Science communication has a number of bottlenecks that include the rising number of published research papers and its non-machine-accessible and document-based paradigm, which makes the exploration, reading, and reuse of research outcomes rather inefficient. Recently, Knowledge Graphs (KG), i.e., semantic interlinked networks of entities, have been proposed as a new core technology to describe and curate scholarly information with the goal to make it machine readable and understandable. However, the main drawback of the use of such a technology is that researchers are asked to manually annotate their research papers and add their contributions within the KGs. To address this problem, in this paper we propose SCICERO, a novel KG generation approach that takes in input text from research articles and generates a KG of research entities. SCICERO uses Natural Language Processing techniques to parse the content of scientific papers to discover entities and relationships, exploits state-of-the-art Deep Learning Transformer models to make sense and validate extracted information, and uses Semantic Web best practices to formally represent the extracted entities and relationships, making the written content of research papers machine-actionable. SCICERO has been tested on a dataset of 6.7M papers about Computer Science generating a KG of about 10M entities. It has been evaluated on a manually generated gold standard of 3,600 triples that cover three Computer Science subdomains (Information Retrieval, Natural Language Processing, and Machine Learning) obtaining remarkable results.

Dessi, D., Osborne, F., Reforgiato Recupero, D., Buscaldi, D., Motta, E. (2022). SCICERO: A deep learning and NLP approach for generating scientific knowledge graphs in the computer science domain. KNOWLEDGE-BASED SYSTEMS, 258(22 December 2022) [10.1016/j.knosys.2022.109945].

SCICERO: A deep learning and NLP approach for generating scientific knowledge graphs in the computer science domain

Osborne F.;
2022

Abstract

Science communication has a number of bottlenecks that include the rising number of published research papers and its non-machine-accessible and document-based paradigm, which makes the exploration, reading, and reuse of research outcomes rather inefficient. Recently, Knowledge Graphs (KG), i.e., semantic interlinked networks of entities, have been proposed as a new core technology to describe and curate scholarly information with the goal to make it machine readable and understandable. However, the main drawback of the use of such a technology is that researchers are asked to manually annotate their research papers and add their contributions within the KGs. To address this problem, in this paper we propose SCICERO, a novel KG generation approach that takes in input text from research articles and generates a KG of research entities. SCICERO uses Natural Language Processing techniques to parse the content of scientific papers to discover entities and relationships, exploits state-of-the-art Deep Learning Transformer models to make sense and validate extracted information, and uses Semantic Web best practices to formally represent the extracted entities and relationships, making the written content of research papers machine-actionable. SCICERO has been tested on a dataset of 6.7M papers about Computer Science generating a KG of about 10M entities. It has been evaluated on a manually generated gold standard of 3,600 triples that cover three Computer Science subdomains (Information Retrieval, Natural Language Processing, and Machine Learning) obtaining remarkable results.
Articolo in rivista - Articolo scientifico
Artificial intelligence; Knowledge graph; Scholarly domain; Scientific facts;
English
5-ott-2022
2022
258
22 December 2022
109945
open
Dessi, D., Osborne, F., Reforgiato Recupero, D., Buscaldi, D., Motta, E. (2022). SCICERO: A deep learning and NLP approach for generating scientific knowledge graphs in the computer science domain. KNOWLEDGE-BASED SYSTEMS, 258(22 December 2022) [10.1016/j.knosys.2022.109945].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/411695
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