# Unpacking the Semantic Web: Integrated Insights into Ontologies, Linked Data Applications, and Open Data Transformation (2009-2025)

Leila Zemmouchi-Ghomari, Djazia Ghomari

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Leila Zemmouchi-Ghomari, Djazia Ghomari. Unpacking the Semantic Web: Integrated Insights into Ontologies, Linked Data Applications, and Open Data Transformation (2009-2025). National Higher School of Advanced Technologies, Algiers, Algeria. 2025, pp.19. [hal-05168637](https://hal.science/hal-05168637v1)

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# Unpacking the Semantic Web:
Integrated Insights into Ontologies, Linked Data Applications,
and Open Data Transformation (2009-2025)

Leila Zemmouchi-Ghomari<fnref n="1" />, Djazia Ghomari<fnref n="2" />

¹ leila.ghomari@ensta.edu.dz, <fnref n="2" /> djazia.ghomari@etu.usthb.dz

**Abstract:** This wrap-up paper synthesizes findings from a collection of my research works from 2009 to 2025 exploring the multifaceted landscape of Semantic Web (SW) technologies, artifacts, methods, and tools. More specifically, by examining the role and application of ontologies in various domains including business models, predictive maintenance, and manufacturing, the cohabitation and conversion between relational databases and ontologies, techniques for natural language to SPARQL query translation, and the exploitation of Linked Data for sample web applications, as well as approaches for transforming Open Government Data (OGD) into Linked Open Data (LOD) to exploit its potential fully, this paper identifies key trends, limitations, and potential synergies. Through a deep analysis of existing methodologies and frameworks, we aim to provide new insights into the integrated application of these technologies to enhance data interoperability, knowledge representation, and intelligent information retrieval, ultimately contributing to the evolution of the Semantic Web. We propose a classification of crucial covered domains in the semantic web context, highlighting each work's contributions, challenges, and limitations, as well as a framework for delimiting the dimensions of these studies. Additionally, we outline KPIs to enhance the practicability and adoption of semantic web technologies, artifacts, methods, and tools. This paper has been written with the assistance of notebookLM<fnref n="1" /> and Quillbot<fnref n="2" /> to summarize the paper's content.

**Keywords:** Semantic web, ontology design and development, ontology quality, linked open data, open government data

## 1. Introduction

The Semantic Web vision aims to transform the current documents web into a "web of data," enhancing data interoperability and knowledge representation. Core technologies underpinning this vision include Linked Data and Ontologies. Ontologies formally represent knowledge, facilitating cohabitation between relational databases and ontologies. Linked Data provides best practices for publishing and connecting structured data on the Web using RDF<fnref n="3" /> and SPARQL<fnref n="4" />. These technologies enable automatic processing, integration, and creating a global interconnected data space, thereby realizing the Semantic Web vision of a more intelligent and interconnected web of data.

This paper synthesizes findings from a range of research conducted between 2009 and 2025, examining the diverse aspects of Semantic Web technologies, artifacts, methods, and tools. The Semantic Web vision fundamentally depends on its core technologies, Linked Data and Ontologies, to improve data interoperability and knowledge representation. Ontologies are essential in multiple domains such as business models, predictive maintenance, and manufacturing, enabling the integration and transformation between relational databases and ontologies. Additionally, methods for translating natural language into SPARQL queries and utilizing Linked Data for sample web applications are essential components of the Semantic Web ecosystem.

OGD is a significant area of emphasis in the Semantic Web. Transforming OGD into Linked Open Data is crucial for maximizing its potential. This transformation allows stakeholders to analyze data effectively with minimal

<footnote n="1">¹ Laboratory of Innovative Technologies, National Higher School of Advanced Technologies, Algiers, Algeria</footnote>

<footnote n="2">² Computer science faculty, Houari Boumediene Sciences and Technologies University, USTHB, Algiers, Algeria</footnote>

<footnote n="1">¹ https://notebooklm.google/</footnote>

<footnote n="2">² https://quillbot.com/</footnote>

<footnote n="3">³ https://www.w3.org/RDF/</footnote>

<footnote n="4">⁴ https://www.w3.org/TR/sparql12-query/</footnote>
human intervention and supports the development of public utility applications. It is essential to comprehend the challenges and obstacles that impede the OGD initiative, especially those related to data protection and security concerns.

This paper aims to synthesize findings from selected research works to identify common themes, challenges, and potential for integration across various areas of Semantic Web research. This synthesis analyzes existing methodologies and frameworks to offer insights into the integrated application of technologies that enhance data interoperability, knowledge representation, and intelligent information retrieval, thereby contributing to the evolution of the Semantic Web. This paper proposes a classification of essential covered domains, emphasizing each work's contributions, challenges, and limitations. It also presents a framework for delineating the dimensions of these studies and key performance indicators to improve the practicality and adoption of Semantic Web technologies.

## 2. Research papers on Semantic Web from 2009-2025

Table 1 summarizes the contributions, challenges, and limitations of my work on the Semantic Web from 2009 to date. The practical implications of these works are presented in a mind map (see Figure 1).

