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Future Internet
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Natural Language Processing (NLP) and Information Retrieval (IR) in Internet...
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Natural Language Processing (NLP) and Information Retrieval (IR) in Internet of Things

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (15 June 2023) | Viewed by 5388

Special Issue Editors

Dr. Wei Emma Zhang

E-Mail Website
Guest Editor
Department of Computer and Mathematical Sciences, The University of Adelaide, Adelaide 5005, Australia
Interests: text mining; natural language processing; information retrieval; semantic web; Internet of Things
Prof. Dr. Chenliang Li

E-Mail Website
Guest Editor
Research Center of Big Data Analysis and Artificial Intelligence (Deputy Director), School of Cyber Science and Engineering, Wuhan University, Wuhan, China
Interests: information retrieval; natural language processing; statistic learning; data mining; social media analysis and mining
Prof. Dr. Michael Sheng

E-Mail Website
Guest Editor
School of Computing, Faculty of Science & Engineering, Macquarie University, Sydney, NSW 2109, Australia
Interests: web of things; internet of things; big data analytics; web science; service-oriented computing; pervasive computing; sensor networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Natural Language Processing (NLP) and Information Retrieval (IR) have increasingly attracted advanced research. When combined with Internet-of-Thing (IoT) techniques, they have promise in real-life applications such as conversational personal assistants and IoT search engines. This Special Issue intends to provide academic and industrial communities with an excellent collection of research covering all aspects of current works on Natural Language Processing (NLP) and Information Retrieval (IR) that are applied in Internet of Things (IoT) or have great potential for application in IoT.

Potential topics include but are not limited to the following:

  • Syntactic, semantic, and context parsing and analysis for IoT;
  • Machine-translation-related tasks;
  • Question answering and chatbot development;
  • Speech synthesis and recognition;
  • Information retrieval in IoT;
  • Ontology;
  • Corpora development and evaluation;
  • Natural language generation;
  • Text and speech analysis;
  • Multi-lingual/cross-lingual approaches;
  • Text summarization for IoT.

Dr. Wei Emma Zhang
Prof. Dr. Chenliang Li
Prof. Dr. Michael Sheng
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. Future Internet is an international peer-reviewed open access monthly 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 1600 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

  • natural language processing
  • information retrieval
  • internet of things

Published Papers (3 papers)

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Research

14 pages, 1196 KiB  
Article
A Hybrid Text Generation-Based Query Expansion Method for Open-Domain Question Answering
by Wenhao Zhu, Xiaoyu Zhang, Qiuhong Zhai and Chenyun Liu
Future Internet 2023, 15(5), 180; https://doi.org/10.3390/fi15050180 - 12 May 2023
Cited by 1 | Viewed by 1412
Abstract
In the two-stage open-domain question answering (OpenQA) systems, the retriever identifies a subset of relevant passages, which the reader then uses to extract or generate answers. However, the performance of OpenQA systems is often hindered by issues such as short and semantically ambiguous [...] Read more.
In the two-stage open-domain question answering (OpenQA) systems, the retriever identifies a subset of relevant passages, which the reader then uses to extract or generate answers. However, the performance of OpenQA systems is often hindered by issues such as short and semantically ambiguous queries, making it challenging for the retriever to find relevant passages quickly. This paper introduces Hybrid Text Generation-Based Query Expansion (HTGQE), an effective method to improve retrieval efficiency. HTGQE combines large language models with Pseudo-Relevance Feedback techniques to enhance the input for generative models, improving text generation speed and quality. Building on this foundation, HTGQE employs multiple query expansion generators, each trained to provide query expansion contexts from distinct perspectives. This enables the retriever to explore relevant passages from various angles for complementary retrieval results. As a result, under an extractive and generative QA setup, HTGQE achieves promising results on both Natural Questions (NQ) and TriviaQA (Trivia) datasets for passage retrieval and reading tasks. Full article
(This article belongs to the Special Issue Natural Language Processing (NLP) and Information Retrieval (IR) in Internet of Things)
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14 pages, 3008 KiB  
Article
Contrastive Refinement for Dense Retrieval Inference in the Open-Domain Question Answering Task
by Qiuhong Zhai, Wenhao Zhu, Xiaoyu Zhang and Chenyun Liu
Future Internet 2023, 15(4), 137; https://doi.org/10.3390/fi15040137 - 31 Mar 2023
Cited by 1 | Viewed by 1184
Abstract
In recent years, dense retrieval has emerged as the primary method for open-domain question-answering (OpenQA). However, previous research often focused on the query side, neglecting the importance of the passage side. We believe that both the query and passage sides are equally important [...] Read more.
In recent years, dense retrieval has emerged as the primary method for open-domain question-answering (OpenQA). However, previous research often focused on the query side, neglecting the importance of the passage side. We believe that both the query and passage sides are equally important and should be considered for improved OpenQA performance. In this paper, we propose a contrastive pseudo-labeled data constructed around passages and queries separately. We employ an improved pseudo-relevance feedback (PRF) algorithm with a knowledge-filtering strategy to enrich the semantic information in dense representations. Additionally, we proposed an Auto Text Representation Optimization Model (AOpt) to iteratively update the dense representations. Experimental results demonstrate that our methods effectively optimize dense representations, making them more distinguishable in dense retrieval, thus improving the OpenQA system’s overall performance. Full article
(This article belongs to the Special Issue Natural Language Processing (NLP) and Information Retrieval (IR) in Internet of Things)
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12 pages, 292 KiB  
Article
ERGCN: Enhanced Relational Graph Convolution Network, an Optimization for Entity Prediction Tasks on Temporal Knowledge Graphs
by Yinglin Wang and Xinyu Xu
Future Internet 2022, 14(12), 376; https://doi.org/10.3390/fi14120376 - 13 Dec 2022
Cited by 2 | Viewed by 1569
Abstract
Reasoning on temporal knowledge graphs, which aims to infer new facts from existing knowledge, has attracted extensive attention and in-depth research recently. One of the important tasks of reasoning on temporal knowledge graphs is entity prediction, which focuses on predicting the missing objects [...] Read more.
Reasoning on temporal knowledge graphs, which aims to infer new facts from existing knowledge, has attracted extensive attention and in-depth research recently. One of the important tasks of reasoning on temporal knowledge graphs is entity prediction, which focuses on predicting the missing objects in facts at current time step when relevant histories are known. The problem is that, for entity prediction task on temporal knowledge graphs, most previous studies pay attention to aggregating various semantic information from entities but ignore the impact of semantic information from relation types. We believe that relation types is a good supplement for our task and making full use of semantic information of facts can promote the results. Therefore, a framework of Enhanced Relational Graph Convolution Network (ERGCN) is put forward in this paper. Rather than only considering representations of entities, the context semantic information of both relations and entities is considered and merged together in this framework. Experimental results show that the proposed approach outperforms the state-of-the-art methods. Full article
(This article belongs to the Special Issue Natural Language Processing (NLP) and Information Retrieval (IR) in Internet of Things)
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