Semantic web models such as ontologies and knowledge graphs were designed to represent knowledge explicitly and to infer implicit knowledge. This brings several benefits like flexibility and reasoning power. On the other hand, since the adoption of these models has been growing during the last years, the demand for consistency has risen in parallel. Therefore, the validation of these representations started to be an important issue for research. Several types of related works have been done; however, there is still a lack of an integrated review to understand the state of the art of formal semantic constraint validation. Addressing this issue is crucial for broader adoption of both the models discussed and the Semantic Web itself. For the community, it is important to review existing progress, outline future avenues, and examine related areas that can benefit from these developments. The contribution of this article includes a taxonomy of related research trends, a classification of selected works into these categories, and an overview of the main open challenges in validating semantic constraints for the Semantic Web. Each of these contributions was obtained through the conduction of a systematic literature review. In addition, an extension of the study is done to consider a very promising model which has a growing intersection with the field: property graphs. Finally, this paper concludes with an outlook that summarizes the contributions made and the challenges that the authors decided to continue researching in further steps.

A real estate observatory plays a significant role in the aggregation and analysis of real estate market data. The information that lies in real estate advertisements can be leveraged to populate such an observatory. However, this data can present itself in both a structured and an unstructured manner. Unstructured data represents a problem to automatically process and extract information since it lacks a predefined structure. Thus, there's a need for techniques to give structure to unstructured data. Information Extraction (IE) is the process of structuring data from unstructured data. Natural Language Processing techniques enable machines to understand texts, making them particularly significant in the context of IE. This work evaluates both rule-based and machine-learning based IE approaches to extract features from real estate descriptions within advertisements. Those features are relevant in the context of real estate observatory construction. The performance of each approach is measured using precision, recall and f1-score metrics.

This paper introduces RDF Graph GPT app as a tool to automatically translate natural language texts into RDF format knowledge graphs using Artificial Intelligence. Throughout its reading we will dissect and elaborate the various aspects of the functionality mentioned above, as well as some more that this app provides such as graph visualization of any RDF format text with the possibility to choose between two graph types. It also presents an overview of the connection with the AI, more precisely Chat GPT, and the prompts structure that leads to the desired results. The app was tested using different types of natural language texts and different prompts in order to make a richer analysis of the results. This evaluation process was made by experts who gave us some conclusions about the scope and limitations of the tool. It has promising results that show us how interesting this task is along with the potential that the resultant app has. This app, then, puts the focus on the IA as a resource on the matter of developing powerful tools for translating natural language texts into structured formats such as RDF. A very desired task to do automatically due to its implications and applications in the matter of adding semantics to the data.

Decision-making can be significantly enhanced by knowledge extraction techniques such as Formal Concept Analysis (FCA), which provides a structured way to analyze and organize data. However, in distributed environments, information is often fragmented, arriving in streams rather than as a complete dataset. Consulting the entire dataset at once is not always feasible due to time and resource constraints. While an existing batch algorithm allows concept lattice computation without requiring full attribute knowledge, batch processing is inherently unsuitable for dynamic, continuously evolving data. In this article, we propose an incremental algorithm for concept lattice computation in arbitrarily distributed formal contexts, which can efficiently update the lattice as new information becomes available. Our method ensures that knowledge extraction remains computationally feasible even when data is incomplete, distributed, or evolving over time. We further analyze the complexity of our approach in comparison to the existing batch-based distributed algorithm, highlighting its advantages in stream processing scenarios. By addressing the limitations of batch computation and enabling real-time lattice updates, our work contributes to enhancing FCA's applicability in distributed and dynamic knowledge systems. The proposed incremental approach paves the way for more adaptive, efficient, and scalable knowledge extraction methods, particularly in fields requiring real-time decision-making and pattern discovery.

The Cognitive Digital Twin is an advanced version of the Digital Twin model. It integrates cognitive computing technologies to create systems that not only connect but also reason, learn from past experiences, and make informed decisions. The integration of machine learning algorithms and artificial intelligence allows cognitive digital twins to process and interpret data. This cognitive capability enables the digital twin to function with a layer of intelligence that mimics human cognitive abilities, making the system adaptable to its environment and capable of handling complex decision-making processes autonomously. The cognitive features of cognitive digital twins are crucial as they enable the system to predict future states, identify potential problems before they occur, and suggest mitigating actions. Furthermore, the use of semantic web technologies can facilitate advanced analytics and machine learning within cognitive digital twins. This article offers a rapid literature analysis of how Semantic Web approaches can support several aspects of cognitive digital twins models.

This paper presents an adaptive gamification pattern catalog based on a PBL (Points, Badges, and Leaderboard) gamification model for collaborative location and time-based collecting systems (CLCS). Nine patterns are presented that adapt either the game's mechanics or components. One of the adaptive gamification patterns was implemented in a citizen science platform for evaluation. Following the gamification design heuristics, an expert inspection and a player experience assessment with end users were developed. Player experience evaluation compared two scenarios: a non-adaptive and an adaptive setup. Results showed the applicability of the presented patterns and game model.

