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Electronics
Special Issues
Intelligent Big Data Analytics and Knowledge Management
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Intelligent Big Data Analytics and Knowledge Management

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 2000

Special Issue Editors

Prof. Dr. Marcello Trovati

E-Mail Website
Guest Editor
Department of Computer Science, Edge Hill University, St Helens Road, Ormskirk, Lancashire L39 4QP, UK
Interests: data science; AI; mathematical modelling
Special Issues, Collections and Topics in MDPI journals
Dr. Umar F Khan

E-Mail Website
Guest Editor
Computer Science and Engineering Department, Edgehill University, St Helen Road, Ormskirk, Liverpool L39 4QP, UK
Interests: partial discharge; autonomous systems; condition monitoring; sensors; localization; control; robotics

Special Issue Information

Dear Colleagues,

Data analytics is essential in many business organisations, academic institutions, as well as R&D departments, as it allows them to identify, assess, and extract actionable information. In particular, data analytics plays a crucial role in innovation creation and knowledge discovery. However, they are often defined by various properties, which are context-dependent and therefore difficult to scientifically and methodologically pinpoint.  Furthermore, innovative knowledge discovery overlaps with several disciplines and research fields, which require bespoke approaches to address the common interdependencies. This Special Issue focuses on the multidisciplinary methods, frameworks and applications applied to the identification, assessment and prediction of knowledge discovery and management and innovation creation and impact.

Topics of interest in this Special Issue include, but are not limited to: 

  1. Data analytics for knowledge management and discovery;
  2. Business analytics for innovation creation;
  3. AI methods and implementation for knowledge discovery;
  4. Predictive analytics to identify, store and assess new and innovative knowledge;
  5. Innovative industry 4.0 and digital health applications.

Prof. Dr. Marcello Trovati
Dr. Umar F Khan
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • data analytics
  • knowledge management and discovery
  • innovation
  • business analytics

Published Papers (1 paper)

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Research

14 pages, 520 KiB  
Article
SS-BERT: A Semantic Information Selecting Approach for Open-Domain Question Answering
by Xuan Fu, Jiangnan Du, Hai-Tao Zheng, Jianfeng Li, Cuiqin Hou, Qiyu Zhou and Hong-Gee Kim
Electronics 2023, 12(7), 1692; https://doi.org/10.3390/electronics12071692 - 03 Apr 2023
Cited by 1 | Viewed by 1381
Abstract
Open-Domain Question Answering (Open-Domain QA) aims to answer any factoid questions from users. Recent progress in Open-Domain QA adopts the “retriever-reader” structure, which has proven effective. Retriever methods are mainly categorized as sparse retrievers and dense retrievers. In recent work, the dense retriever [...] Read more.
Open-Domain Question Answering (Open-Domain QA) aims to answer any factoid questions from users. Recent progress in Open-Domain QA adopts the “retriever-reader” structure, which has proven effective. Retriever methods are mainly categorized as sparse retrievers and dense retrievers. In recent work, the dense retriever showed a stronger semantic interpretation than the sparse retriever. When training a dual-encoder dense retriever for document retrieval and reranking, there are two challenges: negative selection and a lack of training data. In this study, we make three major contributions to this topic: negative selection by query generation, data augmentation from negatives, and a passage evaluation method. We prove that the model performs better by focusing on false negatives and data augmentation in the Open-Domain QA passage rerank task. Our model outperforms other single dual-encoder rerankers over BERT-base and BM25 by 0.7 in MRR@10, achieving the highest Recall@50 and the max Recall@1000, which is restricted by the BM25 retrieval results. Full article
(This article belongs to the Special Issue Intelligent Big Data Analytics and Knowledge Management)
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