Generating knowledge graphs by employing Natural Language Processing and Machine Learning techniques within the scholarly domain
- Submitting institution
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The Open University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1652504
- Type
- D - Journal article
- DOI
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10.1016/j.future.2020.10.026
- Title of journal
- Future Generation Computer Systems
- Article number
- -
- First page
- 253
- Volume
- 116
- Issue
- -
- ISSN
- 0167-739X
- Open access status
- Compliant
- Month of publication
- October
- Year of publication
- 2020
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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4
- Research group(s)
-
-
- Citation count
- 0
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This work, published in the prestigious Future Generation Computer Systems journal, presents an approach to extracting knowledge graphs from collections of research papers. This is the first approach integrating information from multiple publications, while also fusing results from different techniques, including deep learning, entity linking, and others. The approach was used to generate AI-KG (Dessì et al., 2020), a large resource characterizing research in Artificial Intelligence. These novel resources pave the way for a new generation of systems assisting researchers in exploring the literature – for instance by supporting complex queries about the content of the domain literature.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -