Special Issue "Semantics in the Deep: Semantic Analytics for Big Data"

A special issue of Data (ISSN 2306-5729).

Deadline for manuscript submissions: closed (23 November 2018)

Special Issue Editors

Guest Editor
Dr. Dimitrios A. Koutsomitropoulos

Department of Computer Engineering & Informatics, University of Patras, Greece
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Interests: big and linked data; digital libraries; knowledge discovery; metadata integration; semantic web
Guest Editor
Prof. Dr. Spiridon D. Likothanassis

Department of Computer Engineering & Informatics, University of Patras, Greece
Website | E-Mail
Interests: computational intelligence; bioinformatics; web applications; e-learning systems
Guest Editor
Prof. Dr. Panos Kalnis

Computer, Electrical and Mathematical Science and Engineering Division, King Abdullah University of Science and Technology, Saudi Arabia
Website | E-Mail
Interests: big data; cloud computing; parallel and distributed systems; large graphs and long sequences

Special Issue Information

Dear Colleagues,

During the last decade, the field of Artificial Intelligence (AI) has experienced an explosion of applications and innovations, having many facets: On the one hand, there are strong symbolic representations to underpin the next generation of the Web, and the advent of the Semantic Web has a major role in giving everyday browsing tasks a blend of intelligence; and, on the other hand, deep learning techniques and achievements have been proven to tackle problems that would seem intractable some time ago. The wide availability of information on the Internet, storage space, and web-generated content put still more impetus on devising applications that would take advantage of such unprecedented resources, but would also stand up to the challenges posed by processing and value extraction out of big data. Now that big data have become everyday data, two fundamental questions naturally arise:

  • How can semantic technologies contribute towards big data analysis?
  • What is the relationship between Semantic Web logical formalisms and automated- and deep-learning techniques?

The aim of this Special Issue is to put emphasis on big data analysis and, more specifically, on how semantics-aware applications can contribute in this field. The interplay between the logical formalisms of the Semantic Web and automated learning and deep learning techniques is currently an open research topic for both technologies to achieve their next step and forms the state-of-the-art in this area. In this sense, there are numerous open problems, ranging from efficient ontological processing of big data ontologies to knowledge graphs maintenance to ontology evolvement with machine learning techniques.

Topics
Following the theme of SEDSEAL 2018, this special issue solicits contributions to the open problems above, such as innovative techniques, tools, case studies, comparisons, and theoretical advances. The papers should consider and present contributions towards how Semantic Web technologies can help to implement and enhance big data analytics. This can be achieved either by extracting value out of these data (e.g., through reasoning), creating sustainable ontology models, offering a solid foundation for deploying learning techniques or anything in between. In particular, topics of interest include, but are not limited to, the following:

  • Ontologies for big data
  • Semantic applications in big data domains including:
    • open datasets, linked data, scholarly information, e-learning
    • economics, insurance, sensors, bioinformatics
  • Reasoning approaches for knowledge extraction
  • Ontology learning and topic modeling
  • NLP and word embedding
  • Semantic deep learning
  • Semantic lakes and blockchain
  • OBDA approaches for big data access
  • Data science and semantics
  • Evaluation techniques
  • Semantic deep learning
    • Ontologies as training sets
    • Ontology evolution and learning feedback
  • Scalability issues

Dr. Dimitrios A. Koutsomitropoulos
Prof. Dr. Spiridon D. Likothanassis
Prof. Dr. Panos Kalnis
Guest Editors

Editorial Review Board (TBC)

Andreas Andreou, Cyprus University of Technology, Cyprus
Christos Alexakos, University of Patras, Greece
Dimitrios Tsolis, University of Patras, Greece
Dimitrios Tzovaras, CERTH/ITI, Greece
Efstratios Georgopoulos, Technological Institute of Kalamata, Greece
Filipe Portela, University of Minho, Portugal
Jouni Tuominen, University of Helsinki, Finland
Konstantinos Votis, CERTH/ITI, Greece
Miguel-Angel Sicilia, University of Alcala, Spain
Minjuan Wang, San Diego State University, USA
Vassilis Plagianakos, University of Thessaly, Greece

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 papers will be 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.

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. Data is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) is waived for well-prepared manuscripts submitted to this issue. 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

  • Big Data
  • Ontologies
  • Reasoning
  • Deep learning
  • Analytics
  • Semantic Web
  • Data Science
  • Artificial Intelligence

Published Papers (4 papers)

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Editorial

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Open AccessEditorial
Semantics in the Deep: Semantic Analytics for Big Data
Received: 25 April 2019 / Accepted: 6 May 2019 / Published: 7 May 2019
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Abstract
One cannot help but classify the continuous birth and demise of Artificial Intelligence (AI) trends into the everlasting theme of the battle between connectionist and symbolic AI [...] Full article
(This article belongs to the Special Issue Semantics in the Deep: Semantic Analytics for Big Data)

