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Advanced Semantic Technologies and Sensors

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: 30 September 2024 | Viewed by 1215

Special Issue Editor


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Guest Editor
Data Science Institute, University of Technology Sydney, Sydney, Australia
Interests: data mining; machine learning; recommender systems; fake news detection and mitigation; trustworthy AI
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce a new Special Issue on "Advanced Semantic Technologies and Sensors". This Special Issue intends to explore the intersection between semantic technologies and sensors, which has become increasingly important in recent years as the amount of data generated by sensors continues to grow.

Semantic technologies facilitate the processing and analysis of large amounts of data by providing a common vocabulary and ontology for representing and organizing information. Sensors, on the other hand, are capable of collecting vast amounts of data in real-time, providing valuable insights into the physical world.

This Special Issue aims to collate researchers and practitioners from both academia and industry to explore the potential of semantic technologies in enhancing the capabilities of sensors, as well as to showcase the latest advancements and applications in this field.

Topics of interest for this Special Issue include, but are not limited to, sensor data annotation and integration, sensor data analysis and reasoning, semantic sensor networks, sensor-driven ontology evolution, semantic technology applications in sensor-based systems, sementic modelling and understanding, knowledge graphs, as well as any topics related to semantic modelling (such as news modeling and recommendation, fake news/rumor detection and mitigation, natural language modelling and understanding, text mining and modelling, human–machine interaction via language understanding, etc.).

We invite original research articles, review articles, and case studies that present cutting-edge research and innovative solutions in the intersection of semantic technologies and sensors. We look forward to receiving your contributions.

Dr. Shoujin Wang
Guest Editor

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. Sensors 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 2600 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

  • semantics
  • natural language understanding
  • sensors
  • semantic understanding
  • knowledge graph
  • natural language processing and understanding
  • news recommendation
  • fake news detection/mitigation
  • text modeling

Published Papers (1 paper)

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Research

22 pages, 2508 KiB  
Article
Enhancing Cross-Lingual Entity Alignment in Knowledge Graphs through Structure Similarity Rearrangement
by Guiyang Liu, Canghong Jin, Longxiang Shi, Cheng Yang, Jiangbing Shuai and Jing Ying
Sensors 2023, 23(16), 7096; https://doi.org/10.3390/s23167096 - 10 Aug 2023
Cited by 1 | Viewed by 786
Abstract
Cross-lingual entity alignment in knowledge graphs is a crucial task in knowledge fusion. This task involves learning low-dimensional embeddings for nodes in different knowledge graphs and identifying equivalent entities across them by measuring the distances between their representation vectors. Existing alignment models use [...] Read more.
Cross-lingual entity alignment in knowledge graphs is a crucial task in knowledge fusion. This task involves learning low-dimensional embeddings for nodes in different knowledge graphs and identifying equivalent entities across them by measuring the distances between their representation vectors. Existing alignment models use neural network modules and the nearest neighbors algorithm to find suitable entity pairs. However, these models often ignore the importance of local structural features of entities during the alignment stage, which may lead to reduced matching accuracy. Specifically, nodes that are poorly represented may not benefit from their surrounding context. In this article, we propose a novel alignment model called SSR, which leverages the node embedding algorithm in graphs to select candidate entities and then rearranges them by local structural similarity in the source and target knowledge graphs. Our approach improves the performance of existing approaches and is compatible with them. We demonstrate the effectiveness of our approach on the DBP15k dataset, showing that it outperforms existing methods while requiring less time. Full article
(This article belongs to the Special Issue Advanced Semantic Technologies and Sensors)
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