<table><caption>Table 1: Summary of my Semantic Web papers 2009-2025</caption><thead><tr><td>Paper</td><td>Contribution(s)</td><td>Challenge(s)</td></tr></thead><tbody><tr><td>[1]</td><td>Aims to determine the suitability of SysML for representing formal manufacturing ontologies. - Explores the compatibility of SysML with OWL. - Compares the advantages and disadvantages of OWL<sup>5</sup> and SysML<sup>6</sup> for modeling manufacturing ontologies. - Provides insights for designers to select the most suitable language. - Reviews available manufacturing ontologies and the motivation behind representation language choices.</td><td>- Notes that OWL provides formal logic but cannot adequately represent system behavior. - SysML excels in modeling physical systems but lacks formal semantics. - Highlights the challenges in representing a manufacturing ontology with OWL and/or SysML. - Mentions that OWL generally requires more computing power than SysML.</td></tr><tr><td>[2]</td><td>- Aims to identify all Algerian government datasets available on the web that can be considered OGD, including those on international platforms. - The ultimate goal is to create a dedicated portal for consolidating government data in Algeria. - Evaluate Algerian government datasets (from Algerian portals and international platforms) based on the FAIR principles.</td><td>- Notes that the scope of previous studies might have been insufficient for a comprehensive study. - Observes that most defined FAIR measures are not considered for Algerian portals.</td></tr><tr><td>[3]</td><td>- Proposes LOD-GePEx, a three-step approach to transform OGD into LOD: transformation, publication, and exploitation. - Aims to enable insightful data analysis for stakeholders without intensive human workloads. - Demonstrates the approach's effectiveness and performance through functional and performance tests. - Claims the approach is generic and usable for several datasets and any case study. - Provides an interface for uploading, transforming, validating, and publishing LOD.</td><td>- The approach struggles with speed index and interactivity time in RDF and LOD publication generation, especially with large government datasets. - The exploitation interface functionalities include a query editor, service editor, and launching published applications, each with specific performance test results.</td></tr><tr><td>[4]</td><td>- Aims to provide an RDF mapping model to extract RDF triples and link input data to other datasets on the web. - Takes into account input data formats (structured and unstructured) and different data types. - Focuses on reducing human intervention in the mapping task. - Compares the proposed approach to previous work based on input formats and human intervention level.</td><td>- Highlights that RDF mapping for different input formats requires intensive human workload in existing approaches. - Notes that current RDF mapping approaches often require human intervention or focus on particular data formats (e.g., relational databases).</td></tr><tr><td>[5]</td><td>- Aims to analyze and categorize challenges and obstacles that hinder the OGD initiative. - Focuses particularly on data protection and security concerns for data providers. - OGD challenges are classified into three categories: end-user satisfaction, quality of data, and publication by government entities. - Provides technical guidelines for data providers' protection and security.</td><td>- Highlights challenges such as data heterogeneity, protection, quality, and provenance issues. - Notes that many existing works do not address challenges preventing entities from joining the initiative or consider data provider perspectives on protection and security. - Identifies potential legal barriers as a challenge.</td></tr><tr><td>[6]</td><td>The paper explores the bidirectional relationship between ontologies and machine learning, highlighting how ontologies can improve the accuracy and interpretability of machine learning algorithms. It presents various use cases, such as predictive modeling in healthcare, annotation of social media data, personalized video recommender systems, and prediction of pharmacological consequences of drug interactions. The paper also discusses automated ontology learning, which uses machine learning</td><td>The paper identifies several challenges in using ontologies in machine learning, including complexity, scalability, data quality, semantic heterogeneity, expertise, quality of generated ontologies, algorithms' scalability, and methods' complexity. These challenges can lead to high overhead costs, difficulty handling large amounts of data, and the need for continuous evolution. Additionally, the paper suggests interdisciplinary</td></tr></tbody></table>

