The Journal of Socio-Economics

Sustainability in systems refers to applying sustainable principles and practices to create more resilient, efficient, and equitable systems that promote the well-being of people and the planet. Sustainability is an essential topic in contemporary software engineering, and its relationship with the characteristics and properties of a system or product called quality attributes is still an open question since each researcher has established their definition of sustainability in software. This has created diverse terms and concepts for distinct application environments and scopes, creating ambiguity and misconceptions. This work defines a domain ontology of Sustainability in Software named SinSO to address these issues. SinSO was implemented in OWL, using competency-based questions to validate. The findings show that this proposal satisfies several quality and content requirements. Also, using Protégé and the Hermit reasoner, we verified that SinSO is consistent since the ontology statements are coherent and do not lead to conflicting or contradictory conclusions. In addition, competency questions allowed us to demonstrate that SinSO does fulfill its purpose. FOCA methodology allowed us to evaluate SinSO quality. Also, SinSO was used in two case studies, one about software for senior-citizen smart-home, and the other, a simulator to develop and test smart-city applications, achieving positive outcomes. To verify its accuracy, completeness, and maintainability, further evaluations of SinSO are needed in real case studies. We conclude that SinSO can significantly contribute to reducing ambiguity and enhancing comprehension in this area. Furthermore, SinSO can be an effective tool for engineers to recognize the concepts and relationships in the sustainable domain to consider in the systems development life cycle to build sustainable systems.

Ontologies play a vital role in organizing and constructing knowledge across various domains, enabling effective knowledge management and sharing. The development of domain-specific ontologies, such as the ONTO-TDM ontology for teaching domain modeling, is essential for providing a comprehensive and standardized representation of knowledge within a given discipline. However, to maximize the usefulness and relevance of such ontologies, it is crucial to automate their population with domain-specific information, reducing manual work and ensuring scalability. This paper presents a novel method for ontology population by extracting and integrating relevant information from diverse sources. The method combines the TextRank algorithm with Word2Vec to enhance keyword extraction, capturing both semantic meaning and textual importance. Keywords are then annotated and used to train a machine learning classifier, which aids in integrating new instances into the ontology. Experiments show that the proposed method achieves a precision of 63.33%, a recall of 61.29% and an F1-score of 62.28%, significantly improving keyword extraction and ontology population accuracy compared to existing methods. This validates the method’s effectiveness in semi-automatically extracting relevant instances from diverse data sources, enhancing the efficiency and accuracy of ontology population, and advancing automated knowledge management in domain-specific contexts.

In the recruitment process, manually selecting suitable candidates from curriculum vitae (CVs) for a job description (JD) is both time-consuming and expensive. Traditional keyword-based methods struggle to capture skill semantics, prompting the development of more advanced JD-CV matching systems. This paper aims to investigate and construct an ontology-based skills recommendation system, with objectives including creating a skills ontology and developing skills matching methods for JD-CV pairs. The objective of our approach is to enhance the accuracy and contextual relevance of recommendations by utilizing the proposed score. The proposed skills ontology and skills matching strategies are applied to a real dataset in Vietnam. The results of our study can automatically recommend a list of CVs for a given JD. Furthermore, the findings indicate that our proposed model surpasses comparative approaches by a margin of at least 1% to 5%. Overall, the study demonstrates the potential of utilizing ontology-based approaches to offer a practical solution for enhancing hiring practices.

Classifying domain entities into their respective top-level ontology concepts is a complex problem that typically demands manual analysis and deep expertise in the domain of interest and ontology engineering. Using an efficient approach to classify domain entities enhances data integration, interoperability, and the semantic clarity of ontologies, which are crucial for structured knowledge representation and modeling. Based on this, our main motivation is to help an ontology engineer with an automated approach to classify domain entities into top-level ontology concepts using informal definitions of these domain entities during the ontology development process. In this context, we hypothesize that the informal definitions encapsulate semantic information crucial for associating domain entities with specific top-level ontology concepts. Our approach leverages state-of-the-art language models to explore our hypothesis across multiple languages and informal definitions from different knowledge resources. In order to evaluate our proposal, we extracted multi-label datasets from the alignment of the OntoWordNet ontology and the BabelNet semantic network, covering the entire structure of the Dolce-Lite-Plus top-level ontology from most generic to most specific concepts. These datasets contain several different textual representation approaches of domain entities, including terms, example sentences, and informal definitions. Our experiments conducted 3 study cases, investigating the effectiveness of our proposal across different textual representation approaches, languages, and knowledge resources. We demonstrate that the best results are achieved using a classification pipeline with a K-Nearest Neighbor (KNN) method to classify the embedding representation of informal definitions from the Mistral large language model. The findings underscore the potential of informal definitions in reflecting top-level ontology concepts and point towards developing automated tools that could significantly aid ontology engineers during the ontology development process.

