Scalable, Semi-Supervised Extraction of Structured Information from Scientific Literature

Agrawal, Kritika; Mittal, Aakash; Pudi, Vikram

doi:10.18653/v1/w19-2602

Cited by 7 publications

(4 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [66], metadata is trained and passed to two layered bi-directional LSTM to accomplish entity extraction task. Similarly, a scalable, semisupervised and domain-independent method [67] is proposed for extracting concepts from scientific literature using word embedding and pre-trained BERT model. To avoid misinformation in resources and to generate reliable knowledge graph for drug repurposing, COVID-KG [68] is constructed using hierarchical spherical embedding and text embedding in the direction considering cross-media (text and figures) extraction.…”

Section: Neural Network-enabled Kg Creationmentioning

confidence: 99%

Scholarly knowledge graphs through structuring scholarly communication: a review

Verma

Bhatia

Harit

et al. 2022

Complex Intell. Syst.

View full text Add to dashboard Cite

The necessity for scholarly knowledge mining and management has grown significantly as academic literature and its linkages to authors produce enormously. Information extraction, ontology matching, and accessing academic components with relations have become more critical than ever. Therefore, with the advancement of scientific literature, scholarly knowledge graphs have become critical to various applications where semantics can impart meanings to concepts. The objective of study is to report a literature review regarding knowledge graph construction, refinement and utilization in scholarly domain. Based on scholarly literature, the study presents a complete assessment of current state-of-the-art techniques. We presented an analytical methodology to investigate the existing status of scholarly knowledge graphs (SKG) by structuring scholarly communication. This review paper investigates the field of applying machine learning, rule-based learning, and natural language processing tools and approaches to construct SKG. It further presents the review of knowledge graph utilization and refinement to provide a view of current research efforts. In addition, we offer existing applications and challenges across the board in construction, refinement and utilization collectively. This research will help to identify frontier trends of SKG which will motivate future researchers to carry forward their work.

show abstract

Section: Neural Network-enabled Kg Creationmentioning

confidence: 99%

Scholarly knowledge graphs through structuring scholarly communication: a review

Verma

Bhatia

Harit

et al. 2022

Complex Intell. Syst.

View full text Add to dashboard Cite

show abstract

“…Recent lines of research have explored end-toend frameworks based on NLP extraction tasks, such as NER, which involve a series of intercon-nected methods aimed at creating knowledge bases or knowledge graphs. Agrawal et al (2019) focused on extracting the aim, method, and result sections from scientific articles, utilizing this information to construct a scientific knowledge graph. Similarly, Mondal et al (2021) developed SciNLP-KG, a framework designed to extract TDM entities and relations from papers in the NLP domain.…”

Section: Related Workmentioning

confidence: 99%

Team GESIS Cologne: An all in all sentence-based approach for FEVER

Otto¹

2018

Proceedings of the First Workshop on Fact Extraction and VERification (FEVER)

View full text Add to dashboard Cite

In this system description of our pipeline to participate at the Fever Shared Task, we describe our sentence-based approach. Throughout all steps of our pipeline, we regarded single sentences as our processing unit. In our IR-Component, we searched in the set of all possible Wikipedia introduction sentences without limiting sentences to a fixed number of relevant documents. In the entailment module, we judged every sentence separately and combined the result of the classifier for the top 5 sentences with the help of an ensemble classifier to make a judgment whether the truth of a statement can be derived from the given claim.

show abstract

“…Since the growth rates of unstructured information are very high and growing in recent times, the key challenges in IE, mining and analysis must be understood. The main challenges to collect useful information are the scalability, dimensionality, and heterogeneity of unstructured data [4]. The big questions are transforming unstructured data into a structured format to improve representation.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Information Extraction Techniques, Models and Systems

Rahman¹,

Musleh²,

Nabil³

et al. 2022

MMEP

View full text Add to dashboard Cite

The present article aims to review and evaluate the practiced and classical techniques, tools, models, and systems concerning automatic information extraction (IE) from published scientific documents like research articles, patents, theses, technical reports, and case studies etc. IE is performed for various reasons such as better indexing, archiving, searching, and retrieving. That is mainly used by the search engines and the indexing services as well the digital libraries and semantic web. In this regard, several studies have been conducted targeting various nature of documents. The study pays special consideration to the successful IE models, algorithms and approaches applied to structural IE from published documents. To grasp this, the paper is classified into several segments and each segment covers a significant aspect of IE. Furthermore, to validate their benefits and drawbacks, a comparative study of all the approaches have been conducted in terms of various performance factors like precision, accuracy, recall and F-score. Potential areas of improvement have been emphasized as research gap for the scholars in the closely related areas. Ultimately, a comprehensive summary of the evaluation is presented in tabular form and review is concluded. It was observed that the hybrid methods outperform the other methods due to their versatile nature to address various document formats.

show abstract

Scalable, Semi-Supervised Extraction of Structured Information from Scientific Literature

Cited by 7 publications

References 16 publications

Scholarly knowledge graphs through structuring scholarly communication: a review

Scholarly knowledge graphs through structuring scholarly communication: a review

Team GESIS Cologne: An all in all sentence-based approach for FEVER

Assessment of Information Extraction Techniques, Models and Systems

Contact Info

Product

Resources

About