<footnote n="5">⁵ https://www.w3.org/TR/owl2-overview/</footnote>

<footnote n="6">⁶ http://www.omg.org/spec/SysML/index.htm</footnote>
<table><tr><td></td><td>algorithms to generate ontologies from large datasets. It categorizes machine learning algorithms into unsupervised, supervised, and hybrid approaches and provides examples of ontologies generated using these methods in the biomedical domain. Ontologies are also used to explain machine learning models and their outputs, providing a structured representation of concepts and relationships within a domain and enhancing interpretability for domain experts and stakeholders.</td><td>collaboration to overcome these challenges. The paper also highlights the ongoing need for research to interpret big data and adapt it for ontologies, as considerable data research faces similar challenges.</td></tr><tr><td>[7]</td><td>- Aims to propose a percentage scale of marks for metrics to assess data accessibility in OGD portals. - Experiments with the proposed scale on the American OGD portal. - Reviews existing works on OGD quality assessment and highlights the lack of a broad scale of marks. - Proposes a specific formula with expert weighting for evaluating data accessibility aspects (available formats, access options, licensing, timeliness).</td><td>- Highlights the difficulty in comparing and benchmarking evaluations due to the lack of a standardized scale of marks. - Notes that existing means to apply proposed metrics for OGD portal evaluation are similar (giving marks if measures are met) but lack standardization. - The evaluation of the American OGD portal showed that published data still needs to be improved significantly in terms of timeliness.</td></tr><tr><td>[8]</td><td>- Focuses on automatic SPARQL query generation from ontology competency questions. - Reviews related works in translating natural language questions into SPARQL queries. - Proposes an approach based on typical phases from related works, including ontology exploration, question classification, linguistic question treatments, entity mapping, and query building. - Documents each phase meticulously to ensure reproducibility.</td><td>- Mentions that many works tackled SPARQL translation from different perspectives: validation support and NLQ systems. - Notes that studied approaches can be classified following various criteria to build a profile and cluster similar approaches.</td></tr><tr><td>[9]</td><td>- Aims to conduct a detailed bibliographic study on open data and digital Governance: history, context, obstacles, and advantages. - Studies the influence of open data on digital Governance, focusing on transparency, interaction, decision-making, and economic potential.</td><td>The paper highlights challenges in implementing open data and digital governance. Open data offers transparency and participatory governance but challenges data quality and provenance. The most challenging obstacle is the lack of standardization in formats. Digital governance implementation faces costs, cybersecurity concerns, inappropriate management methods, and a rigid corporate culture. Organizations must adapt to these challenges to implement digital transformation effectively.</td></tr><tr><td>[10]</td><td>- Designs and implements a method to guide the exploitation of linked open data to produce public utility applications. - Proposes a system for generating web and mobile applications based on linked open data. - Identifies common steps in related works for developing linked data applications. - Presents a database scheme and application generation stage with various application types (statistics, maps, articles, calendars, search engines). - Conducts performance tests on the proposed system.</td><td>- Highlights that a comprehensive search is fundamental to distinguish a systematic literature review from a traditional one. - Identifies data collection, storage, integration, and visualization as the most common steps in related approaches. - Notes that performance measurements are proportional to the number of applications.</td></tr><tr><td>[11]</td><td>- Develops a core industrial maintenance ontology (IMO) that is generic and extensible. - Aims to integrate heterogeneous systems for semantic interoperability. - Describes the development process from requirements to evaluation using competency questions. - Presents a comparison of related works in industrial maintenance ontologies. - Constructs tables of classes, associations, and attributes for the IMO.</td><td>- Highlights the issue of semantic interoperability in e-maintenance platforms. - Notes that many existing industrial ontologies are not available for download.</td></tr><tr><td>[12]</td><td>- Proposes a new approach to transform open web data in various formats (CSV, text, HTML, PDF, image) to linked data. - Aims to overcome issues revealed in existing approaches. - Details each step of the proposed approach. - Supports various file formats with corresponding MIME types. - Performs functional and performance tests to verify the tool's quality.</td><td>- Notes that existing literature approaches for transforming data to linked data have different limitations. - Mentions the challenge of providing a model that can generate RDF mapping for input data formats with intensive human workload. - Reports performance test results for text file transformation, showing extraction time increases with file size.</td></tr><tr><td>[13]</td><td>- Discusses Industry 4.0 challenges and issues. - Presents semantic web technologies and artifacts to solve Industry 4.0 integration issues. - Matches Industry 4.0 design principles with corresponding criteria to identify relevant technologies. - Highlights the potential of linked data, ontologies, and inferencing mechanisms to address inconsistencies and maximize data accessibility.</td><td>The paper highlights the challenges of implementing Industry 4.0, particularly through Semantic Web technologies. These include deficiencies in interoperability and standardization at hardware, software, and data levels, as well as scalability and data security. Data integration in industrial environments is time-consuming and often involves ERP systems or APIs, leading to heterogeneity. The maturity of Industry 4.0 definitions and methodologies is insufficient for real-world implementation. Semantic Web technologies have the potential to address these challenges. Still, they face challenges such as determining and fixing inconsistencies, heterogeneity in model selection, a user-friendly interface, and the emergence of undesigned data structures. The paper concludes that while Semantic Web technologies offer promising solutions, their industrial application still faces certain hurdles.</td></tr><tr><td>[14]</td><td>- Provides a systematic literature review on existing approaches to transform web open data to linked data. - A classification of these approaches and a synthesis study are proposed to highlight the main trends and challenges. - Follows a filtering process to select relevant articles. - Identifies common limitations of existing approaches.</td><td>- Notes that most open data is merely available in a human-readable format and is not interlinked. - Highlights that some analyzed approaches missed guidelines on how to use or publish the generated linked data. - Observed that most studies used</td></tr></table>
<table><tr><td></td><td></td><td>explicit data sources without proper semantic description and documentation.</td></tr><tr><td>[15]</td><td>- Surveys current research works on translating natural language questions (especially competency questions) into SPARQL queries. - Proposes a classification of these works based on automation degree, question types, and application field. - Highlights the most common aspects and general trends in the studied approaches. - Deducts a generic approach comprising five essential activities based on standard translation processing phases.</td><td>- The difficulty in translation consists of ensuring the translated question is relevant to the intended meaning. - Some techniques like NLP and ontology alignment can maximize semantic precision. - Mentions that approach classification is complex and depends on selected criteria. - Identifies five familiar processing phases in most studied approaches.</td></tr><tr><td>[17]</td><td>- Proposes a simplified approach inspired by the BBC portal development for creating linked data web pages. - The approach involves extracting terms, representing them in RDF using DBpedia, including links, and publishing on web pages. - Demonstrates the usability of the approach through implementation.</td><td>The BBC's proposed approach for generating web pages from linked data faces challenges such as not considering coherent linkage, testing the feasibility on various knowledge bases, not integrating unstructured archives, evaluating linked data in terms of provenance, not guaranteeing accurate data, maintaining a single categorization system, and transforming the RDF graph model into an HTML DOM tree without duplicating re-entrant nodes. The paper also highlights the importance of provenance and quality in Linked Data, data quality, maintenance and interlinking, ensuring quality and relevance for users, and scalability limitations for large-scale Linked Data applications. These challenges are specific to the BBC's approach and broader Linked Data technologies.</td></tr><tr><td>[18]</td><td>- Proposes a two-way generic conversion approach between relational databases (RDB) and ontologies (Ontology-RDB and RDB-Ontology). - Aims to implement a mutual conversion tool based on the proposed approach. - Presents background on databases and ontologies, their differences, and conversion approach categories. - Describe related works and a comparison. - Explains the motivation for converting between these artifacts. - Details the conversion approach and its implementation.</td><td>The paper discusses the challenges of converting relational databases (RDBs) to ontologies, highlighting the loss of semantics due to language expressivity differences. Existing conversion tools are incomplete and miss essential OWL constructs, and many proposed approaches are not fully implemented. These limitations include no support for Named Graphs and inference, loss of object properties, use of OWL Full, ignoring inheritance relationships, heavy user involvement, and limited expressiveness of DL-Lite. The authors acknowledge their proposed approach has limitations, including not supporting OWL 2 constructs and the lack of standardized mapping rules. Existing solutions often lack support for inheritance relationships and instances, limiting reasoning capabilities in the resulting ontology.</td></tr><tr><td>[19]</td><td>This chapter provides an in-depth introduction to Linked Data, a set of best practices for publishing and connecting structured data on the Web, based on RDF and queryable using SPARQL. It discusses four principles of Linked Data, including using URIs for things, HTTP URIs for dereferencing, providing helpful information with RDF, and including links to other URIs. The chapter also discusses SPARQL as the query language for Linked Data, its structure, and various means of publishing it. It also discusses consuming Linked Data through browsers, mashups, and search engines.</td><td>The paper highlights the challenges in exploiting Linked Data, including provenance, quality, credit, and reproducibility. Data quality is a significant issue, and maintenance and interlinking are crucial. Ensuring the quality and relevance of Linked Data is ongoing, and language expressivity can limit knowledge reasoning. Exploitation and scalability are also challenges, with underuse of available datasets, semantic difficulties, performance penalties, and large-scale processing limitations. End-user tools are lacking for adequate data consumption and interaction.</td></tr><tr><td>[20]</td><td>- Describes similarities and differences between ontologies and databases from engineering methodology and role/usage perspectives. - Provides an overview of methods to generate an ontology model from a data model and vice versa. - Highlights cases where ontologies and databases are more suitable and can be complementary. - Summarize the mutual fundamental disparities and convenient cases. - Discusses database querying about RDF specifications.</td><td>- Mentions efficiency issues and representation capabilities as points of comparison. - Notes that database querying needs to be reformulated to consider logical specifications of RDF queries. - Constraints on queries add further complexity.</td></tr><tr><td>[21]</td><td>- Proposes an approach to evaluate ontologies concerning the linked data principles. - It presents a concrete interpretation of the four linked data principles applied to ontologies. - Provides algorithms to detect violations and semi-automatically fix them automatically. - Implemented the approach using Java/Jena and applied it to state-of-the-art ontologies. - Proposes means to correct detected anomalies.</td><td>- Highlights the need to address web data compliance with linked data principles. - Notes that most LD validator tools focus on principle 2 (URI dereferenceability) and principle 3 (RDF validation) and do not address fixing errors.</td></tr><tr><td>[22]</td><td>Ontology-based context modeling offers several benefits, including avoiding semantic ambiguity in data interpretation, enabling automatic interpretation and reasoning capabilities, impacting the interoperability of context-sensitive systems, offering semantic reasoning, and providing independence from applications. Contextualized ontologies combine the advantages of ontologies and context, allowing for knowledge reuse through mappings between local ontologies of different contexts. Formalisms like Context OWL (C-OWL) enable semantic relationships between local ontologies, facilitating global reasoning by reusing context knowledge.</td><td>Ontology-based context modeling faces challenges such as finding an appropriate ontology, scalability, and lack of systematic input experience. The evolution of systems and their contexts impacts integration, and dealing with different contexts, especially in multidisciplinary domains, is difficult. Formalizing all context information is likely insurmountable, and scalability of contexts is a challenge. A standardized context language based on top-level ontologies is needed, and evaluation of context modeling ontologies faces difficulties. In smart homes, covering</td></tr></table>
