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

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 66111

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Dr. Konstantinos Kotis

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Department of Cultural Technology and Communication, School of Social Sciences, 81100 Mytilene, Greece
Interests: IoT; ontologies and semantic web
Special Issues, Collections and Topics in MDPI journals

Dr. Dimitris Spiliotopoulos

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Guest Editor

Department of Management Science and Technology, University of the Peloponnese, 22131 Tripoli, Greece
Interests: recommender systems; usability; social media analysis; human–computer interaction
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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

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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 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 1800 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

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Published Papers (13 papers)

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Research

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22 pages, 1122 KiB

Open AccessArticle

Refining Preference-Based Recommendation with Associative Rules and Process Mining Using Correlation Distance

by Mohd Anuaruddin Bin Ahmadon, Shingo Yamaguchi, Abd Kadir Mahamad and Sharifah Saon

Big Data Cogn. Comput. 2023, 7(1), 34; https://doi.org/10.3390/bdcc7010034 - 10 Feb 2023

Cited by 3 | Viewed by 2345

Abstract

Online services, ambient services, and recommendation systems take user preferences into data processing so that the services can be tailored to the customer’s preferences. Associative rules have been used to capture combinations of frequently preferred items. However, for some item sets X and Y, only the frequency of occurrences is taken into consideration, and most of the rules have weak correlations between item sets. In this paper, we proposed a method to extract associative rules with a high correlation between multivariate attributes based on intuitive preference settings, process mining, and correlation distance. The main contribution of this paper is the intuitive preference that is optimized to extract newly discovered preferences, i.e., implicit preferences. As a result, the rules output from the methods has around 70% of improvement in correlation value even if customers do not specify their preference at all. Full article

(This article belongs to the Special Issue Semantic Web Technology and Recommender Systems)

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21 pages, 3906 KiB

Open AccessArticle

GTDOnto: An Ontology for Organizing and Modeling Knowledge about Global Terrorism

by Reem Qadan Al-Fayez, Marwan Al-Tawil, Bilal Abu-Salih and Zaid Eyadat

Big Data Cogn. Comput. 2023, 7(1), 24; https://doi.org/10.3390/bdcc7010024 - 28 Jan 2023

Cited by 3 | Viewed by 2452

Abstract

In recent years and with the advancement of semantic technologies, shared and published online data have become necessary to improve research and development in all fields. While many datasets are publicly available in social and economic domains, most lack standardization. Unlike the medical field, where terms and concepts are well defined using controlled vocabulary and ontologies, social datasets are not. Experts such as the National Consortium for the Study of Terrorism and Responses to Terrorism (START) collect data on global incidents and publish them in the Global Terrorism Database (GTD). Thus, the data are deficient in the technical modeling of its metadata. In this paper, we proposed GTD ontology (GTDOnto) to organize and model knowledge about global incidents, targets, perpetrators, weapons, and other related information. Based on the NeOn methodology, the goal is to build on the effort of START and present controlled vocabularies in a machine-readable format that is interoperable and can be reused to describe potential incidents in the future. The GTDOnto was implemented with the Web Ontology Language (OWL) using the Protégé editor and evaluated by answering competency questions, domain experts’ opinions, and running examples of GTDOnto for representing actual incidents. The GTDOnto can further be used to leverage the publishing of GTD as a knowledge graph that visualizes related incidents and build further applications to enrich its content. Full article

(This article belongs to the Special Issue Semantic Web Technology and Recommender Systems)

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19 pages, 686 KiB

Open AccessEditor’s ChoiceArticle

THOR: A Hybrid Recommender System for the Personalized Travel Experience

by Alireza Javadian Sabet, Mahsa Shekari, Chaofeng Guan, Matteo Rossi, Fabio Schreiber and Letizia Tanca

Big Data Cogn. Comput. 2022, 6(4), 131; https://doi.org/10.3390/bdcc6040131 - 4 Nov 2022

