Generating Knowledge Graphs by Employing Natural Language Processing and Machine Learning Techniques within the Scholarly Domain
@article{Dess2020GeneratingKG, title={Generating Knowledge Graphs by Employing Natural Language Processing and Machine Learning Techniques within the Scholarly Domain}, author={Danilo Dess{\'i} and Francesco Osborne and Diego Reforgiato Recupero and D. Buscaldi and Enrico Motta}, journal={Future Gener. Comput. Syst.}, year={2020}, volume={116}, pages={253-264} }
29 Citations
Completing Scientific Facts in Knowledge Graphs of Research Concepts
- Computer ScienceIEEE Access
- 2022
This paper introduces SciCheck, a new triple classification approach for completing scientific statements in knowledge graphs and provides a real-world use case and applies it to the Artificial Intelligence Knowledge Graph (AI-KG).
AI-KG: An Automatically Generated Knowledge Graph of Artificial Intelligence
- Computer ScienceSEMWEB
- 2020
The Artificial Intelligence Knowledge Graph (AI-KG), a large-scale automatically generated knowledge graph that describes 820K research entities, has been generated by applying an automatic pipeline that extracts entities and relationships using three tools: DyGIE++, Stanford CoreNLP, and the CSO Classifier.
Scholarly knowledge graphs through structuring scholarly communication: a review
- Computer ScienceComplex & intelligent systems
- 2022
This review paper investigates the field of applying machine learning, rule-based learning, and natural language processing tools and approaches to construct SKG and presents the review of knowledge graph utilization and refinement to provide a view of current research efforts.
CS-KG: A Large-Scale Knowledge Graph of Research Entities and Claims in Computer Science
- Computer ScienceSEMWEB
- 2022
The Computer Science Knowledge Graph (CS-KG) is introduced, a large-scale knowledge graph composed by over 350 M RDF triples describing 41 M statements from 6 .7 M articles about 10 M entities linked by 179 semantic relations that provides a very comprehensive representation of tasks, methods, materials, and metrics in Computer Science.
Taxonomy Enrichment with Text and Graph Vector Representations
- Computer ScienceSemantic Web
- 2021
This paper addresses the problem of taxonomy enrichment which aims at adding new words to the existing taxonomy with a new method which allows achieving high results on this task with little effort and creates a number of datasets for taxonomy extension for English and Russian.
Scientific Knowledge Graph-driven Research Profiling
- Computer ScienceCSAE
- 2022
A research profiling framework driven by scientific knowledge graphs (SKGs), which aims at achieving the deep fusion and thorough disclosure of scientific resources and domain knowledge, as well as satisfying the needs of researchers for knowledge overview and acquisition in a reasonable period of time using fine-grained and multi-dimensional profiling scenarios.
Detection, Analysis, and Prediction of Research Topics with Scientific Knowledge Graphs
- Computer SciencePredicting the Dynamics of Research Impact
- 2021
This chapter presents an innovative framework for detecting, analysing, and forecasting research topics based on a large-scale knowledge graph characterising research articles according to the research topics from the Computer Science Ontology.
K-LM: Knowledge Augmenting in Language Models Within the Scholarly Domain
- Computer ScienceIEEE Access
- 2022
This work provides a Knowledge Language Model (K-LM) to use the Resource Description Framework (RDF) triples directly, extracted from world knowledge bases and introduces heuristic methods to inject domain-specific knowledge in K-LM, leveraging knowledge graphs (KGs).
CSO Classifier 3.0: a scalable unsupervised method for classifying documents in terms of research topics
- Computer ScienceInt. J. Digit. Libr.
- 2022
This new version of the CSO Classifier includes a new component for discarding outlier topics and offers improved scalability, and is evaluated on a gold standard of manually annotated articles, demonstrating a significant improvement over alternative methods.
CSO Classifier 3.0: a scalable unsupervised method for classifying documents in terms of research topics
- Computer ScienceInternational Journal on Digital Libraries
- 2021
This new version of the CSO Classifier includes a new component for discarding outlier topics and offers improved scalability, and is evaluated on a gold standard of manually annotated articles, demonstrating a significant improvement over alternative methods.
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