<table><tr><td></td><td>Approaches like the PLAB ontology model enable modeling context within an ontology, allowing context to become part of the database and be queried as an entity.</td><td>all aspects of context in activity recognition is considered insurmountable.</td></tr><tr><td>[23]</td><td>- List ontologies related to crisis management. - Discusses the representation of central concepts in the crisis management domain. - Describes some research works on crisis management based on linked data and/or ontologies. - Mentions some open-source tools and platforms for crisis management. - Proposes an approach coupling information systems and knowledge-based systems to support crisis management phases.</td><td>- The purpose of ontology construction is to provide a metric to measure the relevance of government website content in terms of disaster preparedness, response, and lessons learned. - Identifies six groups of topographical characteristics. - Defines terms to represent properties of a crisis.</td></tr><tr><td>[24]</td><td>The paper presents a nine-phase approach for integrating an ontology into a software system, covering database schema and ontology schema conversion, model fusion, proposition of new functionalities, implementation and testing, periodic mapping, reasoner application, verification of new facts, and system maintenance. The paper uses a real-world case study involving the "ESI Clever Network" competency location system for a computer science school to demonstrate the effectiveness of this approach. Integrating the ECAO (ESI Competency Application Ontology) avoids ambiguities and inconsistencies in the system. Tools like Protégé UML Backend and Ontop are used for ontology and relational model conversion and mapping.</td><td>The paper presents a lengthy integration approach for a software system, involving nine manual phases and requiring stakeholder participation. The process requires periodic execution to enrich the database with new information. Selecting an appropriate ontology is a significant challenge, and the approach requires disjointed tool use. Long-term evaluation is needed as the system matures. The paper acknowledges the potential loss of expressivity during conversion to relational databases and the need for joint effort to identify equivalences and overlaps. The ontology integration is presented as a promising solution to these challenges.</td></tr><tr><td>[25]</td><td>The paper proposes a system for automatically generating questionnaires from domain ontologies, aiming to estimate the quality and suitability of ontologies. It details a five-step approach for ontology validation, starting with questionnaire derivation and ending with ontology updates based on expert feedback. The system represents ontology components in natural language through questionnaires, allowing domain experts to evaluate the content. It establishes a link between ontology quality criteria and possible questionnaire answers. The system's implementation uses J2EE, JSP, Apache Tomcat, PostGreSQL, and the Jena API. An experiment showed a global total adequacy of over 79% for tested ontologies.</td><td>The paper highlights challenges ontology developers face, including the lack of support for question templates in natural languages other than English, ambiguous generated questions, and lengthy questionnaires. It also emphasizes the need for larger-scale testing with ontology designers, domain experts, and ontologies of varying size, domain, and language. Future improvements include considering OWL 2 features, supporting multiple natural languages for questionnaire generation beyond English, and exploring Natural Language Generation (NLG) techniques for deriving questionnaires from ontologies.</td></tr><tr><td>[26]</td><td>- Proposes a web application that provides the most cited authors and publications related to a research field to support researchers in literature review. - The approach combines data from academic search engine APIs and linked data knowledge bases. - Develops a knowledge base to memorize relevant citation knowledge. - Classifies research areas based on well-established classifications.</td><td>- Mentions that citation numbers can differ from one dataset to another. - Reports technical limitations faced during development, mainly limited memory space when handling many instances.</td></tr><tr><td>[28]</td><td>- Highlights the lack of task-based assessment of enterprise business model ontologies via case studies. - Aims to tackle this issue by proposing indicators for measuring the impact of business model ontologies in the framework of enterprise business models. - Reviews and compares main business model ontologies (REA, BMO, E3-value). - Discusses potential benefits and challenges of ontologies for enterprise applications.</td><td>- Notes that indications of the impact of business model ontologies do not ease assessment of added value. - Points out that the studied ontologies are deficient in task-based assessment. - Mentions that the benefits of ontologies for enterprises have not yet been experimentally confirmed. - Notes the difficulty in addressing task-based assessment using the case study approach.</td></tr><tr><td>[29]</td><td>- Focuses on building a competency ontology (ECAO) for a computer science school. - Aims to improve the school's competency location system. - Will provide unique definitions for relevant concepts. - Illustrates key concepts of the ontology.</td><td>- Mentions that measuring competencies is the most challenging task in such a system.</td></tr><tr><td>[30]</td><td>- The paper proposes a five-phase approach for translating competency questions into SPARQL queries: identifying the question's category, determining the expected answer, extracting entities from the question and answer, identifying the answer entity type and location in the ontology, and constructing the appropriate SPARQL query. The paper applies this approach to the HERO (Higher Education Reference Ontology), providing examples of different categories of competency questions (Definition, Yes/No, Factual, List, Complex) and their corresponding SPARQL queries.</td><td>A limitation shared by described web Question Answering (QA) approaches is scalability, as they are often tested with relatively small ontologies. Approaches that rely on a dictionary mapping questions' concepts to knowledge base items are tricky to implement and maintain, especially for large knowledge bases. The proposed approach has two crucial phases, entirely manual and dependent on user knowledge: entity extraction from questions/answers and mapping these terms to ontology entities. The paper recognizes a weak treatment of complex questions, where ontology annotations are often considered the best answers rather than more precise information. It highlights the need for more empirical evaluation to assess the approach's performance and effectiveness.</td></tr><tr><td>[32]</td><td>The paper introduces the Higher Education Reference Ontology (HERO), a reusable and generalizable academic knowledge resource. It defines reference ontology as representing knowledge about a domain independently of specific objectives. The paper compares reference ontologies with foundational, core, and application ontologies, highlighting their distinct characteristics and roles. HERO ontology building follows the NeOn methodology, combining development from scratch with reusing non-ontological and</td><td>The authors acknowledge the time-consuming and challenging ontology-building process involving multiple phases and potential forms. Due to its broad coverage, developing a reference ontology requires more effort than developing domain or application ontologies. Ensuring consensus and widespread terminology recognition is challenging, and achieving agreement among domain experts and end-users is an ongoing goal.</td></tr></table>
<table><tr><td></td><td>ontological resources. The requirements specification phase involves identifying competency questions and classifying them. HERO ontology evaluation is conducted through expert agreement and task assessment.</td><td></td></tr><tr><td>[33]</td><td>- The paper explains the HERO ontology building process, detailing the steps from requirements specification to evaluation using the NeOn methodology. It presents a concrete example of a reference ontology in higher education, HERO, and demonstrates the application of the NeOn methodology. The paper also discusses the "Middle-Out" approach to competency questions, identifying 81 questions to detect HERO requirements. The paper also details HERO's structural, functional, and usability-related evaluations, focusing on structural, functional, and usability. The work suggests that HERO can serve as a university profiling and strategy development instrument and potentially a non-discriminatory ranking tool.</td><td>The paper addresses the challenges of ontologies being too specific and not reusable, hindering information sharing and reuse. To address this issue, a reference ontology like HERO should be developed. The complexity of the higher education domain necessitates a combination of development from scratch with reuse-oriented strategies. The evaluation phase involves expert feedback, leading to updates in the ontology. The level of granularity is crucial for relevance. The authors also highlight the difficulty of evaluating ontologies and using consensus terminology in a diverse domain like higher education.</td></tr><tr><td>[34]</td><td>- Addresses the issue of misperception between terminology and ontology. - Explores how these knowledge representations can benefit from each other's characteristics, specifically terminology reuse in ontology building. - Provides a global comparison between ontologies and terminologies based on fundamental principles and methodological aspects. - Examines different levels of formality. - Focuses on case studies of reusing terminologies.</td><td>The paper highlights the lack of clarity and misperception surrounding these terms, which can hinder research. Building ontologies is more time-consuming and costly than terminologies due to their complexity and formality. Terminologies often cannot take advantage of implicitly available knowledge, have limited reusability due to semantic ambiguity, and lack expressive mechanisms for complex knowledge. Ontologies face limitations in representing meaning, computational efficiency is not always guaranteed, and complex top-level ontologies may not be suitable for real-world applications. Ontologies often reflect the viewpoints of their developers, and as ontologies grow in size and applications become more complex, inconsistencies in design and development become more likely. It also emphasizes the challenge of selecting the appropriate level of formality for both ontologies and terminologies, as choosing an inappropriate level can negatively impact the sharing scope and stability of the knowledge resource.</td></tr><tr><td>[35]</td><td>- Proposes to compare a syntactic and a semantic ontology matching system. - Aims to evaluate the progress achieved by each type of system in solving matching problems. - The goal is to help designers improve matching quality of both system types. - Provides a manual matching as a reference. - Compares the systems based on pre-matching and post-matching effort.</td><td>- Notes that most matching systems are syntactic. - Reports bad results with initial combination parameters in the semantic matching system.</td></tr><tr><td>[36]</td><td>- Briefly mentions that some research papers give tracks to derive specific ontologies from a reference ontology. - Examples include using semantic web query languages or enriching terminologies. - Summarizes that main research areas benefit from reference ontologies.</td><td>The paper highlights the information heterogeneity caused by the increasing number of available ontologies on the web, which hinders information sharing and reuse. Many existing domain ontologies are too specific and not robust enough for large applications, causing bottlenecks in Semantic Web research and application development. A consensus definition of "Reference ontology" is another challenge. Building a Reference ontology involves complex and time-consuming processes, and standard criteria for transparent ontology evaluation and harmonization are lacking. It also discusses the challenge of separating application ontologies from Reference ontologies and the need for formal methods for this transformation.</td></tr></table>