Cited by 4 | Viewed by 4602

Abstract

One of the travelers’ main challenges is that they have to spend a great effort to find and choose the most desired travel offer(s) among a vast list of non-categorized and non-personalized items. Recommendation systems provide an effective way to solve the problem of information overload. In this work, we design and implement “The Hybrid Offer Ranker” (THOR), a hybrid, personalized recommender system for the transportation domain. THOR assigns every traveler a unique contextual preference model built using solely their personal data, which makes the model sensitive to the user’s choices. This model is used to rank travel offers presented to each user according to their personal preferences. We reduce the recommendation problem to one of binary classification that predicts the probability with which the traveler will buy each available travel offer. Travel offers are ranked according to the computed probabilities, hence to the user’s personal preference model. Moreover, to tackle the cold start problem for new users, we apply clustering algorithms to identify groups of travelers with similar profiles and build a preference model for each group. To test the system’s performance, we generate a dataset according to some carefully designed rules. The results of the experiments show that the THOR tool is capable of learning the contextual preferences of each traveler and ranks offers starting from those that have the higher probability of being selected. Full article

(This article belongs to the Special Issue Semantic Web Technology and Recommender Systems)

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24 pages, 1286 KiB

Open AccessEditor’s ChoiceArticle

Ontology-Based Personalized Job Recommendation Framework for Migrants and Refugees

by Dimos Ntioudis, Panagiota Masa, Anastasios Karakostas, Georgios Meditskos, Stefanos Vrochidis and Ioannis Kompatsiaris

Big Data Cogn. Comput. 2022, 6(4), 120; https://doi.org/10.3390/bdcc6040120 - 19 Oct 2022

Cited by 11 | Viewed by 3245

Abstract

Participation in the labor market is seen as the most important factor favoring long-term integration of migrants and refugees into society. This paper describes the job recommendation framework of the Integration of Migrants MatchER SErvice (IMMERSE). The proposed framework acts as a matching tool that enables the contexts of individual migrants and refugees, including their expectations, languages, educational background, previous job experience and skills, to be captured in the ontology and facilitate their matching with the job opportunities available in their host country. Profile information and job listings are processed in real time in the back-end, and matches are revealed in the front-end. Moreover, the matching tool considers the activity of the users on the platform to provide recommendations based on the similarity among existing jobs that they already showed interest in and new jobs posted on the platform. Finally, the framework takes into account the location of the users to rank the results and only shows the most relevant location-based recommendations. Full article

(This article belongs to the Special Issue Semantic Web Technology and Recommender Systems)

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23 pages, 1576 KiB

Open AccessArticle

ID2SBVR: A Method for Extracting Business Vocabulary and Rules from an Informal Document

by Irene Tangkawarow, Riyanarto Sarno and Daniel Siahaan

Big Data Cogn. Comput. 2022, 6(4), 119; https://doi.org/10.3390/bdcc6040119 - 19 Oct 2022

Cited by 6 | Viewed by 2494

Abstract

Semantics of Business Vocabulary and Rules (SBVR) is a standard that is applied in describing business knowledge in the form of controlled natural language. Business process designers develop SBVR from formal documents and later translate it into business process models. In many immature companies, these documents are often unavailable and could hinder resource efficiency efforts. This study introduced a novel approach called informal document to SBVR (ID2SBVR). This approach is used to extract operational rules of SBVR from informal documents. ID2SBVR mines fact type candidates using word patterns or extracting triplets (actor, action, and object) from sentences. A candidate fact type can be a complex, compound, or complex-compound sentence. ID2SBVR extracts fact types from candidate fact types and transforms them into a set of SBVR operational rules. The experimental results show that our approach can be used to generate the operational rules of SBVR from informal documents with an accuracy of 0.91. Moreover, ID2SBVR can also be used to extract fact types with an accuracy of 0.96. The unstructured data is successfully converted into semi-structured data for use in pre-processing. ID2SBVR allows the designer to automatically generate business process models from informal documents. Full article

