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Future Internet
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Information Retrieval on the Semantic Web
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Information Retrieval on the Semantic Web

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Techno-Social Smart Systems".

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 15732

Special Issue Editors

Prof. Dr. Diego Reforgiato Recupero

E-Mail Website
Guest Editor
Department of Mathematics and Computer Science, Università degli Studi di Cagliari, 09124 Cagliari, Spain
Interests: machine learning; semantic web; sentiment analysis; text mining; knowledge graphs
Special Issues, Collections and Topics in MDPI journals
Dr. Alessandro Sebastian Podda

E-Mail Website
Guest Editor
Department of Mathematics and Computer Science, University of Cagliari, 09124 Cagliari, Italy
Interests: artificial intelligence; deep learning; information security; financial forecasting; blockchain; smart contracts
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Semantic Web’s vision, according to Berners-Lee, 2001, was “an extension of the current web in which information is given well-defined meaning, better enabling computers and people to work in cooperation”. 

Semantic Web technologies have defined ways of creating meaningful structured collections of data and their relationships. Semantic Web applications, thus, help people to build data stores on the Internet, build vocabulary, and compose data handling guidelines. Ontologies are basic components of the semantic web which provide explicit and formal specifications of a conceptualization and are very useful in supporting the semantic operability. In addition, over recent years, there has been a rapid growth in the use and the importance of Knowledge Graphs (KGs) along with their application to many important tasks. KGs are large networks of real-world entities described in terms of their semantic types and their relationships to each other. 

Information Retrieval (IR) involves identifying and extracting relevant pages containing specific information according to predefined guidelines. There are many IR techniques for extracting keywords, such as NLP-based extraction techniques, which are used to search for simple keywords. Current IR techniques are unable to exploit the semantic knowledge within documents and, hence, cannot give precise answers to precise questions. We cannot automatically extract such content from general documents yet. Industries are currently developing many metadata languages (e.g., RDF(S), OML) to let people index web information resources with knowledge representations (logical statements) and store them in web documents. As an example, DAML+OIL is an effort to develop a universal Semantic Web markup language that is suffciently rich to provide machines not only with the capability to read data, but also with the capability to interpret and infer over the data. Challenges are, therefore, in both directions: Semantic Web approaches to improve IR systems and IR systems for searching within structured content.

So, in the current situation, how can we design IR tools that can efficiently leverage semantic information? Can we come up with IR systems that are able to search through semantic data? How can we design simple and complex question answering systems that can exploit the huge amount of structured information, such as knowledge graphs? How can we extend old IR tools so that they can perform well while searching through structured contents?

Finally, I would like to thank Mr. Simone Angioni and his valuable work for assisting me with this Special Issue.

Topics of interest include but are not limited to the following:

  • Enhanced data aggregation, querying, and information retrieval;
  • Simple knowledge organization systems;
  • Semantic exploration, recommendation, and analytics;
  • Semantic crawling, indexing, and compression;
  • Semantics for web search;
  • Semantics for domain-specific search;
  • Semantics for web browsing and recommendation;
  • Sentiment analysis and semantic data;
  • Question answering and conversational search over knowledge graphs;
  • Entity and knowledge graphs summarization;
  • Retrieval and ranking models for knowledge graphs;
  • Entity and knowledge graphs search;
  • Domain-specific search over knowledge graphs.

Prof. Dr. Diego Reforgiato Recupero
Dr. Alessandro Sebastian Podda
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

  • semantic web
  • semantic data
  • information retrieval
  • information technology
  • data aggregation
  • knowledge graphs

Published Papers (6 papers)