Three references have not been included in Table 1 because they are either master's theses ([31]), doctoral theses ([27]), or textbooks on the Semantic Web ([16]). Based on the central ideas of the papers in Table 1, Table 2 presents a proposed classification of these papers according to their topics.

<table><caption>Table 2 Papers' classification according to some semantic web topics</caption><thead><tr><td>Ontology and Relational Database Interaction</td><td>Theoretical Discussions on Ontologies</td><td>Ontology design and development</td><td>Ontology Evaluation and Quality</td><td>Semantic Web Applications</td><td>Linked Data Transformation and Publishing</td><td>OGD</td></tr></thead><tbody><tr><td>[20], [18] [24]</td><td>[34], [36], [22], [1] [13] [6] [23]</td><td>[23], [29], [34], [33], [11] [24] [9] [35] [32] [16] [27] [31]</td><td>[28], [15], [33], [25] [22] [21] [9] [8] [30]</td><td>[3] [26] [17] [10]</td><td>[3], [14], [26], [17] [3] [10] [12] [21] [7] [4] [14] [19]</td><td>[5] [3] [2] [9] [12] [7]</td></tr></tbody></table>
Explanation of the Categories:

*   **Theoretical Discussions on Ontologies:** These papers engage in more conceptual discussions. This includes defining and comparing ontologies with terminologies, exploring the concept of reference ontologies, discussing context modeling using ontologies, and comparing different knowledge representation languages like OWL and SysML.

*   **Ontology Design and Development:** These papers delve into creating ontologies. They discuss methodologies, identify relevant concepts and relationships for specific domains, and explore the reuse of existing repapers like terminologies and other ontologies in the development process. [33] details the development of a specific reference ontology.

*   **Ontology Evaluation and Quality:** These papers address the crucial aspect of assessing the quality and suitability of ontologies. They explore different methods, such as using competency questions for validation, proposing metrics, and developing tools for evaluation via questionnaires.

*   **Ontology and Relational Database Interaction:** These papers focus on the interplay between ontologies and relational databases. They discuss their differences and similarities and propose approaches for converting between these two data representation models.

*   **Semantic Web Applications:** These papers showcase practical applications of Semantic Web technologies. This includes using natural language processing to query ontologies, building web applications that leverage Linked Data for tasks like citation analysis, and generating web pages from Linked Data.

*   **Linked Data Transformation and Publishing:** These papers focus on the practical aspects of converting existing data into Linked Data format and making it available on the web. They discuss methodologies, tools, and challenges involved in this process. [3] specifically proposes an approach for OGD to LOD conversion, while [14] reviews existing approaches for this transformation. [17] explores using Linked Data repapers to generate web pages.

*   **OGD:** These papers show the potential of OGD and the good practices in terms of publication, exploitation, and transformation into LOD.

Some papers belong to two categories, such as [3], which describes the potential of OGD and proposes an automatic approach for transforming OGD to LOD. In addition, two publications are master and doctoral theses ([31], [27]), and one publication is a textbook on the Semantic Web ([16]).

From Table 1 and Table 2, Figure 1 represents the main semantic web challenges as a mind map.

Figure 2 illustrates the repartition of the papers among the domains. We can notice that most publications are dedicated to LOD publishing. This focus is explained by the fact that I have supervised three master's theses and a doctoral dissertation on these topics. On the other hand, ontology design and development, as well as evaluation, was the central topic of my doctoral thesis.

Figure 3 illustrates the number of publications per year. This figure demonstrates the regularity in the rhythm of papers publication with peaks in three years being years preceding the grade passages, namely: in 2013 (one year before my doctoral thesis defense), in 2018 (one year before my university accreditation defense) and 2024 (hopefully one year before my promotion to the rank of professor). This shows that a researcher's academic career strongly impacts and paces his scientific production, or at least this is the case for me. At the same time, I am slowly but surely shifting towards the field of computer science for engineering, given the orientation of my affiliated school, the National School of Advanced Technologies.