(This article belongs to the Special Issue Semantic Web Technology and Recommender Systems)

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17 pages, 1055 KiB

Open AccessArticle

Social Media Analytics as a Tool for Cultural Spaces—The Case of Twitter Trending Topics

by Vassilis Poulopoulos and Manolis Wallace

Big Data Cogn. Comput. 2022, 6(2), 63; https://doi.org/10.3390/bdcc6020063 - 2 Jun 2022

Cited by 1 | Viewed by 4094

Abstract

We are entering an era in which online personalities and personas will grow faster and faster. People are tending to use the Internet, and social media especially, more frequently and for a wider variety of purposes. In parallel, a number of cultural spaces have already decided to invest in marketing and message spreading through the web and the media. Growing their audience, or locating the appropriate group of people to share their information, remains a tedious task within the chaotic environment of the Internet. The investment is mainly financial—usually large—and directed to advertisements. Still, there is much space for research and investment in analytics that can provide evidence considering the spreading of the word and finding groups of people interested in specific information or trending topics and influencers. In this paper, we present a part of a national project that aims to perform an analysis of Twitter’s trending topics. The main scope of the analysis is to provide a basic ordering on the topics based on their “importance”. Based on this, we clarify how cultural institutions can benefit from such an analysis in order to empower their online presence. Full article

(This article belongs to the Special Issue Semantic Web Technology and Recommender Systems)

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14 pages, 577 KiB

Open AccessArticle

The Predictive Power of a Twitter User’s Profile on Cryptocurrency Popularity

by Maria Trigka, Andreas Kanavos, Elias Dritsas, Gerasimos Vonitsanos and Phivos Mylonas

Big Data Cogn. Comput. 2022, 6(2), 59; https://doi.org/10.3390/bdcc6020059 - 20 May 2022

Cited by 7 | Viewed by 4010

Abstract

Microblogging has become an extremely popular communication tool among Internet users worldwide. Millions of users daily share a huge amount of information related to various aspects of their lives, which makes the respective sites a very important source of data for analysis. Bitcoin (BTC) is a decentralized cryptographic currency and is equivalent to most recurrently known currencies in the way that it is influenced by socially developed conclusions, regardless of whether those conclusions are considered valid. This work aims to assess the importance of Twitter users’ profiles in predicting a cryptocurrency’s popularity. More specifically, our analysis focused on the user influence, captured by different Twitter features (such as the number of followers, retweets, lists) and tweet sentiment scores as the main components of measuring popularity. Moreover, the Spearman, Pearson, and Kendall Correlation Coefficients are applied as post-hoc procedures to support hypotheses about the correlation between a user influence and the aforementioned features. Tweets sentiment scoring (as positive or negative) was performed with the aid of Valence Aware Dictionary and Sentiment Reasoner (VADER) for a number of tweets fetched within a concrete time period. Finally, the Granger causality test was employed to evaluate the statistical significance of various features time series in popularity prediction to identify the most influential variable for predicting future values of the cryptocurrency popularity. Full article

(This article belongs to the Special Issue Semantic Web Technology and Recommender Systems)

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21 pages, 30151 KiB

Open AccessEditor’s ChoiceArticle

Context-Aware Explainable Recommendation Based on Domain Knowledge Graph

by Muzamil Hussain Syed, Tran Quoc Bao Huy and Sun-Tae Chung

Big Data Cogn. Comput. 2022, 6(1), 11; https://doi.org/10.3390/bdcc6010011 - 20 Jan 2022