Research

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Open AccessArticle
From a Smoking Gun to Spent Fuel: Principled Subsampling Methods for Building Big Language Data Corpora from Monitor Corpora
Received: 29 November 2018 / Revised: 22 March 2019 / Accepted: 30 March 2019 / Published: 2 April 2019
Cited by 1 | PDF Full-text (2755 KB) | HTML Full-text | XML Full-text
Abstract
With the influence of Big Data culture on qualitative data collection, acquisition, and processing, it is becoming increasingly important that social scientists understand the complexity underlying data collection and the resulting models and analyses. Systematic approaches for creating computationally tractable models need to [...] Read more.
With the influence of Big Data culture on qualitative data collection, acquisition, and processing, it is becoming increasingly important that social scientists understand the complexity underlying data collection and the resulting models and analyses. Systematic approaches for creating computationally tractable models need to be employed in order to create representative, specialized reference corpora subsampled from Big Language Data sources. Even more importantly, any such method must be tested and vetted for its reproducibility and consistency in generating a representative model of a particular population in question. This article considers and tests one such method for Big Language Data downsampling of digitally accessible language data to determine both how to operationalize this form of corpus model creation, as well as testing whether the method is reproducible. Using the U.S. Nuclear Regulatory Commission’s public documentation database as a test source, the sampling method’s procedure was evaluated to assess variation in the rate of which documents were deemed fit for inclusion or exclusion from the corpus across four iterations. After performing multiple sampling iterations, the approach pioneered by the Tobacco Documents Corpus creators was deemed to be reproducible and valid using a two-proportion z-test at a 99% confidence interval at each stage of the evaluation process–leading to a final mean rejection ratio of 23.5875 and variance of 0.891 for the documents sampled and evaluated for inclusion into the final text-based model. The findings of this study indicate that such a principled sampling method is viable, thus necessitating the need for an approach for creating language-based models that account for extralinguistic factors and linguistic characteristics of documents. Full article
(This article belongs to the Special Issue Semantics in the Deep: Semantic Analytics for Big Data)
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Open AccessArticle
CRC806-KB: A Semantic MediaWiki Based Collaborative Knowledge Base for an Interdisciplinary Research Project
Received: 31 August 2018 / Revised: 2 October 2018 / Accepted: 20 October 2018 / Published: 25 October 2018
Cited by 1 | PDF Full-text (2960 KB) | HTML Full-text | XML Full-text
Abstract
In the frame of an interdisciplinary research project that is concerned with data from heterogeneous domains, such as archaeology, cultural sciences, and the geosciences, a web-based Knowledge Base system was developed to facilitate and improve research collaboration between the project participants. The presented [...] Read more.
In the frame of an interdisciplinary research project that is concerned with data from heterogeneous domains, such as archaeology, cultural sciences, and the geosciences, a web-based Knowledge Base system was developed to facilitate and improve research collaboration between the project participants. The presented system is based on a Wiki that was enhanced with a semantic extension, which enables to store and query structured data within the Wiki. Using an additional open source tool for Schema–Driven Development of the data model, and the structure of the Knowledge Base, improved the collaborative data model development process, as well as semi-automation of data imports and updates. The paper presents the system architecture, as well as some example applications of a collaborative Wiki based Knowledge Base infrastructure. Full article
(This article belongs to the Special Issue Semantics in the Deep: Semantic Analytics for Big Data)
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Review

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Open AccessReview
Neural Networks in Big Data and Web Search
Received: 4 November 2018 / Revised: 24 December 2018 / Accepted: 24 December 2018 / Published: 30 December 2018
Cited by 1 | PDF Full-text (3151 KB) | HTML Full-text | XML Full-text
Abstract
As digitalization is gradually transforming reality into Big Data, Web search engines and recommender systems are fundamental user experience interfaces to make the generated Big Data within the Web as visible or invisible information to Web users. In addition to the challenge of [...] Read more.
As digitalization is gradually transforming reality into Big Data, Web search engines and recommender systems are fundamental user experience interfaces to make the generated Big Data within the Web as visible or invisible information to Web users. In addition to the challenge of crawling and indexing information within the enormous size and scale of the Internet, e-commerce customers and general Web users should not stay confident that the products suggested or results displayed are either complete or relevant to their search aspirations due to the commercial background of the search service. The economic priority of Web-related businesses requires a higher rank on Web snippets or product suggestions in order to receive additional customers. On the other hand, web search engine and recommender system revenue is obtained from advertisements and pay-per-click. The essential user experience is the self-assurance that the results provided are relevant and exhaustive. This survey paper presents a review of neural networks in Big Data and web search that covers web search engines, ranking algorithms, citation analysis and recommender systems. The use of artificial intelligence (AI) based on neural networks and deep learning in learning relevance and ranking is also analyzed, including its utilization in Big Data analysis and semantic applications. Finally, the random neural network is presented with its practical applications to reasoning approaches for knowledge extraction. Full article
(This article belongs to the Special Issue Semantics in the Deep: Semantic Analytics for Big Data)
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