Figure 4 visually represents how these topics are interconnected and influence each other within Semantic Web technologies and knowledge representation. The solid arrows with triangular heads indicate a more direct "is-a" or "leads-to" relationship, while the dashed arrows with open circles indicate a dependency or association.
<figure><img src="image_2.png" /><figcaption>Figure 1 Semantic Web challenges according to the papers</figcaption></figure>

<figure><img src="image_3.png" /><figcaption>Figure 2 Number of papers by topic</figcaption></figure>
<figure><img src="image_4.png" /><figcaption>Figure 3 publications per year</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>Figure 4. Class Diagram of the interplay between subdomains</figcaption></figure>

What follows is an explanation of the relationships depicted:

* **Ontology Design and Development** --|> **Linked Data Transformation and Publishing**: Ontology design provides the foundational models and vocabularies that are often the target format for transforming data into Linked Data.
* **Ontology Design and Development** > **Semantic Web Applications**: Semantic Web applications heavily rely on well-designed ontologies as their underlying knowledge bases to provide meaning and structure to the data.

* **Ontology Evaluation and Quality** --o **Ontology Design and Development**: The quality of an ontology is a critical aspect of its design and development. Evaluation methods help ensure the ontology meets its intended purpose and is consistent.

* **Ontology Evaluation and Quality** --o **Linked Data Transformation and Publishing**: Evaluating the quality of the Linked Data produced is essential, and this often involves assessing how well it aligns with the underlying ontologies.

* **Ontology Evaluation and Quality** -Semantic Web Applications: The performance and reliability of Semantic Web applications depend on the quality of the ontologies and data they utilize.

* **Ontology and Relational Database Interaction**—|> Linked Data Transformation and Publishing: Converting relational databases to RDF (the basis of Linked Data) is a significant publishing method.

* **Linked Data Transformation and Publishing**>|> **Semantic Web Applications**: Many Semantic Web applications consume and utilize data published as Linked Data from various papers.

* **Ontology Design and Development**>|> **Theoretical Discussions on Ontologies**: The practical processes of ontology design and development are informed by ontology's theoretical underpinnings and principles.

* **Linked Data Transformation and Publishing**>|> **OGD**: The principles and techniques of Linked Data transformation can be applied to make Open Government Data more accessible and interoperable.

* **Semantic Web Applications**>|> **Ontology Design and Development**: Semantic Web applications are built upon and interact with ontologies to perform tasks like question answering and data integration.

* **Semantic Web Applications**—|> **Linked Data Transformation and Publishing**: These applications often utilize data that has been transformed and published using Linked Data principles.

* **OGD** --o **Linked Data Transformation and Publishing**: OGD is a domain where applying Linked Data transformation techniques is highly relevant for improving data sharing and access.

### 3. Framework of some semantic web subdomains

Based on the papers (Figures 5, 6, and 7), three frameworks for some semantic web subdomains (ontology design and development, Linked Data transformation and publishing, and semantic web applications) can be proposed.

#### a. Ontology Design and Development

##### 1. What (activities):

a. **Ontology Specification**: Defining the purpose, scope, level of formality, target group, intended uses, and requirements (often expressed as competency questions) of the ontology. This results in an Ontology Requirements Specification Document (ORSD).

b. **Ontology Conceptualization**: Identifying, organizing, and structuring the domain knowledge into concepts, relations, axioms, and instances. This often involves using natural language initially and then translating it into a model using formalisms like Entity/Relationship or UML.

c. **Ontology Formalization (Implementation)**: Translating the conceptual model into a formal language such as RDF or OWL, including defining axioms to constrain possible interpretations and enable inference.

d. **Ontology Evaluation**: Verify and validate the ontology against the defined requirements, often using competency questions translated into SPARQL queries.

e. **Ontology Maintenance**: Continuously enriching and updating the ontology with new knowledge from relevant sources.

f. **Ontology Reuse and Reengineering**: Utilizing existing ontologies or non-ontological resources, adapting, and extending them for a new purpose. This includes extracting and integrating relevant fragments and using terms' definitions as concept annotations.

g. **Ontology Integration**: Combining ontologies from different sources requires resolving differences and finding common ground.
<figure><img src="image_6.png" /><figcaption>Figure 5 Ontology Design and Development framework</figcaption></figure>

## 2. Who (Stakeholders):

* **Ontology Engineers:** They are Responsible for the technical aspects of building and maintaining the ontology.

* **Domain Experts:** Provide the knowledge and expertise about the modeled domain.

* **End-users:** Their intended uses and competency questions drive the requirements and evaluation of the ontology.

* **System Developers:** They may be involved in integrating ontologies into software systems.

* **Institutional Sponsors:** Influence context, purpose, and scope decisions.

## 3. How (Means and Process):

* **Ontology Specification:** Defining the ORSD, identifying competency questions (potentially using NeOn methodology), and analyzing business requirements and use cases.

* **Ontology Conceptualization:** Brainstorming terms, analyzing glossaries and data dictionaries, using graphical representations (e.g., UML).

* **Ontology Formalization:** Utilizing ontology editing tools (e.g., Protégé), selecting appropriate ontology languages (RDF, OWL), and defining axioms using formal language.

* **Ontology Evaluation:** Translating competency questions into SPARQL queries, executing them against the ontology, and using ontology evaluation tools.

* **Ontology Maintenance:** Regularly reviewing and updating the ontology based on new information and feedback.
* **Ontology Reuse and Reengineering:** Searching for existing ontologies, aligning and mapping concepts, and using methodologies that support reuse (e.g., NeOn).

* **Ontology Integration:** Collaborative discussions to merge relational models derived from ontologies, defining mappings between ontologies.

4. **Why (Motivation or Purpose):**

* To share a common understanding of information.

* To enable the reuse of domain knowledge.

* To make domain assumptions explicit.

* To separate domain knowledge from operational knowledge.

* To analyze domain knowledge.

* To reduce ambiguities and misunderstandings.

* To enhance knowledge bases via inference.

* To support data integration, interoperability, and federation.

* To provide a frame of reference for data.

* To overcome limitations of Semantic Web resources by formalizing context.

* To support reasoning and consistency checking.

* To assist in decision-making through querying knowledge bases.

* To build applications that leverage semantic data.

* To validate that an ontology adequately covers its domain.

5. **When and Where (time and location):**

* **Throughout the Ontology Development Lifecycle:** Specification is the initial phase, followed by conceptualization, formalization, and evaluation. Maintenance is an ongoing process.

