An approach based on Open Research Knowledge Graph for Knowledge Acquisition from scientific papers

• URL: http://arxiv.org/abs/2308.12981v1
• Date: Wed, 23 Aug 2023 20:05:42 GMT
• Title: An approach based on Open Research Knowledge Graph for Knowledge Acquisition from scientific papers
• Authors: Azanzi Jiomekong and Sanju Tiwari
• Abstract summary: Open Research Knowledge Graph (ORKG) is a computer-assisted tool to organize key-insights extracted from research papers. It is currently used to document "food information engineering", "Tabular data to Knowledge Graph Matching" and "Question Answering" research problems and "Neuro-symbolic AI" domain.
• Score: 4.8951183832371
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: A scientific paper can be divided into two major constructs which are Metadata and Full-body text. Metadata provides a brief overview of the paper while the Full-body text contains key-insights that can be valuable to fellow researchers. To retrieve metadata and key-insights from scientific papers, knowledge acquisition is a central activity. It consists of gathering, analyzing and organizing knowledge embedded in scientific papers in such a way that it can be used and reused whenever needed. Given the wealth of scientific literature, manual knowledge acquisition is a cumbersome task. Thus, computer-assisted and (semi-)automatic strategies are generally adopted. Our purpose in this research was two fold: curate Open Research Knowledge Graph (ORKG) with papers related to ontology learning and define an approach using ORKG as a computer-assisted tool to organize key-insights extracted from research papers. This approach was used to document the "epidemiological surveillance systems design and implementation" research problem and to prepare the related work of this paper. It is currently used to document "food information engineering", "Tabular data to Knowledge Graph Matching" and "Question Answering" research problems and "Neuro-symbolic AI" domain.

Related papers

• Knowledge Navigator: LLM-guided Browsing Framework for Exploratory Search in Scientific Literature [48.572336666741194]
  We present Knowledge Navigator, a system designed to enhance exploratory search abilities. It organizes retrieved documents into a navigable, two-level hierarchy of named and descriptive scientific topics and subtopics.
  arXiv  Detail & Related papers  (2024-08-28T14:48:37Z)
• Ontology Embedding: A Survey of Methods, Applications and Resources [54.3453925775069]
  Ontologies are widely used for representing domain knowledge and meta data. One straightforward solution is to integrate statistical analysis and machine learning. Numerous papers have been published on embedding, but a lack of systematic reviews hinders researchers from gaining a comprehensive understanding of this field.
  arXiv  Detail & Related papers  (2024-06-16T14:49:19Z)
• Artificial intelligence to automate the systematic review of scientific literature [0.0]
  We present a survey of AI techniques proposed in the last 15 years to help researchers conduct systematic analyses of scientific literature. We describe the tasks currently supported, the types of algorithms applied, and available tools proposed in 34 primary studies.
  arXiv  Detail & Related papers  (2024-01-13T19:12:49Z)
• Object Recognition from Scientific Document based on Compartment Refinement Framework [2.699900017799093]
  It has become increasingly important to extract valuable information from vast resources efficiently. Current data extraction methods for scientific documents typically use rule-based (RB) or machine learning (ML) approaches. We propose a new document layout analysis framework called CTBR(Compartment & Text Blocks Refinement)
  arXiv  Detail & Related papers  (2023-12-14T15:36:49Z)
• Document AI: Benchmarks, Models and Applications [35.46858492311289]
  Document AI refers to the techniques for automatically reading, understanding, and analyzing business documents. In recent years, the popularity of deep learning technology has greatly advanced the development of Document AI. This paper briefly reviews some of the representative models, tasks, and benchmark datasets.
  arXiv  Detail & Related papers  (2021-11-16T16:43:07Z)
• CitationIE: Leveraging the Citation Graph for Scientific Information Extraction [89.33938657493765]
  We use the citation graph of referential links between citing and cited papers. We observe a sizable improvement in end-to-end information extraction over the state-of-the-art.
  arXiv  Detail & Related papers  (2021-06-03T03:00:12Z)
• A New Neural Search and Insights Platform for Navigating and Organizing AI Research [56.65232007953311]
  We introduce a new platform, AI Research Navigator, that combines classical keyword search with neural retrieval to discover and organize relevant literature. We give an overview of the overall architecture of the system and of the components for document analysis, question answering, search, analytics, expert search, and recommendations.
  arXiv  Detail & Related papers  (2020-10-30T19:12:25Z)
• Generating Knowledge Graphs by Employing Natural Language Processing and Machine Learning Techniques within the Scholarly Domain [1.9004296236396943]
  We present a new architecture that takes advantage of Natural Language Processing and Machine Learning methods for extracting entities and relationships from research publications. Within this research work, we i) tackle the challenge of knowledge extraction by employing several state-of-the-art Natural Language Processing and Text Mining tools. We generated a scientific knowledge graph including 109,105 triples, extracted from 26,827 abstracts of papers within the Semantic Web domain.
  arXiv  Detail & Related papers  (2020-10-28T08:31:40Z)
• From Standard Summarization to New Tasks and Beyond: Summarization with Manifold Information [77.89755281215079]
  Text summarization is the research area aiming at creating a short and condensed version of the original document. In real-world applications, most of the data is not in a plain text format. This paper focuses on the survey of these new summarization tasks and approaches in the real-world application.
  arXiv  Detail & Related papers  (2020-05-10T14:59:36Z)
• A Survey on Knowledge Graphs: Representation, Acquisition and Applications [89.78089494738002]
  We review research topics about 1) knowledge graph representation learning, 2) knowledge acquisition and completion, 3) temporal knowledge graph, and 4) knowledge-aware applications. For knowledge acquisition, especially knowledge graph completion, embedding methods, path inference, and logical rule reasoning, are reviewed. We explore several emerging topics, including meta learning, commonsense reasoning, and temporal knowledge graphs.
  arXiv  Detail & Related papers  (2020-02-02T13:17:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.