The CSO Classifier: Ontology-Driven Detection of Research Topics in Scholarly Articles

• URL: http://arxiv.org/abs/2104.00948v1
• Date: Fri, 2 Apr 2021 09:02:32 GMT
• Title: The CSO Classifier: Ontology-Driven Detection of Research Topics in Scholarly Articles
• Authors: Angelo A. Salatino, Francesco Osborne, Thiviyan Thanapalasingam, Enrico Motta
• Abstract summary: We present a new unsupervised approach for automatically classifying research papers according to the Computer Science Ontology (CSO) The CSO takes as input the metadata associated with a research paper (title, abstract, keywords) and returns a selection of research concepts drawn from the ontology. The approach was evaluated on a gold standard of manually annotated articles yielding a significant improvement over alternative methods.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Classifying research papers according to their research topics is an important task to improve their retrievability, assist the creation of smart analytics, and support a variety of approaches for analysing and making sense of the research environment. In this paper, we present the CSO Classifier, a new unsupervised approach for automatically classifying research papers according to the Computer Science Ontology (CSO), a comprehensive ontology of re-search areas in the field of Computer Science. The CSO Classifier takes as input the metadata associated with a research paper (title, abstract, keywords) and returns a selection of research concepts drawn from the ontology. The approach was evaluated on a gold standard of manually annotated articles yielding a significant improvement over alternative methods.

Related papers

• Automating Bibliometric Analysis with Sentence Transformers and Retrieval-Augmented Generation (RAG): A Pilot Study in Semantic and Contextual Search for Customized Literature Characterization for High-Impact Urban Research [2.1728621449144763]
  Bibliometric analysis is essential for understanding research trends, scope, and impact in urban science. Traditional methods, relying on keyword searches, often fail to uncover valuable insights not explicitly stated in article titles or keywords. We leverage Generative AI models, specifically transformers and Retrieval-Augmented Generation (RAG), to automate and enhance bibliometric analysis.
  arXiv  Detail & Related papers  (2024-10-08T05:13:27Z)
• A Survey on Knowledge Organization Systems of Research Fields: Resources and Challenges [0.0]
  Knowledge Organization Systems (KOSs) play a fundamental role in categorising, managing, and retrieving information. This paper aims to present a comprehensive survey of the current KOS for academic disciplines. We analysed 45 KOSs according to five main dimensions: scope, structure, usage, and links to other KOSs.
  arXiv  Detail & Related papers  (2024-09-06T17:54:43Z)
• Reconciling Methodological Paradigms: Employing Large Language Models as Novice Qualitative Research Assistants in Talent Management Research [1.0949553365997655]
  This study proposes a novel approach by leveraging Retrieval Augmented Generation (RAG) based Large Language Models (LLMs) for analyzing interview transcripts. The novelty of this work lies in strategizing the research inquiry as one that is augmented by an LLM that serves as a novice research assistant. Our findings demonstrate that the LLM-augmented RAG approach can successfully extract topics of interest, with significant coverage compared to manually generated topics.
  arXiv  Detail & Related papers  (2024-08-20T17:49:51Z)
• Retrieval-Enhanced Machine Learning: Synthesis and Opportunities [60.34182805429511]
  Retrieval-enhancement can be extended to a broader spectrum of machine learning (ML) This work introduces a formal framework of this paradigm, Retrieval-Enhanced Machine Learning (REML), by synthesizing the literature in various domains in ML with consistent notations which is missing from the current literature. The goal of this work is to equip researchers across various disciplines with a comprehensive, formally structured framework of retrieval-enhanced models, thereby fostering interdisciplinary future research.
  arXiv  Detail & Related papers  (2024-07-17T20:01:21Z)
• 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)
• SurveyAgent: A Conversational System for Personalized and Efficient Research Survey [50.04283471107001]
  This paper introduces SurveyAgent, a novel conversational system designed to provide personalized and efficient research survey assistance to researchers. SurveyAgent integrates three key modules: Knowledge Management for organizing papers, Recommendation for discovering relevant literature, and Query Answering for engaging with content on a deeper level. Our evaluation demonstrates SurveyAgent's effectiveness in streamlining research activities, showcasing its capability to facilitate how researchers interact with scientific literature.
  arXiv  Detail & Related papers  (2024-04-09T15:01:51Z)
• Incremental hierarchical text clustering methods: a review [49.32130498861987]
  This study aims to analyze various hierarchical and incremental clustering techniques. The main contribution of this research is the organization and comparison of the techniques used by studies published between 2010 and 2018 that aimed to texts documents clustering.
  arXiv  Detail & Related papers  (2023-12-12T22:27:29Z)
• Systematic Analysis of COVID-19 Ontologies [5.286727853896068]
  The study is conducted through a dual-stage approach, commencing with a systematic review of relevant literature. Twenty-four COVID-19 Ontologies (CovOs) are selected and examined. The METHONTOLOGY approach emerges as a favored design methodology, often coupled with application-based or data-centric evaluation methods.
  arXiv  Detail & Related papers  (2023-09-15T18:17:01Z)
• Research Trends and Applications of Data Augmentation Algorithms [77.34726150561087]
  We identify the main areas of application of data augmentation algorithms, the types of algorithms used, significant research trends, their progression over time and research gaps in data augmentation literature. We expect readers to understand the potential of data augmentation, as well as identify future research directions and open questions within data augmentation research.
  arXiv  Detail & Related papers  (2022-07-18T11:38:32Z)
• Research Scholar Interest Mining Method based on Load Centrality [15.265191824669555]
  This paper proposes a research scholar interest mining algorithm based on load centrality. The regional structure of each topic can be used to closely calculate the weight of the centrality research model of the node. The scientific research cooperation based on the load rate center proposed in this paper can effectively extract the interests of scientific research scholars.
  arXiv  Detail & Related papers  (2022-03-21T04:16:46Z)
• 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)

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.