* **Various Locations:** Activities can occur in meetings, workshops, or using collaborative software tools. Reuse can happen by accessing online ontology repositories. Integration may involve distributed teams.

b. Linked Data Generation, Publication, and Exploitation

<figure><img src="image_7.png" /><figcaption>Figure 6 LD Generation, publication, and exploitation framework</figcaption></figure>
1. What (activities):

o **Linked Data Generation**: Transforming data from various formats (e.g., CSV, Excel, HTML, PDF, TXT) into RDF triples, often involving Named Entity Recognition (NER) and linking to existing vocabularies or knowledge bases (e.g., DBpedia) using URIs.

o **Linked Data Publication**: Making the generated RDF data accessible on the Web using standard Web technologies (HTTP, URIs) and Semantic Web technologies (RDF, SPARQL). This includes ensuring URI dereferenceability.

o **Linked Data Exploitation (Consumption)**: Utilizing the published linked data for various applications, such as web portals with automatically updated content, search engines, recommendation systems, data integration, and generating visualizations and statistics through SPARQL queries.

o **Linked Data Evaluation**: Assessing the quality and adherence to linked data principles (URI naming, URI dereferencing, providing helpful information using standards like RDF and SPARQL, and linking to other data). Also involves evaluating data accessibility.

2. Who (Stakeholders):

o **Data Providers**: Organizations or individuals who want to publish their data as linked data.

o **Web Developers**: Build applications that consume linked data.

o **Semantic Web Experts**: Provide the technical knowledge for linked data generation and publication.

o **End-users**: Benefit from applications and services built on linked data.

o **Government Organizations**: Publish OGD as LOD.

3. How (Means and Process):

o **Linked Data Generation**: Using tools and approaches to extract data, map it to RDF, and link entities to URIs (e.g., using NER techniques and DBpedia lookup). This can be manual, semi-automatic, or fully automatic.

o **Linked Data Publication**: Making RDF data available at dereferenceable URIs, providing SPARQL endpoints for querying.

o **Linked Data Exploitation**: Querying RDF data using SPARQL, developing web and mobile applications that consume RDF data, and using linked data for data integration and knowledge discovery.

o **Linked Data Evaluation**: Using validator tools to check URI syntax and dereferenceability, verifying RDF syntax, and assessing adherence to the four linked data principles. Evaluating data accessibility based on performance, availability, and security.

4. Why (Motivation or Purpose):

o To make data machine-readable and interpretable.

o To enable automatic data processing and integration.

o To create a global data space with interconnected data from different sources.

o To improve data discoverability, accessibility, and exploitation.

o To enhance web pages with semantic meaning.

o To facilitate the development of intelligent applications and services.

o To unlock the full potential of Web data by overcoming heterogeneity and decentralization.

o To support Open Government Data initiatives by making government information more accessible and usable.

5. When and Where (time and location):

o **Data Transformation**: Occurs as a step to prepare data for the Semantic Web.

o **Publication**: Happens online, making data globally accessible.

o **Exploitation**: Can occur at any time through web applications and services.

o **Evaluation**: Can be performed after publication to ensure quality and compliance.

c. Semantic Web Applications

1. What (types):

a. **Semantic Web Portals**: Web applications that use ontologies and linked data to organize and present information, enabling better navigation and access to knowledge.

b. **Knowledge Bases**: Repositories of information organized using semantic web technologies, allowing for querying and reasoning.

c. **Data Integration Systems**: Utilize ontologies to provide a common semantic framework for integrating heterogeneous data sources.

d. **Question Answering Systems**: Translate natural language questions into SPARQL queries to retrieve answers from knowledge bases and ontologies.
e. **Linked Data Applications**: A wide range of applications that consume and utilize linked data for tasks like search, recommendations, visualizations, and data analysis.

f. **Smart Homes**: Utilize ontologies for activity recognition and managing smart home devices.

g. **Industry 4.0**: Leveraging semantic web technologies for data integration, consistency verification, and enhanced data accessibility in industrial contexts.

<figure><img src="image_8.png" /><figcaption>Figure 7 SW applications framework</figcaption></figure>

* **Who (Stakeholders):**

* **Various Domain Users:** Benefit from improved access to information and knowledge.

* **Organizations:** Use semantic web applications for knowledge management, data integration, and decision support.

* **Researchers:** Develop new semantic web applications and techniques.

* **How (Means and Process):**

* **Leveraging Semantic Web Technologies:** Using RDF to represent data, OWL for ontologies, and SPARQL for querying.

* **Developing Specific Algorithms and Frameworks** For tasks like question answering or data integration.

* **Integrating Ontologies into Software Systems** To enhance their functionality and data understanding.

* **Following Linked Data Principles:** To build applications that seamlessly discover and integrate data.

* **Why (Motivation or Purpose):**

* To improve information access and retrieval.

* To enable more intelligent data processing and analysis.

* To facilitate data sharing and interoperability across different systems and domains.
* To create new functionalities and services by leveraging the interconnected data web.

* To address specific challenges in various domains like healthcare, government, and industry.

* **When and Where (time and location):**

* **Deployed on the Web:** Accessible through internet browsers.

* **Used within Organizations:** For internal knowledge management and data integration.

## 4. KPIs for enhancing the practicability of the proposals

Some concrete and practical KPIs (qualitative and quantitative) for improving the practicability of proposals within each of the subdomains.

### Ontology Design and Development

* Adherence to a recognized ontology development methodology (e.g., NeOn Methodology, Methontology): Indicates a structured and potentially more robust development process.

* Number of iterations and revisions during the ontology development lifecycle: This can indicate the level of refinement and expert involvement.

* Coverage of the ontology scope as defined by competency questions and use cases: Measures how well the developed ontology meets the initial requirements.

* Clarity and completeness of ontology documentation (metadata, conceptual diagrams, term definitions) enhance understandability and facilitate reuse.

* Level of tool support utilized during ontology design and development (e.g., ontology editors like Protégé, reasoners): This reflects established best practices and technologies.

* Time and resources required for the ontology design and development process: Measures the efficiency of the development effort.

* The number of domain experts and ontology engineers involved in the development process indicates the level of collaboration and expertise brought to the task.

* Number of unresolved conflicts or disagreements among stakeholders during conceptualization: Can highlight potential areas of ambiguity or complexity in the domain.

* Modularity and reusability of ontology components: Assesses the potential for extending and adapting the ontology for different purposes.

### Ontology Evaluation and Quality

* Percentage of competency questions answered correctly by the ontology (after translation to SPARQL if necessary): This directly measures the ontology's ability to represent the domain knowledge accurately and comprehensively.

* Number of inconsistencies or contradictions detected by logical reasoners: Indicates the formal quality and coherence of the ontology.

* Evaluation scores based on Linked Data principles (Principle 1, 2, 3, and 4 rates): Measures the ontology's readiness for integration within the Semantic Web and its potential for reuse.

* Metrics related to ontology structure and complexity (e.g., number of classes, properties, depth of hierarchy): Can provide insights into the maintainability and understandability of the ontology, though these should be considered about the domain's complexity.

* The level of agreement between domain experts and ontology developers on the accuracy and completeness of the ontology (measured through surveys or validation sessions): This reflects the ontology's consensual nature.

* Number of identified ambiguities or non-pertinent elements in the ontology: Indicates potential issues affecting clarity and usability.