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Research

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29 pages, 3732 KiB  
Article
Searching Online for Art and Culture: User Behavior Analysis
by Minas Pergantis, Iraklis Varlamis, Nikolaos Grigorios Kanellopoulos and Andreas Giannakoulopoulos
Future Internet 2023, 15(6), 211; https://doi.org/10.3390/fi15060211 - 11 Jun 2023
Cited by 1 | Viewed by 1503
Abstract
With the constant expansion of the Web, search engines became part of people’s daily routines. How users behave during the search process depends on a variety factors, one of which is the topic of their search interest. This study focused on the behavior [...] Read more.
With the constant expansion of the Web, search engines became part of people’s daily routines. How users behave during the search process depends on a variety factors, one of which is the topic of their search interest. This study focused on the behavior of users searching the Web for content related to art and cultural heritage. A proprietary, publicly available, federated search engine, in the form of a web and mobile app, was developed for this research’s purposes. This platform was used to monitor actual user behavior during a six-month period. Quantitative data related to the platform’s usage were collected and analyzed in order to provide a detailed picture of the way interested parties engaged with it. This information pertained not only to the search queries and results viewed, but also to the various characteristics of the search sessions themselves. The study presented an analysis of these data, with emphasis on query and result characteristics, usage devices, login preferences and session duration, and drew conclusions. The study’s findings showed, among other things, that art searchers showed a preference for shorter queries, a tendency for higher query repeatability, and showed interest in a wider number of results than general purpose searchers. Additionally, they were more keen to use desktop devices instead of mobile ones and displayed higher engagement metrics during longer search sessions or when logged in. These findings outlined an art searcher who was interested in concepts and people often revisited searches and results, showed interest for more than the first few hits, was attracted by rich content, and understood the art search process as a task which requires focus. They also pointed out a duality in the art search process itself which can be long and involved or short and purposeful. Full article
(This article belongs to the Special Issue Information Retrieval on the Semantic Web)
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19 pages, 3218 KiB  
Article
Elastic Stack and GRAPHYP Knowledge Graph of Web Usage: A Win–Win Workflow for Semantic Interoperability in Decision Making
by Otmane Azeroual, Renaud Fabre, Uta Störl and Ruidong Qi
Future Internet 2023, 15(6), 190; https://doi.org/10.3390/fi15060190 - 25 May 2023
Cited by 2 | Viewed by 1520
Abstract
The use of Elastic Stack (ELK) solutions and Knowledge Graphs (KGs) has attracted a lot of attention lately, with promises of vastly improving business performance based on new business insights and better decisions. This allows organizations not only to reap the ultimate benefits [...] Read more.
The use of Elastic Stack (ELK) solutions and Knowledge Graphs (KGs) has attracted a lot of attention lately, with promises of vastly improving business performance based on new business insights and better decisions. This allows organizations not only to reap the ultimate benefits of data governance but also to consider the widest possible range of relevant information when deciding their next steps. In this paper, we examine how data management and data visualization are used in organizations that use ELK solutions to collect integrated data from different sources in one place and visualize and analyze them in near-real time. We also present some interpretable Knowledge Graphs, GRAPHYP, which are innovative by processing an analytical information geometry and can be used together with an ELK to improve data quality and visualize the data to make informed decisions in organizations. Good decisions are the backbone of successful organizations. Ultimately, this research is about integrating a combined solution between ELK and SKG GRAPHYP and showing users the advantages in this area. Full article
(This article belongs to the Special Issue Information Retrieval on the Semantic Web)
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24 pages, 3393 KiB  
Article
A Multiverse Graph to Help Scientific Reasoning from Web Usage: Interpretable Patterns of Assessor Shifts in GRAPHYP
by Renaud Fabre, Otmane Azeroual, Joachim Schöpfel, Patrice Bellot and Daniel Egret
Future Internet 2023, 15(4), 147; https://doi.org/10.3390/fi15040147 - 10 Apr 2023
Cited by 1 | Viewed by 2138
Abstract
The digital support for scientific reasoning presents contrasting results. Bibliometric services are improving, but not academic assessment; no service for scholars relies on logs of web usage to base query strategies for relevance judgments (or assessor shifts). Our Scientific Knowledge Graph GRAPHYP innovates [...] Read more.
The digital support for scientific reasoning presents contrasting results. Bibliometric services are improving, but not academic assessment; no service for scholars relies on logs of web usage to base query strategies for relevance judgments (or assessor shifts). Our Scientific Knowledge Graph GRAPHYP innovates with interpretable patterns of web usage, providing scientific reasoning with conceptual fingerprints and helping identify eligible hypotheses. In a previous article, we showed how usage log data, in the form of ‘documentary tracks’, help determine distinct cognitive communities (called adversarial cliques) within sub-graphs. A typology of these documentary tracks through a triplet of measurements from logs (intensity, variety and attention) describes the potential approaches to a (research) question. GRAPHYP assists interpretation as a classifier, with possibilistic graphical modeling. This paper shows what this approach can bring to scientific reasoning; it involves visualizing complete interpretable pathways, in a multi-hop assessor shift, which users can then explore toward the ‘best possible solution’—the one that is most consistent with their hypotheses. Applying the Leibnizian paradigm of scientific reasoning, GRAPHYP highlights infinitesimal learning pathways, as a ‘multiverse’ geometric graph in modeling possible search strategies answering research questions. Full article
(This article belongs to the Special Issue Information Retrieval on the Semantic Web)
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20 pages, 7317 KiB  
Article
Complex Queries for Querying Linked Data
by Hasna Boumechaal and Zizette Boufaida
Future Internet 2023, 15(3), 106; https://doi.org/10.3390/fi15030106 - 09 Mar 2023
Cited by 1 | Viewed by 1061
Abstract
Querying Linked Data is one of the most important issues for the semantic web community today because it requires the user to understand the structure and vocabularies used in various data sources. Furthermore, users must be familiar with the syntax of query languages, [...] Read more.
Querying Linked Data is one of the most important issues for the semantic web community today because it requires the user to understand the structure and vocabularies used in various data sources. Furthermore, users must be familiar with the syntax of query languages, such as SPARQL. However, because users are accustomed to natural language-based search, novice users may find it challenging to use these features. As a result, new approaches for querying Linked Data sources on the web with NL queries must be defined. In this paper, we propose a novel system for converting natural language queries into SPARQL queries to query linked and heterogeneous semantic data on the web. While most existing methods have focused on simple queries and have ignored complex queries, the method described in this work aims to handle various types of NL queries, particularly complex queries containing negation, numbers, superlatives, and comparative adjectives. Three complementary strategies are used in this context: (1) identifying the semantic relations between query terms in order to understand the user’s needs; (2) mapping the NL terms to semantic entities; and (3) constructing the query’s valid triples based on the different links used to describe the identified entities in order to generate correct SPARQL queries. The empirical evaluations show that the proposed system is effective. Full article
(This article belongs to the Special Issue Information Retrieval on the Semantic Web)
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18 pages, 1662 KiB  
Article
Retrieving Adversarial Cliques in Cognitive Communities: A New Conceptual Framework for Scientific Knowledge Graphs
by Renaud Fabre, Otmane Azeroual, Patrice Bellot, Joachim Schöpfel and Daniel Egret
Future Internet 2022, 14(9), 262; https://doi.org/10.3390/fi14090262 - 07 Sep 2022
Cited by 2 | Viewed by 2127
Abstract
The variety and diversity of published content are currently expanding in all fields of scholarly communication. Yet, scientific knowledge graphs (SKG) provide only poor images of the varied directions of alternative scientific choices, and in particular scientific controversies, which are not currently identified [...] Read more.
The variety and diversity of published content are currently expanding in all fields of scholarly communication. Yet, scientific knowledge graphs (SKG) provide only poor images of the varied directions of alternative scientific choices, and in particular scientific controversies, which are not currently identified and interpreted. We propose to use the rich variety of knowledge present in search histories to represent cliques modeling the main interpretable practices of information retrieval issued from the same “cognitive community”, identified by their use of keywords and by the search experience of the users sharing the same research question. Modeling typical cliques belonging to the same cognitive community is achieved through a new conceptual framework, based on user profiles, namely a bipartite geometric scientific knowledge graph, SKG GRAPHYP. Further studies of interpretation will test differences of documentary profiles and their meaning in various possible contexts which studies on “disagreements in scientific literature” have outlined. This final adjusted version of GRAPHYP optimizes the modeling of “Manifold Subnetworks of Cliques in Cognitive Communities” (MSCCC), captured from previous user experience in the same search domain. Cliques are built from graph grids of three parameters outlining the manifold of search experiences: mass of users; intensity of uses of items; and attention, identified as a ratio of “feature augmentation” by literature on information retrieval, its mean value allows calculation of an observed “steady” value of the user/item ratio or, conversely, a documentary behavior “deviating” from this mean value. An illustration of our approach is supplied in a positive first test, which stimulates further work on modeling subnetworks of users in search experience, that could help identify the varied alternative documentary sources of information retrieval, and in particular the scientific controversies and scholarly disputes. Full article
(This article belongs to the Special Issue Information Retrieval on the Semantic Web)
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Review

