNATIONAL JOURNAL ON DIGITAL LIBRARIES 2024年第2期25卷 273-285页

作者： Oelen, Allard Stocker, Markus Auer, Soren TIB Leibniz Informat Ctr Sci & Technol Hannover Germany

Due to the growing number of scholarly publications, finding relevant articles becomes increasingly difficult. scholarly knowledge graphs can be used to organize the scholarly knowledge presented within those publications and represent them in machine-readable formats. Natural language processing (NLP) provides scalable methods to automatically extract knowledge from articles and populate scholarly knowledge graphs. However, NLP extraction is generally not sufficiently accurate and, thus, fails to generate high granularity quality data. In this work, we present TinyGenius, a methodology to validate NLP-extracted scholarly knowledge statements using microtasks performed with crowdsourcing. TinyGenius is employed to populate a paper-centric knowledge graph, using five distinct NLP methods. We extend our previous work of the TinyGenius methodology in various ways. Specifically, we discuss the NLP tasks in more detail and include an explanation of the data model. Moreover, we present a user evaluation where participants validate the generated NLP statements. The results indicate that employing microtasks for statement validation is a promising approach despite the varying participant agreement for different microtasks.

关键词： Crowdsourcing microtasks knowledge graph validation scholarly knowledge graphs User interface evaluation

来源：评论

学校读者我要写书评

暂无评论

TinyGenius: Intertwining Natural Language Processing with Microtask Crowdsourcing for scholarly knowledge Graph Creation 22

TinyGenius: Intertwining Natural Language Processing with Mi...

引用

22nd ACM/IEEE Joint Conference on Digital Libraries (JCDL)

作者： Oelen, Allard Stocker, Markus Auer, Soeren TIB Leibniz Informat Ctr Sci & Technol Hannover Germany

ISBN: (数字)9781450393454

ISBN: (纸本)9781665491556

As the number of published scholarly articles grows steadily each year, new methods are needed to organize scholarly knowledge so that it can be more efficiently discovered and used. Natural Language Processing (NLP) techniques are able to autonomously process scholarly articles at scale and to create machine readable representations of the article content. However, autonomous NLP methods are by far not sufficiently accurate to create a high-quality knowledge graph. Yet quality is crucial for the graph to be useful in practice. We present TinyGenius, a methodology to validate NLP-extracted scholarly knowledge statements using microtasks performed with crowdsourcing. The scholarly context in which the crowd workers operate has multiple challenges. The explainability of the employed NLP methods is crucial to provide context in order to support the decision process of crowd workers. We employed TinyGenius to populate a paper-centric knowledge graph, using five distinct NLP methods. In the end, the resulting knowledge graph serves as a digital library for scholarly articles.

关键词： Crowdsourcing Microtasks knowledge Graph Validation scholarly knowledge graphs Intelligent User Interfaces

来源：评论

学校读者我要写书评

暂无评论

VeTo plus : improved expert set expansion in academia

引用

INTERNATIONAL JOURNAL ON DIGITAL LIBRARIES 2022年第1期23卷 57-75页

作者： Chatzopoulos, Serafeim Vergoulis, Thanasis Dalamagas, Theodore Tryfonopoulos, Christos Athena Res Ctr IMSI Athens 15125 Greece Univ Peloponnese Dept Informat & Tel Tions Tripoli 22100 Greece

Expanding a set of known domain experts with new individuals, sharing similar expertise, is a problem that has various applications, such as adding new members to a conference program committee or finding new referees to review funding proposals. In this work, we focus on applications of the problem in the academic world and we introduce VeTo+, a novel approach to effectively deal with it by exploiting scholarly knowledge graphs. VeTo+ expands a given set of experts by identifying scholars having similar publishing habits with them. Our experiments show that VeTo+ outperforms, in terms of accuracy, previous approaches to recommend expansions to a set of given academic experts.

关键词： Expertise retrieval Expert finding scholarly knowledge graphs Data mining

来源：评论

学校读者我要写书评

暂无评论

Pattern-Based Acquisition of Scientific Entities from scholarly Article Titles 23rd

Pattern-Based Acquisition of Scientific Entities from Schola...

引用

23rd International Conference on Asia-Pacific Digital Libraries (ICADL)

作者： D'Souza, Jennifer Auer, Soren TIB Leibniz Informat Ctr Sci & Technol Hannover Germany Leibniz Univ Hannover L3S Res Ctr Hannover Germany

ISBN: (纸本)9783030916695;9783030916688

We describe a rule-based approach for the automatic acquisition of salient scientific entities from Computational Linguistics (CL) scholarly article titles. Two observations motivated the approach: (i) noting salient aspects of an article's contribution in its title;and (ii) pattern regularities capturing the salient terms that could be expressed in a set of rules. Only those lexico-syntactic patterns were selected that were easily recognizable, occurred frequently, and positionally indicated a scientific entity type. The rules were developed on a collection of 50,237 CL titles covering all articles in the ACL Anthology. In total, 19,799 research problems, 18,111 solutions, 20,033 resources, 1,059 languages, 6,878 tools, and 21,687 methods were extracted at an average precision of 75%.

