A Search Engine for Scientific Publications: a Cybersecurity Case Study

• URL: http://arxiv.org/abs/2107.00082v1
• Date: Wed, 30 Jun 2021 20:10:04 GMT
• Title: A Search Engine for Scientific Publications: a Cybersecurity Case Study
• Authors: Nuno Oliveira, Norberto Sousa, Isabel Pra\c{c}a
• Abstract summary: This work proposes a new search engine for scientific publications which combines both information retrieval and reading comprehension algorithms. The proposed solution although being applied to the context of cybersecurity exhibited great generalization capabilities and can be easily adapted to perform under other distinct knowledge domains.
• Score: 0.7734726150561086
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Cybersecurity is a very challenging topic of research nowadays, as digitalization increases the interaction of people, software and services on the Internet by means of technology devices and networks connected to it. The field is broad and has a lot of unexplored ground under numerous disciplines such as management, psychology, and data science. Its large disciplinary spectrum and many significant research topics generate a considerable amount of information, making it hard for us to find what we are looking for when researching a particular subject. This work proposes a new search engine for scientific publications which combines both information retrieval and reading comprehension algorithms to extract answers from a collection of domain-specific documents. The proposed solution although being applied to the context of cybersecurity exhibited great generalization capabilities and can be easily adapted to perform under other distinct knowledge domains.

Related papers

• Examining Different Research Communities: Authorship Network [0.0]
  We collected Google Scholar data for two different research domains in computer science: Data Mining and Software Engineering. The scholar database resources are powerful for network analysis, data mining, and identify links between authors via authorship network.
  arXiv  Detail & Related papers  (2024-08-24T19:04:02Z)
• DiscipLink: Unfolding Interdisciplinary Information Seeking Process via Human-AI Co-Exploration [34.23942131024738]
  In this paper, we introduce DiscipLink, a novel interactive system that facilitates collaboration between researchers and large language models (LLMs) Based on users' topics of interest, DiscipLink initiates exploratory questions from the perspectives of possible relevant fields of study. Our evaluation, comprising a within-subject comparative experiment and an open-ended exploratory study, reveals that DiscipLink can effectively support researchers in breaking down disciplinary boundaries.
  arXiv  Detail & Related papers  (2024-08-01T10:36:00Z)
• 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)
• A Comprehensive Survey of Forgetting in Deep Learning Beyond Continual Learning [58.107474025048866]
  Forgetting refers to the loss or deterioration of previously acquired knowledge. Forgetting is a prevalent phenomenon observed in various other research domains within deep learning.
  arXiv  Detail & Related papers  (2023-07-16T16:27:58Z)
• Machine Unlearning: A Survey [56.79152190680552]
  A special need has arisen where, due to privacy, usability, and/or the right to be forgotten, information about some specific samples needs to be removed from a model, called machine unlearning. This emerging technology has drawn significant interest from both academics and industry due to its innovation and practicality. No study has analyzed this complex topic or compared the feasibility of existing unlearning solutions in different kinds of scenarios. The survey concludes by highlighting some of the outstanding issues with unlearning techniques, along with some feasible directions for new research opportunities.
  arXiv  Detail & Related papers  (2023-06-06T10:18:36Z)
• Characterising Research Areas in the field of AI [68.8204255655161]
  We identified the main conceptual themes by performing clustering analysis on the co-occurrence network of topics. The results highlight the growing academic interest in research themes like deep learning, machine learning, and internet of things.
  arXiv  Detail & Related papers  (2022-05-26T16:30:30Z)
• A Search Engine for Discovery of Biomedical Challenges and Directions [38.72769142277108]
  We construct and release an expert-annotated corpus of texts sampled from full-length papers. We focus on a large corpus of interdisciplinary work relating to the COVID-19 pandemic. We apply a model trained on our data to identify challenges and directions across the corpus and build a dedicated search engine for this information.
  arXiv  Detail & Related papers  (2021-08-31T11:08:20Z)
• Semantics for Robotic Mapping, Perception and Interaction: A Survey [93.93587844202534]
  Study of understanding dictates what does the world "mean" to a robot. With humans and robots increasingly operating in the same world, the prospects of human-robot interaction also bring semantics into the picture. Driven by need, as well as by enablers like increasing availability of training data and computational resources, semantics is a rapidly growing research area in robotics.
  arXiv  Detail & Related papers  (2021-01-02T12:34:39Z)
• 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)
• Topic Diffusion Discovery Based on Deep Non-negative Autoencoder [0.0]
  We propose using a Deep Non-negative Autoencoder with information divergence measurement to monitor topic diffusion. The proposed approach is able to identify the evolution of research topics as well as to discover topic diffusions in online fashions.
  arXiv  Detail & Related papers  (2020-10-08T00:58:10Z)

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.