In this paper, we showcase our research on the use of ontologies and information extraction for the purpose of modeling damages incurred on car bodies. With the increasing use of technology in the automotive industry, it is important to have a standardized and efficient way of documenting and analyzing car damage reports. Most existing reports are unstructured, and there is a lack of standardization in describing the damage. To address this issue, we have developed a domain ontology for car damage modeling ( OCD), 1 1 industryportal.enit.fr/ontologies/OCD , 2 2 github.com/OntologyCarDamage/OCD and proposed an end-to-end system to extract information from French automotive reports. The information extraction process involves using named entity recognition (NER) and relationship extraction (RE) techniques to identify and extract relevant information from the reports. Then, the extracted information is used to populate the [Formula: see text] ontology, allowing a structured and standardized representation of the damage information. The proposed system was tested on a real dataset of automotive reports and showed promising results.

In 2013, Bennett proposed a mereological theory in which the parthood relation is defined on the basis of two primitive relations: a is a part of b iff a fills a slot owned by b. However, this theory has issues counting how many parts an entity has. We explore the various counting problems and propose a new theory to solve them. Keeping the core idea of Bennett’s slots, this theory introduces mereological relations between slots. This theory enables us to solve all known counting problems and to go beyond the limits of Bennett’s theory by theorising expected features of mereological theories: supplementation principles and mereological sum and fusion. The theory is illustrated on ontological issues on the nature of structural universals and informational entities.

Decentralised ledgers are gaining momentum following the interest of industries and people in smart contracts. Major attention is paid to blockchain applications intended for trading assets that exploit digital cryptographic certificates called tokens. Particularly relevant tokens are the non-fungible tokens (NFTs), namely, unique and non-replicable tokens used to represent the cryptographic counterpart of assets ranging from pieces of art through to licenses and certifications. A relevant consequence of the hard-coded nature of blockchains is the hardness of probing, in particular when advanced searchers involving the capabilities of the smart contracts or the assets digitised by NFTs are required. For this purpose, a formal representation for the operational semantics of smart contracts and of tokens has become particularly urgent, especially in economy and finance, where blockchains become increasingly relevant. Hence, we feel the need to tailor Semantic Web technologies to achieve that semantic representation at least for NFTS. This article reports on an ontology that leverages the Ontology for Agents, Systems, and Integration of Services (“OASIS”) towards the semantic representation of smart contracts responsible for managing ERC721-compliant NFTs and running on the Ethereum blockchain. Called Ether-OASIS, the proposed ontology adopts OASIS and tailors its behaviouristic approach to the Ethereum blockchain by conceiving smart contracts as agents running on the blockchain and, consequently, smart contract interactions as agent commitments. Smart contracts are represented in terms of their actions, purposes and tokens that they manage, thus realising a blockchain that is more usable both by users and automated applications. The ontology is evaluated using standard ontological metrics and applied on a case study concerning the minting and transferring of NFTs that digitise batches of wheat.

This paper examines the concept of “terminological gaps” and strives to identify suitable methods for dealing with them during translation. The analysis begins with an investigation of the contended notion of gaps in terminology based on empirical examples drawn from a German-Italian terminological database specifically designed for translation purposes. Two macro categories of gaps are identified, conceptual and linguistic level gaps, which only partially correspond to previous observations in the literature. The paper uses examples to explore the advantages of ontological representations for detecting conceptual terminological gaps and identifying appropriate translation strategies. However, limitations are also observed and an attempt is made to resolve these using a frame-based approach. A frame-based analysis reveals that while certain designations may appear to refer to convergent conceptual units with matching distinctive features, differences also emerge due to the way the two language systems build designations. Examples from the corpus make it evident that a frame-based approach is helpful for identifying both kinds of terminological gaps, and then resolving them during translation. An important presupposition for this approach is that larger units of analysis need to be addressed rather than just terms themselves. There is confirmation of the existing idea that methods embracing entire segments or paragraphs as units of investigation are preferable during translation, and this is also seen to apply in terminological studies.

In the study of special languages and translation, the legal field is often insulated from other domains. This is primarily due to the extreme system dependence of the terminology of law, which results from a lack of a common legal system of reference throughout the world. The abstract nature of this human-made field and its dynamicity in view of the continuously evolving case law and constant changes in legislation make it difficult to illustrate its complex ontology through traditional terminology management techniques. Therefore, this paper argues for an interdisciplinary approach to constructing the ontology of legal concepts based on structural constituents from frame semantics and comparative law. Frames allowing for the representation of interconnected knowledge segments evoked by legal concepts and the distinction between micro- and macro-dimensions in legal comparison research make it possible to capture the complex ontology of legal terminology evoked in a specific point in time and a determined legal context. The ontological knowledge structure will be exemplified by terms from German social, commercial, employment, and tax law.