* Rate of reusing ontology terms and structures from existing, well-established ontologies: Measures adherence to best practices and promotes interoperability.

* Time and effort required for ontology verification and validation: Assesses the efficiency of the evaluation process.

* Improvement in evaluation scores after applying correction methods: Measures the effectiveness of quality enhancement efforts.

### Ontology and Relational Database Interaction
* Efficiency of data transformation between relational databases and ontologies (e.g., time taken for conversion, data loss): Measures the practical feasibility of data integration.

* Performance of SPARQL queries executed against ontology-backed relational databases: Assesses the efficiency of data retrieval and knowledge inference.

* Level of semantic loss during the conversion between databases and ontologies: Measures preserving meaning and relationships.

* The complexity and maintainability of mapping rules between database schemas and ontology structures affect the integration's long-term viability.

* Number of database instances successfully mapped to ontology concepts: Indicates the extent of data integration achieved.

* The frequency of updates and synchronization between the ontology and the underlying database Measures how well the integrated systems stay current.

* Number of applications successfully leveraging the integrated ontology and database: Demonstrates the practical utility of the interaction.

* Scalability of the ontology-database interaction with increasing data volume: This assesses the integrated system's ability to handle large datasets.

## Semantic Web Applications

* User satisfaction with the functionality and usability of Semantic Web applications: Directly measures the practical value for end-users.

* Improvement in task performance when using Semantic Web applications compared to traditional methods: Demonstrates the added value of semantic technologies.

* Number of unique users or sessions for Semantic Web applications: Indicates the level of adoption and engagement.

* The frequency and type of SPARQL queries generated by or for the application show the extent to which the underlying knowledge base is utilized.

* Accuracy and relevance of results provided by Semantic Web applications (e.g., in question answering or recommendation systems): Measures the quality of the application's output.

* Response time of Semantic Web applications to user requests: Assesses the efficiency and responsiveness of the applications.

* Data integration from multiple heterogeneous sources within the application demonstrates Semantic Web technologies' power for data integration.

* The level of automation in tasks enabled by the Semantic Web application (e.g., data linking, knowledge inference) highlights the efficiency gains provided by the technology.

## Linked Data Transformation and Publishing

* Percentage of OGD datasets transformed into Linked Data format (e.g., RDF): This directly measures the progress of transformation efforts.

* Number of interlinks created between the published Linked Data and external datasets: This indicates the level of adherence to the Linked Data principles and the potential for data integration.

* Rate of URI dereferenceability for published Linked Data: This KPI assesses whether the URIs used to identify entities and concepts are resolvable, a key aspect of Linked Data.

* Adherence to RDF validation standards for published Linked Data: Measures the syntactic correctness and machine-readability of the transformed data.

* Time and resources required for end-to-end Linked Data transformation and publishing: This KPI assesses the efficiency and practicability of the transformation process.

* The number of unique queries executed against the published Linked Data indicates the usage and exploitation of the published data.

* User satisfaction with the accessibility and usability of published Linked Data (measured through surveys or feedback): Directly gauges the practical value of the publishing efforts for data consumers.

* Coverage of key vocabularies and ontologies in the published Linked Data: Assesses the reuse of established semantic standards, enhancing interoperability.

* The number of applications or services built leveraging the published Linked Data demonstrates the transformed and published data's real-world impact and practical utility.

OGD, in particular for a specific context or country
* The number of OGD datasets published on the OGD portal (if applicable) and their format diversity: This measures open data's basic availability.

* Accessibility of OGD datasets according to accessibility metrics (e.g., machine-readability, availability in open formats): This assesses how easily the data can be accessed and used.

* The number of datasets published as Linked Open Data on the portal: This number tracks explicitly the adoption of Semantic Web standards.

* Frequency of updates for published OGD datasets (Timeliness): Measures how current and relevant the data remains.

* Availability of transparent and open licenses for OGD datasets: This is Crucial for enabling reuse.

* The number of applications or services developed using OGD (if trackable) demonstrates the practical exploitation of the data.

* User engagement with the OGD portal (e.g., number of downloads, API usage): Indicates the level of interest and activity.

* Assessment of data quality dimensions (e.g., accuracy, completeness, consistency) for published OGD: Ensures the reliability and usefulness of the data.

* Technical support and institutional structures in place for OGD initiatives: Indicates the sustainability and potential for growth of OGD efforts.

## Theoretical Discussions on Ontologies (the most tricky to assess)

* The number of citations and influence of theoretical work on practical ontology development and application (measured through academic databases) indicate the impact and relevance of theoretical discussions in the field.

* Clarity and consistency of ontology definitions and foundational principles discussed in theoretical work are essential for building a solid understanding of ontologies.

* Extent to which theoretical frameworks address practical challenges in ontology engineering (e.g., scalability, maintenance, evaluation): Shows the practical relevance of theoretical discussions.

* Development of new methodologies or best practices directly inspired by theoretical insights: Demonstrates the translation of theory into actionable guidance.

* The number of research projects or tools that explicitly adopt or build upon theoretical ontology models indicates the practical uptake of theoretical concepts.

These KPIs should be defined with specific measurement criteria and targets to effectively track progress and enhance the practicability of proposals in each category. Selecting KPIs most relevant to the work's particular goals and context.

## 5. Conclusion and Future Work

The primary conclusions from these papers underscore the crucial importance of Semantic Web technologies, especially LOD and ontologies, in improving data accessibility, interoperability, and knowledge representation across several fields. Numerous publications concentrate on converting OGD into LOD, suggesting approaches such as LOD-GePEx and emphasizing compliance with the four linked data principles to facilitate machine interpretability and comprehensive data analysis.

The utility of ontologies for knowledge representation in business models, predictive maintenance, manufacturing processes, and crisis management is examined. It highlights their ability to integrate and reason semantically while recognizing the difficulties in demonstrating their impact and choosing suitable representation languages such as OWL and SysML.

Additionally, the papers explore methodologies for querying ontologies through natural language, underscoring the intricacies of semantic mapping and the necessity for query relevance.

They also discuss the coexistence and transformation between ontologies and relational databases or terminologies, accentuating their complementary advantages and the absence of standardized mapping protocols.

The practical application of linked data is exemplified through case studies such as the BBC's web portal, which illustrates how linked data can dynamically produce semantically rich web content, alongside evaluations of Open Government Data initiatives in specific contexts, highlighting areas for enhancement by international standards like the FAIR principles.

Future work in linked data transformation and publishing should prioritize adopting FAIR data principles, leveraging AI technologies, and evaluating metadata quality. Generic end-to-end solutions should be developed, and publication guidelines should be improved.
Interdisciplinary collaboration is suggested to overcome challenges in using ontologies in machine learning. Further research is needed to address the lack of standardization in formats and provenance for Open Government Data.

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