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32 pages, 2434 KiB  
Review
Charting Past, Present, and Future Research in the Semantic Web and Interoperability
by Abderahman Rejeb, John G. Keogh, Wayne Martindale, Damion Dooley, Edward Smart, Steven Simske, Samuel Fosso Wamba, John G. Breslin, Kosala Yapa Bandara, Subhasis Thakur, Kelly Liu, Bridgette Crowley, Sowmya Desaraju, Angela Ospina and Horia Bradau
Future Internet 2022, 14(6), 161; https://doi.org/10.3390/fi14060161 - 25 May 2022
Cited by 6 | Viewed by 6457
Abstract
Huge advances in peer-to-peer systems and attempts to develop the semantic web have revealed a critical issue in information systems across multiple domains: the absence of semantic interoperability. Today, businesses operating in a digital environment require increased supply-chain automation, interoperability, and data governance. [...] Read more.
Huge advances in peer-to-peer systems and attempts to develop the semantic web have revealed a critical issue in information systems across multiple domains: the absence of semantic interoperability. Today, businesses operating in a digital environment require increased supply-chain automation, interoperability, and data governance. While research on the semantic web and interoperability has recently received much attention, a dearth of studies investigates the relationship between these two concepts in depth. To address this knowledge gap, the objective of this study is to conduct a review and bibliometric analysis of 3511 Scopus-registered papers on the semantic web and interoperability published over the past two decades. In addition, the publications were analyzed using a variety of bibliometric indicators, such as publication year, journal, authors, countries, and institutions. Keyword co-occurrence and co-citation networks were utilized to identify the primary research hotspots and group the relevant literature. The findings of the review and bibliometric analysis indicate the dominance of conference papers as a means of disseminating knowledge and the substantial contribution of developed nations to the semantic web field. In addition, the keyword co-occurrence network analysis reveals a significant emphasis on semantic web languages, sensors and computing, graphs and models, and linking and integration techniques. Based on the co-citation clustering, the Internet of Things, semantic web services, ontology mapping, building information modeling, bioinformatics, education and e-learning, and semantic web languages were identified as the primary themes contributing to the flow of knowledge and the growth of the semantic web and interoperability field. Overall, this review substantially contributes to the literature and increases scholars’ and practitioners’ awareness of the current knowledge composition and future research directions of the semantic web field. Full article
(This article belongs to the Special Issue Information Retrieval on the Semantic Web)
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