This paper explains conceptual modeling within the framework of Frame-Based Terminology (Faber, 2012; 2015; 2022), as applied to EcoLexicon (ecolexicon.ugr.es), a specialized knowledge base on the environment (León-Araúz, Reimerink &, Faber, 2019; Faber & León-Araúz, 2021). It describes how a frame-based terminological resource is currently being restructured and reengineered as an initial step towards its formalization and subsequent transformation into an ontology. It also explains how the information in EcoLexicon can be integrated in environmental ontologies such as ENVO (Buttigieg, Morrison, Smith, Mungall & Lewis, 2013; Buttigieg, Pafilis, Lewis, Schildhauer, Walls & Mungall, 2016), particularly at the bottom tiers of the Ontology Learning Layer Cake (Cimiano, 2006; Cimiano, Maedche, Staab & Volker, 2009). The assumption is that frames, as a conceptual modeling tool, and information extracted from corpora can be used to represent the conceptual structure of a specialized domain.

In the last few decades, the study of terminology has undergone a cognitive shift that has led to the development of several approaches that study the social, linguistic, and cognitive dimension of terms, such as Communicative Theory of Terminology (CTT) and Frame-Based Terminology (FBT). CTT was developed in the early 1990s and argues that the study of terminology should be based on a communicative perspective, taking into account aspects such as the communicators and the context of communication. FBT has been developed from 2007 and uses certain aspects of Frame Semantics to conceptualise specialised domains and create non-language-specific representations through the analysis of the domain event and on the study of the behaviour of the terminological units in texts. The two theories share many of the same premises and propose the representation of the concepts of a domain in an ontology. FBT also proposes a representation in frames. We explore how these two methods of domain representation can be used to represent the terminology of the domain of seafood in Germany and Spain.

This contribution reflects on the current role of ontologies in terminology research and practice and their future role, especially with a view to the creation of fully digital terminographic resources. The very notion of (domain) ontology, its concept and term, is discussed, highlighting metaterminological differences and substantial ambiguities arising from the interdisciplinary contact between Ontology Engineering and Terminology. Major challenges in ontology building, e.g. subjectivity, are mentioned, also with respect to the distinction between realist and non-realist ontologies and their relevance in Terminology. In addition, this contribution presents some examples of terminology resources with a distinct ontological component, showing a diversity of approaches depending on the purpose of the resource and its scope. In this context, more specific topics are addressed, such as the acquisition of ontological data and suitable formats and models for representing domain knowledge. The contribution ends with a vision of the integration of complex concept systems such as ontologies in future terminology work: here, the development of models based on terminology-specific requirements and typical users will be fundamental.

Ontologies are the prime way of organizing data in the Semantic Web. Often, it is necessary to combine several, independently developed ontologies to obtain a complete representation of a domain of interest. The complementarity of existing ontologies can be leveraged by merging them. Existing approaches for ontology merging mostly implement a binary merge. However, with the growing number and size of relevant ontologies across domains, scalability becomes a central challenge. A multi-ontology merging technique offers a potential solution to this problem. We present Co Merger, a scalable multiple ontologies merging method. It takes as input a set of source ontologies and existing mappings across them and generates a merged ontology. For efficient processing, rather than successively merging complete ontologies pairwise, we group related concepts across ontologies into partitions and merge first within and then across those partitions. In both steps, user-specified subsets of generic merge requirements (GMRs) are taken into account and used to optimize outputs. The experimental results on well-known datasets confirm the feasibility of our approach and demonstrate its superiority over binary strategies. A prototypical implementation is freely accessible through a live web portal.

The labor market is an area with diverse data structures and multiple applications, such as matching job seekers with the right training or job. For this reason, the multilingual classification of European Skills, Competences, Qualifications and Occupations (ESCO) is a good example of the central role of ontologies in this area. However, ESCO cannot provide all the details of local labor market needs and does not provide links to other hierarchies of competences. For example, other taxonomies of occupations and skills are widely used in German-speaking countries, but they are not in a state where they are easily accessible for interoperability and reasoning. In this paper, we present a first version of a German Labor Market Ontology (GLMO) that uses ESCO as a top-level ontology for the target domain. This makes it highly interoperable and comparable to existing ontologies by providing details for the regional structures in German-speaking countries. In addition, we present a detailed evaluation of the provided data and applications, as well as an extensive discussion of future work.