关键词： Terminology extraction Rule-based system Natural language processing scholarly knowledge graphs Semantic publishing

来源：评论

学校读者我要写书评

暂无评论

Sentence,Phrase,and Triple Annotations to Build a knowledge Graph of Natural Language Processing Contributions—A Trial Dataset

引用

Journal of Data and Information Science 2021年第3期6卷 6-34页

作者： Jennifer D’Souza Sören Auer TIB Leibniz Information Centre for Science and Technology HannoverGermany

Purpose:This work aims to normalize the NLPCONTRIBUTIONS scheme(henceforward,NLPCONTRIBUTIONGRAPH)to structure,directly from article sentences,the contributions information in Natural Language Processing(NLP)scholarly articles via a two-stage annotation methodology:1)pilot stage-to define the scheme(described in prior work);and 2)adjudication stage-to normalize the graphing model(the focus of this paper).Design/methodology/approach:We re-annotate,a second time,the contributions-pertinent information across 50 prior-annotated NLP scholarly articles in terms of a data pipeline comprising:contribution-centered sentences,phrases,and triple *** this end,specifically,care was taken in the adjudication annotation stage to reduce annotation noise while formulating the guidelines for our proposed novel NLP contributions structuring and graphing ***:The application of NLPCONTRIBUTIONGRAPH on the 50 articles resulted finally in a dataset of 900 contribution-focused sentences,4,702 contribution-information-centered phrases,and 2,980 surface-structured *** intra-annotation agreement between the first and second stages,in terms of F1-score,was 67.92%for sentences,41.82%for phrases,and 22.31%for triple statements indicating that with increased granularity of the information,the annotation decision variance is *** limitations:NLPCONTRIBUTIONGRAPH has limited scope for structuring scholarly contributions compared with STEM(Science,Technology,Engineering,and Medicine)scholarly knowledge at ***,the annotation scheme in this work is designed by only an intra-annotator consensus-a single annotator first annotated the data to propose the initial scheme,following which,the same annotator reannotated the data to normalize the annotations in an adjudication ***,the expected goal of this work is to achieve a standardized retrospective model of capturing NLP contributions from scholarly *** would entail a larger initiati

关键词： scholarly knowledge graphs Open science graphs knowledge representation Natural language processing Semantic publishing

来源：评论

学校读者我要写书评

暂无评论

VeTo-web: A Recommendation Tool for the Expansion of Sets of Scholars 21

VeTo-web: A Recommendation Tool for the Expansion of Sets of...

引用

21st ACM/IEEE Joint Conference on Digital Libraries (JCDL)

作者： Chatzopoulos, Serafeim Vergoulis, Thanasis Dalamagas, Theodore Tryfonopoulos, Christos Univ Peloponnese Dept Informat & Tel Tions Tripoli Greece ATHENA RC IMSI Athens Greece

ISBN: (纸本)9781665417709

Expanding a set of known experts with new ones that share similar expertise is a problem that emerges in various real-life applications. We demonstrate VeTo-web, an open source, publicly available tool that deals with this problem in the context of searching for academic experts. VeTo-web exploits analysis techniques for scholarly knowledge graphs to identify scholars that share similar research activities with a given expert group and offers a Web-based user interface to assist its users in expanding a set of academic experts with additional scholars with similar expertise.

关键词： expert finding scholarly knowledge graphs

来源：评论

学校读者我要写书评

暂无评论

Triple Classification for scholarly knowledge Graph Completion 21

Triple Classification for Scholarly Knowledge Graph Completi...

引用

11th knowledge Capture Conference (K-CAP)

作者： Jaradeh, Mohamad Yaser Singh, Kuldeep Stocker, Markus Auer, Soeren Leibniz Univ Hannover L3S Res Ctr Hannover Germany Cerence GmbH Zerotha Res Aachen Germany TIB Leibniz Informat Ctr Sci & Technol Hannover Germany

ISBN: (纸本)9781450384575

scholarly knowledge graphs (KGs) provide a rich source of structured information representing knowledge encoded in scientific publications. With the sheer volume of published scientific literature comprising a plethora of inhomogeneous entities and relations to describe scientific concepts, these KGs are inherently incomplete. We present exBERT ('epsilon ksp3:rt), a method for leveraging pre-trained transformer language models to perform scholarly knowledge graph completion. We model triples of a knowledge graph as text and perform triple classification (i.e., belongs to KG or not). The evaluation shows that exBERT outperforms other baselines on three scholarly KG completion datasets in the tasks of triple classification, link prediction, and relation prediction. Furthermore, we present two scholarly datasets as resources for the research community, collected from public KGs and online resources.

关键词： scholarly knowledge graphs Triple Classification Link Prediction Relation Prediction

来源：评论

学校读者我要写书评

暂无评论

VeTo: Expert Set Expansion in Academia 24th

VeTo: Expert Set Expansion in Academia

引用

24th International Conference on Theory and Practice of Digital Libraries (TPDL)

作者： Vergoulis, Thanasis Chatzopoulos, Serafeim Dalamagas, Theodore Tryfonopoulos, Christos Athena Res Ctr IMSI Athens 15125 Greece Univ Peloponnese Dept Informat & Telecommun Tripoli 22100 Greece

ISBN: (纸本)9783030549558;9783030549565

Expanding a set of known domain experts with new individuals, that have similar expertise, is a problem with many practical applications (e.g., adding new members to a conference program committee). In this work, we study this problem in the context of academic experts and we introduce VeTo, a novel method to effectively deal with it by exploiting scholarly knowledge graphs. In particular, VeTo expands the given set of experts by identifying researchers that share similar publishing habits with them, based on a graph analysis approach. Our experiments show that VeTo is more effective than existing techniques that can be applied to deal with the same problem.

关键词： Expertise retrieval scholarly knowledge graphs

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：