A Responsive Framework for Research Portals Data using Semantic Web Technology

• URL: http://arxiv.org/abs/2306.11642v1
• Date: Tue, 20 Jun 2023 16:12:33 GMT
• Title: A Responsive Framework for Research Portals Data using Semantic Web Technology
• Authors: Muhammad Zohaib
• Abstract summary: The research aims to address this issue by designing a framework for the semantic organization of research portal data. The framework focuses on the extraction of information from two specific research portals, namely Microsoft Academic and IEEE Xplore.
• Score: 0.6798775532273751
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As the amount of data on the World Wide Web continues to grow exponentially, access to semantically structured information remains limited. The Semantic Web has emerged as a solution to enhance the machine-readability of data, making it significantly more accessible and interpretable. Various techniques, such as web scraping and mapping, have been employed by different websites to provide semantic access. Web scraping involves the extraction of valuable information from diverse data sources, such as the World Wide Web, utilizing powerful string manipulation operations.In the research field, researchers face the challenge of collecting relevant data from multiple sources, which requires substantial time and effort. This research aims to address this issue by designing a framework for the semantic organization of research portal data. The framework focuses on the extraction of information from two specific research portals, namely Microsoft Academic and IEEE Xplore. Its primary objective is to gather diverse research-related data from these targeted sources.By implementing this framework, researchers can streamline the process of collecting valuable information for their work, saving time and effort. The semantic organization of research portal data offers enhanced accessibility and interpretability, facilitating more effective and efficient knowledge discovery. This research contributes to the advancement of research data management and promotes the utilization of semantic web technologies in the academic community.

Related papers

• Web Scraping for Research: Legal, Ethical, Institutional, and Scientific Considerations [11.851771490297693]
  This paper proposes a comprehensive framework for web scraping in social science research for U.S.-based researchers. We present an overview of the current regulatory environment impacting when and how researchers can access, collect, store, and share data via scraping. We then provide researchers with recommendations to conduct scraping in a scientifically legitimate and ethical manner.
  arXiv  Detail & Related papers  (2024-10-30T20:20:44Z)
• Data-Centric AI in the Age of Large Language Models [51.20451986068925]
  This position paper proposes a data-centric viewpoint of AI research, focusing on large language models (LLMs) We make the key observation that data is instrumental in the developmental (e.g., pretraining and fine-tuning) and inferential stages (e.g., in-context learning) of LLMs. We identify four specific scenarios centered around data, covering data-centric benchmarks and data curation, data attribution, knowledge transfer, and inference contextualization.
  arXiv  Detail & Related papers  (2024-06-20T16:34:07Z)
• MS MARCO Web Search: a Large-scale Information-rich Web Dataset with Millions of Real Click Labels [95.48844474720798]
  We introduce MS MARCO Web Search, the first large-scale information-rich web dataset. This dataset mimics real-world web document and query distribution. MS MARCO Web Search offers a retrieval benchmark with three web retrieval challenge tasks.
  arXiv  Detail & Related papers  (2024-05-13T07:46:44Z)
• The Web Can Be Your Oyster for Improving Large Language Models [98.72358969495835]
  Large language models (LLMs) encode a large amount of world knowledge. We consider augmenting LLMs with the large-scale web using search engine. We present a web-augmented LLM UNIWEB, which is trained over 16 knowledge-intensive tasks in a unified text-to-text format.
  arXiv  Detail & Related papers  (2023-05-18T14:20:32Z)
• Assessing Scientific Contributions in Data Sharing Spaces [64.16762375635842]
  This paper introduces the SCIENCE-index, a blockchain-based metric measuring a researcher's scientific contributions. To incentivize researchers to share their data, the SCIENCE-index is augmented to include a data-sharing parameter. Our model is evaluated by comparing the distribution of its output for geographically diverse researchers to that of the h-index.
  arXiv  Detail & Related papers  (2023-03-18T19:17:47Z)
• Advanced Data Augmentation Approaches: A Comprehensive Survey and Future directions [57.30984060215482]
  We provide a background of data augmentation, a novel and comprehensive taxonomy of reviewed data augmentation techniques, and the strengths and weaknesses (wherever possible) of each technique. We also provide comprehensive results of the data augmentation effect on three popular computer vision tasks, such as image classification, object detection and semantic segmentation.
  arXiv  Detail & Related papers  (2023-01-07T11:37:32Z)
• 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)
• Knowledge Graph Induction enabling Recommending and Trend Analysis: A Corporate Research Community Use Case [11.907821975089064]
  We present a use case of how a corporate research community, IBM Research, utilizes Semantic Web technologies to induce a unified Knowledge Graph. We identify a set of common patterns for exploiting the induced knowledge and exposed them as APIs. Those patterns were born out of user research which identified the most valuable use cases or user pain points to be alleviated.
  arXiv  Detail & Related papers  (2022-07-11T20:51:28Z)
• A Crawler Architecture for Harvesting the Clear, Social, and Dark Web for IoT-Related Cyber-Threat Intelligence [1.1661238776379117]
  The clear, social, and dark web have lately been identified as rich sources of valuable cyber-security information. We present a novel crawling architecture for transparently harvesting data from security websites in the clear web, security forums in the social web, and hacker forums/marketplaces in the dark web.
  arXiv  Detail & Related papers  (2021-09-14T19:26:08Z)
• 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)
• Coupling semantic and statistical techniques for dynamically enriching web ontologies [0.0]
  We propose an automatic coupled statistical/semantic framework for dynamically enriching large-scale generic from the World Wide Web. The benefits of our approach are: (i) proposing the dynamic enrichment of large-scale semantic patterns with missing background knowledge, and thus, enabling the reuse of such knowledge.
  arXiv  Detail & Related papers  (2020-04-23T11:21:30Z)

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.