Cited by 16 | Viewed by 6892

Abstract

With the rapid growth of internet data, knowledge graphs (KGs) are considered as efficient form of knowledge representation that captures the semantics of web objects. In recent years, reasoning over KG for various artificial intelligence tasks have received a great deal of research interest. Providing recommendations based on users’ natural language queries is an equally difficult undertaking. In this paper, we propose a novel, context-aware recommender system, based on domain KG, to respond to user-defined natural queries. The proposed recommender system consists of three stages. First, we generate incomplete triples from user queries, which are then segmented using logical conjunction (∧) and disjunction (∨) operations. Then, we generate candidates by utilizing a KGE-based framework (Query2Box) for reasoning over segmented logical triples, with ∧, ∨, and ∃ operators; finally, the generated candidates are re-ranked using neural collaborative filtering (NCF) model by exploiting contextual (auxiliary) information from GraphSAGE embedding. Our approach demonstrates to be simple, yet efficient, at providing explainable recommendations on user’s queries, while leveraging user-item contextual information. Furthermore, our framework has shown to be capable of handling logical complex queries by transforming them into a disjunctive normal form (DNF) of simple queries. In this work, we focus on the restaurant domain as an application domain and use the Yelp dataset to evaluate the system. Experiments demonstrate that the proposed recommender system generalizes well on candidate generation from logical queries and effectively re-ranks those candidates, compared to the matrix factorization model. Full article

(This article belongs to the Special Issue Semantic Web Technology and Recommender Systems)

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26 pages, 1621 KiB

Open AccessArticle

GeoLOD: A Spatial Linked Data Catalog and Recommender

by Vasilis Kopsachilis and Michail Vaitis

Big Data Cogn. Comput. 2021, 5(2), 17; https://doi.org/10.3390/bdcc5020017 - 19 Apr 2021

Cited by 6 | Viewed by 5844

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 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)

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16 pages, 882 KiB

Open AccessArticle

ParlTech: Transformation Framework for the Digital Parliament

by Dimitris Koryzis, Apostolos Dalas, Dimitris Spiliotopoulos and Fotios Fitsilis

Big Data Cogn. Comput. 2021, 5(1), 15; https://doi.org/10.3390/bdcc5010015 - 15 Mar 2021

Cited by 23 | Viewed by 9567

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 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)

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17 pages, 1460 KiB

Open AccessArticle

OTNEL: A Distributed Online Deep Learning Semantic Annotation Methodology

by Christos Makris and Michael Angelos Simos

Big Data Cogn. Comput. 2020, 4(4), 31; https://doi.org/10.3390/bdcc4040031 - 29 Oct 2020

Cited by 5 | Viewed by 6051

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 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)

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19 pages, 3738 KiB

Open AccessEditor’s ChoiceArticle

Keyword Search over RDF: Is a Single Perspective Enough?

by Christos Nikas, Giorgos Kadilierakis, Pavlos Fafalios and Yannis Tzitzikas

Big Data Cogn. Comput. 2020, 4(3), 22; https://doi.org/10.3390/bdcc4030022 - 27 Aug 2020

Cited by 15 | Viewed by 6070

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 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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Review

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25 pages, 2399 KiB

Open AccessFeature PaperReview

Semantic Trajectory Analytics and Recommender Systems in Cultural Spaces

by Sotiris Angelis, Konstantinos Kotis and Dimitris Spiliotopoulos

Big Data Cogn. Comput. 2021, 5(4), 80; https://doi.org/10.3390/bdcc5040080 - 16 Dec 2021

Cited by 10 | Viewed by 4874

Abstract

Semantic trajectory analytics and personalised recommender systems that enhance user experience are modern research topics that are increasingly getting attention. Semantic trajectories can efficiently model human movement for further analysis and pattern recognition, while personalised recommender systems can adapt to constantly changing user needs and provide meaningful and optimised suggestions. This paper focuses on the investigation of open issues and challenges at the intersection of these two topics, emphasising semantic technologies and machine learning techniques. The goal of this paper is twofold: (a) to critically review related work on semantic trajectories and knowledge-based interactive recommender systems, and (b) to propose a high-level framework, by describing its requirements. The paper presents a system architecture design for the recognition of semantic trajectory patterns and for the inferencing of possible synthesis of visitor trajectories in cultural spaces, such as museums, making suggestions for new trajectories that optimise cultural experiences. Full article

(This article belongs to the Special Issue Semantic Web Technology and Recommender Systems)

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