Special Issue "Semantic Web Technology and Recommender Systems"

A special issue of Big Data and Cognitive Computing (ISSN 2504-2289).

Deadline for manuscript submissions: 30 June 2022.

Special Issue Editors

Dr. Konstantinos Kotis
E-Mail Website
Guest Editor
Department of Cultural Informatics and Communication, University of the Aegean, 811 00 Lesbos, Greece
Interests: semantic web technologies; knowledge engineering; knowledge graphs; semantic data management; linked data; Semantic Web of Things; AI chatbots
Dr. Dimitris Spiliotopoulos
E-Mail Website
Guest Editor
Informatics and Telecommunications, University of the Peloponnese, 221 00 Tripoli, 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 1400 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 (4 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Article
GeoLOD: A Spatial Linked Data Catalog and Recommender
Big Data Cogn. Comput. 2021, 5(2), 17; https://doi.org/10.3390/bdcc5020017 - 19 Apr 2021
Viewed by 2097
Abstract
The increasing availability of linked data poses new challenges for the identification and retrieval of the most appropriate data sources that meet user needs. Recent dataset catalogs and recommenders provide advanced methods that facilitate linked data search, but none exploits the spatial characteristics [...] Read more.
The increasing availability of linked data poses new challenges for the identification and retrieval of the most appropriate data sources that meet user needs. Recent dataset catalogs and recommenders provide advanced methods that facilitate linked data search, but none exploits the spatial characteristics of datasets. In this paper, we present GeoLOD, a web catalog of spatial datasets and classes and a recommender for spatial datasets and classes possibly relevant for link discovery processes. GeoLOD Catalog parses, maintains and generates metadata about datasets and classes provided by SPARQL endpoints that contain georeferenced point instances. It offers text and map-based search functionality and dataset descriptions in GeoVoID, a spatial dataset metadata template that extends VoID. GeoLOD Recommender pre-computes and maintains, for all identified spatial classes in the Web of Data (WoD), ranked lists of classes relevant for link discovery. In addition, the on-the-fly Recommender allows users to define an uncatalogued SPARQL endpoint, a GeoJSON or a Shapefile and get class recommendations in real time. Furthermore, generated recommendations can be automatically exported in SILK and LIMES configuration files in order to be used for a link discovery task. In the results, we provide statistics about the status and potential connectivity of spatial datasets in the WoD, we assess the applicability of the recommender, and we present the outcome of a system usability study. GeoLOD is the first catalog that targets both linked data experts and geographic information systems professionals, exploits geographical characteristics of datasets and provides an exhaustive list of WoD spatial datasets and classes along with class recommendations for link discovery. Full article
(This article belongs to the Special Issue Semantic Web Technology and Recommender Systems)
Show Figures

Figure 1

Article
ParlTech: Transformation Framework for the Digital Parliament
Big Data Cogn. Comput. 2021, 5(1), 15; https://doi.org/10.3390/bdcc5010015 - 15 Mar 2021
Cited by 2 | Viewed by 3153
Abstract
Societies are entering the age of technological disruption, which also impacts governance institutions such as parliamentary organizations. Thus, parliaments need to adjust swiftly by incorporating innovative methods into their organizational culture and novel technologies into their working procedures. Inter-Parliamentary Union World e-Parliament Reports [...] Read more.
Societies are entering the age of technological disruption, which also impacts governance institutions such as parliamentary organizations. Thus, parliaments need to adjust swiftly by incorporating innovative methods into their organizational culture and novel technologies into their working procedures. Inter-Parliamentary Union World e-Parliament Reports capture digital transformation trends towards open data production, standardized and knowledge-driven business processes, and the implementation of inclusive and participatory schemes. Nevertheless, there is still a limited consensus on how these trends will materialize into specific tools, products, and services, with added value for parliamentary and societal stakeholders. This article outlines the rapid evolution of the digital parliament from the user perspective. In doing so, it describes a transformational framework based on the evaluation of empirical data by an expert survey of parliamentarians and parliamentary administrators. Basic sets of tools and technologies that are perceived as vital for future parliamentary use by intra-parliamentary stakeholders, such as systems and processes for information and knowledge sharing, are analyzed. Moreover, boundary conditions for development and implementation of parliamentary technologies are set and highlighted. Concluding recommendations regarding the expected investments, interdisciplinary research, and cross-sector collaboration within the defined framework are presented. Full article
(This article belongs to the Special Issue Semantic Web Technology and Recommender Systems)
Show Figures

Figure 1

Article
OTNEL: A Distributed Online Deep Learning Semantic Annotation Methodology
Big Data Cogn. Comput. 2020, 4(4), 31; https://doi.org/10.3390/bdcc4040031 - 29 Oct 2020
Viewed by 2447
Abstract
Semantic representation of unstructured text is crucial in modern artificial intelligence and information retrieval applications. The semantic information extraction process from an unstructured text fragment to a corresponding representation from a concept ontology is known as named entity disambiguation. In this work, we [...] Read more.
Semantic representation of unstructured text is crucial in modern artificial intelligence and information retrieval applications. The semantic information extraction process from an unstructured text fragment to a corresponding representation from a concept ontology is known as named entity disambiguation. In this work, we introduce a distributed, supervised deep learning methodology employing a long short-term memory-based deep learning architecture model for entity linking with Wikipedia. In the context of a frequently changing online world, we introduce and study the domain of online training named entity disambiguation, featuring on-the-fly adaptation to underlying knowledge changes. Our novel methodology evaluates polysemous anchor mentions with sense compatibility based on thematic segmentation of the Wikipedia knowledge graph representation. We aim at both robust performance and high entity-linking accuracy results. The introduced modeling process efficiently addresses conceptualization, formalization, and computational challenges for the online training entity-linking task. The novel online training concept can be exploited for wider adoption, as it is considerably beneficial for targeted topic, online global context consensus for entity disambiguation. Full article
(This article belongs to the Special Issue Semantic Web Technology and Recommender Systems)
Show Figures

Figure 1

Article
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
Cited by 3 | Viewed by 2526
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)
Show Figures

Figure 1

Back to TopTop