Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.5
2.7
Journals
Applied Sciences
Special Issues
Knowledge Maps Applications and Future Perspectives
share announcement

Knowledge Maps Applications and Future Perspectives

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (31 October 2022) | Viewed by 3785

Special Issue Editors

Dr. Luigi Di Caro

E-Mail Website
Guest Editor
Department of Computer Science, University of Turin, Torino, Italy
Interests: data mining; computational linguistics; legal informatics; visualization and human interaction; analysis of collaborative networks; social media mining
Dr. Claudio Schifanella

E-Mail Website
Guest Editor
Department of Computer Science, University of Turin, Torino, Italy
Interests: data mining; machine learning; knowledge representation; geoinformatics; blockchain technologies

Special Issue Information

Dear Colleagues,

We are inviting submissions to this Special Issue on “Knowledge Maps Applications and Future Perspectives”.

Knowledge maps are often the keystone of the most advanced frameworks and applications thanks to their ability to integrate different data sources and bridge the semantic gap between different types of data, both structured and unstructured.

The availability of knowledge maps will enable new possibilities to query, explore, visualize, and interact with information.

This Special Issue is aimed at fostering new research on the concept of “knowledge maps”, i.e., mechanisms and models to represent large-scale information sources and data to demonstrate how knowledge maps can be leveraged in different tasks such as information retrieval, exploration, detection, and classification of data.

Today, the notion of “Big Data” and its related dimensions and problems touch many aspects of very different scientific areas. The idea of this issue is to integrate ideas from different communities and approaches, with the main objective of fostering shared views on the topic.

In this Special Issue, we invite submissions exploring cutting-edge research and recent advances demonstrating how different communities and research fields (such as information retrieval and exploration, recommendation systems, etc.) can benefit from the availability of knowledge maps. Domains and applications may include different kinds of data and scenarios, from geographic information to text-based document collections such as scientific texts or social media content, as well as images and videos.

Dr. Luigi Di Caro
Dr. Claudio Schifanella
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. Applied Sciences 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

  • K. maps applications and tools
  • K. maps for information retrieval
  • K. maps for information exploration and visualization
  • K. maps for information personalization and human–computer interaction
  • K. maps based on graphs embeddings and neural networks
  • K. maps for spatial data and geographic information
  • K. maps in crowdsourcing-based systems
  • K. maps for urban computing and urban informatics
  • K. maps for natural language technologies and lexical resources
  • K. maps for scientific literature understanding and tracking
  • K. maps for social media mining and sentiment analysis
  • K. maps for recommendation systems
  • K. maps for advanced conversational systems
  • K. maps for data integration
  • K. maps in industry and in the healthcare sector

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

16 pages, 859 KiB  
Article
Sentence Graph Attention for Content-Aware Summarization
by Giovanni Siragusa and Livio Robaldo
Appl. Sci. 2022, 12(20), 10382; https://doi.org/10.3390/app122010382 - 14 Oct 2022
Cited by 1 | Viewed by 1297
Abstract
Neural network-based encoder–decoder (ED) models are widely used for abstractive text summarization. While the encoder first reads the source document and embeds salient information, the decoder starts from such encoding to generate the summary word-by-word. However, the drawback of the ED model is [...] Read more.
Neural network-based encoder–decoder (ED) models are widely used for abstractive text summarization. While the encoder first reads the source document and embeds salient information, the decoder starts from such encoding to generate the summary word-by-word. However, the drawback of the ED model is that it treats words and sentences equally, without discerning the most relevant ones from the others. Many researchers have investigated this problem and provided different solutions. In this paper, we define a sentence-level attention mechanism based on the well-known PageRank algorithm to find the relevant sentences, then propagate the resulting scores into a second word-level attention layer. We tested the proposed model on the well-known CNN/Dailymail dataset, and found that it was able to generate summaries with a much higher abstractive power than state-of-the-art models, in spite of an unavoidable (but slight) decrease in terms of the Rouge scores. Full article
(This article belongs to the Special Issue Knowledge Maps Applications and Future Perspectives)
►▼ Show Figures

Figure 1

15 pages, 1142 KiB  
Article
Citation Oriented AuthorRank for Scientific Publication Ranking
by Jinsong Zhang and Xiaozhong Liu
Appl. Sci. 2022, 12(9), 4345; https://doi.org/10.3390/app12094345 - 25 Apr 2022
Cited by 3 | Viewed by 1362
Abstract
It is now generally accepted that an article written by influential authors often deserves a higher ranking in information retrieval. However, it is a challenging task to determine an author’s relative influence since information about the author is, much of the time, inaccessible. [...] Read more.
It is now generally accepted that an article written by influential authors often deserves a higher ranking in information retrieval. However, it is a challenging task to determine an author’s relative influence since information about the author is, much of the time, inaccessible. Actually, in scientific publications, the author is an important metadata item, which has been widely used in previous studies. In this paper, we bring an optimized AuthorRank, which is a topic-sensitive algorithm calculated by citation context, into citation analysis for testing whether and how topical AuthorRank can replace or enhance classical PageRank for publication ranking. For this purpose, we first propose a PageRank with Priors (PRP) algorithm to rank publications and authors. PRP is an optimized PageRank algorithm supervised by the Labeled Latent Dirichlet Allocation (Labeled-LDA) topic model with full-text information extraction. We then compared four methods of generating an AuthorRank score, looking, respectively, at the first author, the last author, the most famous author, and the “average” author (of a publication). Additionally, two combination methods (Linear and Cobb–Douglas) of AuthorRank and PRP were compared with several baselines. Finally, as shown in our evaluation results, the performance of AuthorRank combined with PRP is better (p < 0.001) than other baselines for publication ranking. Full article
(This article belongs to the Special Issue Knowledge Maps Applications and Future Perspectives)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop