Special Issue "Semantic Web Technology and Recommender Systems"

Special Issue Editors

Dr. Konstantinos Kotis
Website SciProfiles
Guest Editor
Department of Cultural Informatics and Communication, University of the Aegean, Lesbos 811 00, Greece
Interests: semantic web technologies; knowledge engineering; knowledge graphs; semantic data management; linked data; Semantic Web of Things; AI chatbots
Dr. Dimitris Spiliotopoulos
Website SciProfiles
Guest Editor
Informatics and Telecommunications, University of the Peloponnese, Tripoli 221 00, Greece
Interests: recommender systems; usability; social media analysis; human–computer interaction

Special Issue Information

Dear Colleagues,

Semantic web technologies define and analyse web data, linked or not, to enable semantic interconnection. This allows data analysts, application designers and cross-domain experts (linguists, cognitive scientists, machine learning experts, user interface designers) to utilise data semantics to build and work on approaches and ideas that require a deep understanding of the data at hand. Data-driven methods in computation and especially in recommender systems analyse single-source big data to identify and select recommendable content for users and applications. Multi-source data are a larger challenge. Such data are of immense value to understanding the user expectations and redefining the goals for content recommendation. The challenge is that combining data from distinct sources and for an undefined or unknown original target has to go through a layer of data understanding. Advanced data management and knowledge graphs are potential means of achieving the interlinking of data from original, social, cognitive and world sources.

This Special Issue will present the state-of-the-art in:

  • semantic web methods and tools for advanced data analysis
  • design and development of social data-driven applications
  • intelligent analysis of complex data
  • linguistic and psychological analysis of data
  • human factors and the semantics of language communication
  • methods for the enrichment of recommendation systems
  • deep learning techniques for identifying and recommending content
  • models, tools and methods that assist or supplement recommender systems
  • privacy and security for semantic data management
  • big data analytics for recommendation systems
  • analytics and recommendation systems for semantic trajectories
  • semantic sentiment analysis of big social data
  • social and semantic web applications to politics
  • social and semantic web applications to terrorism
  • social and semantic web applications to psychology
  • social and semantic web applications to societal issues

Dr. Konstantinos Kotis
Dr. Dimitris Spiliotopoulos
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 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. 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. Big Data and Cognitive Computing 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) for publication in this open access journal is 1000 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

  • semantic web
  • semantics
  • recommender system
  • recommendation method
  • big data analytics
  • sentiment analysis
  • social web

Published Papers (1 paper)

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Research

Open AccessArticle
Keyword Search over RDF: Is a Single Perspective Enough?
Big Data Cogn. Comput. 2020, 4(3), 22; https://doi.org/10.3390/bdcc4030022 - 27 Aug 2020
Abstract
Since the task of accessing RDF datasets through structured query languages like SPARQL is rather demanding for ordinary users, there are various approaches that attempt to exploit the simpler and widely used keyword-based search paradigm. However this task is challenging since there [...] Read more.
Since the task of accessing RDF datasets through structured query languages like SPARQL is rather demanding for ordinary users, there are various approaches that attempt to exploit the simpler and widely used keyword-based search paradigm. However this task is challenging since there is no clear unit of retrieval and presentation, the user information needs are in most cases not clearly formulated, the underlying RDF datasets are in most cases incomplete, and there is not a single presentation method appropriate for all kinds of information needs. As a means to alleviate these problems, in this paper we investigate an interaction approach that offers multiple presentation methods of the search results (multiple-perspectives), allowing the user to easily switch between these perspectives and thus exploit the added value that each such perspective offers. We focus on a set of fundamental perspectives, we discuss the benefits from each one, we compare this approach with related existing systems and report the results of a task-based evaluation with users. The key finding of the task-based evaluation is that users not familiar with RDF (a) managed to complete the information-seeking tasks (with performance very close to that of the experienced users), and (b) they rated positively the approach. Full article
(This article belongs to the Special Issue Semantic Web Technology and Recommender Systems)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Semantic immigration trajectories’ visual analytics for efficient policy recommendation
Authors: Maria I. Karathanasi, Konstantinos I. Kotis
Author Affiliations: Intelligent Systems Lab, University of the Aegean, Department of Cultural Technology and Communication
Email: [email protected], [email protected]
Abstract: To improve the understanding of the characteristics of the immigrant population and to understand the long-term effects of migration on immigrants and receiving societies better, there is need to exploit existing related datasets in new ways, moving from correlation to seeking the causation. Available data related to moving entities (refugees & asylum seekers, immigrants), together with static/historical or dynamic data of societies and supportive parties (e.g., NGOs, UNHCR, FRONTEX), can be processed in an integrated way in order to infer and visualize high-level spatiotemporally-constrained information and knowledge, supporting eventually advanced immigration policy making. The aim of this critical review paper is threefold: (a) to introduce the state-of-the-art modeling of interlinked data, semantic trajectories, visual analytics and policy recommendation systems in the immigration domain, (b) to highlight the challenges and open issues concerning the aforementioned, and (c) based on the identified challenges/open issues, to propose a novel approach and a system architecture to policy recommendation. Specifically, the proposed research work will result to an ontology-based system for the integration of heterogeneous immigration-related data, the visual analysis of high-level knowledge inferred, and the use of such knowledge for policy recommendation, facilitating policy makers to more efficient access of new in-depth knowledge on immigration characteristics and their impact on the societies affected.

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