This article is an extension of a work carried out in 2023 where the activities carried out within the framework of the extension project of the Faculty of Informatics of the National University of La Plata (UNLP) entitled "Citizen Science in the census of urban trees" were presented. Here, in a complementary way, the new contributions proposed from a qualitative and interdisciplinary perspective and the results obtained in the experience are mentioned.

In a knowledge society, the term knowledge must be considered a core resource for organizations. So, beyond being a medium to progress and to innovate, knowledge is one of our most important resources: something necessary to decide.Organizations that are embracing knowledge retention activities are gaining a competitive advantage. Organizational rearrangements from companies, notably outsourcing, increase a possible loss of knowledge, making knowledge retention an essential need for them. When Knowledge is less shared, collaborative decision-making seems harder to obtain insofar as a “communication breakdown” characterizes participants' discourse. At best, stakeholders have to finda consensus according to their knowledge. Sharing knowledge ensures its retention and catalyzes the construction of this consensus. Our vision of collaborative decision-making aims not only at increasing the quality of the first parts of the decision-making process: intelligence and design, but also at increasing the acceptance of the choice. Intelligence and design will be done by more than one individual and constructed together; the decision is more easily accepted. The decided choice will then be shared. Thereby where decision-making could be seen as a constructed model, collaborative decision-making, for us,is seen as the use of socio-technical media to improve decision-making performance and acceptability. The shared decision making is a core activity in a lot of human activities. For example, the sustainable decision-making is the job of not only governments and institutions but also broader society. Recognizing the urgent need for sustainability, we can argue that to realize sustainable development, it must be considered as a decision-making strategy. The location of knowledge in the realization of collaborative decision-making has to be regarded insofar as knowledge sharing leads to improve collaborative decision-making: a “static view” has to be structured and constitutes the “collaborative knowledge.” Knowledge has an important role in individual decision-making, and we consider that for collaborative decision-making, knowledge has to be shared. What is required is a better understanding of the nature of group work”. Knowledge has to be shared, but how do we share knowledge?

Software test case design is one of the most challenging activities since many actors with different backgrounds must cover most of the user's needs and expectations. In the agricultural domain tasks can be done in very different ways since practices vary worldwide. Thus, in this context, it is very hard to design test cases to validate requested functionality that automatizes some farm tasks. This paper proposes an approach to make the testing step easier, designing User Acceptance Tests (UATs) from requirements captured through scenarios. The scenarios capture the knowledge of different stakeholders (farmers) and using natural language processing tools, the approach proposed to consolidate the set of scenarios in a consistent and coherent base of knowledge organised in a tree, from where the design of test cases is extracted using the Task/Method model, a tool from the Artificial Intelligence.

The Internet of Things massive adoption in many industrial areas in addition to the requirement of modern services is posing huge challenges to the field of data mining. Moreover, the semantic interoperability of systems and enterprises requires to operate between many different formats such as ontologies, knowledge graphs, or relational databases, as well as different contexts such as static, dynamic, or real time. Consequently, supporting this semantic interoperability requires a wide range of knowledge discovery methods with different capabilities that answer to the context of distributed architectures (DA). However, to the best of our knowledge there is no general review in recent time about the state of the art of Concept Analysis (CA) and multi-relational data mining (MRDM) methods regarding knowledge discovery in DA considering semantic interoperability. In this work, a systematic literature review on CA and MRDM is conducted, providing a discussion on the characteristics they have according to the papers reviewed, supported by a clusterization technique based on association rules. Moreover, the review allowed the identification of three research gaps toward a more scalable set of methods in the context of DA and heterogeneous sources.

Scenarios are ideal to capture knowledge in human computer interface software engineering. Requirements engineering is a fundamental part of software development. If errors appear in this stage, it will be expensive to correct them in further stages. The domain experts and the developer team belong to different worlds. This generates a gap in communication between them. Because of it, it is important to use artifacts in natural language to communicate both sides. One simpler approach to specify requirements is Scenarios. They are widely used artifacts that generally describe the dynamics (tasks, activities) to be carried out in some specific situation. Generally, scenarios promote communication and participation from both sides. This can cause some problems. One of these problems is redundancy, that occurs when two stakeholders describe the same situation in different artifacts. This paper proposes an approach to analyze a set of scenarios by grouping them according to their similarity. The similarity is calculated through a series of comparisons of the different attributes of the scenario. This paper also describes a prototype implementing this method. Finally, the paper shows the result of a preliminary evaluation with results about the applicability of the approach.

This article presents an innovative approach to developing a strategy for situational awareness in an adaptive gamification framework within the context of Collaborative location-based collecting systems (CLCS). The proposed approach involves incorporating five key factors that represent user behavior and context in the adaptive gamification process for CLCS. These factors include player preferences, player status, gamified activities, groupware activities, and project goal status. Each factor is crucial in describing various aspects of the framework and groupware interaction that players should be aware of during their game experience. By acquiring knowledge about these five axes, users can analyze and react appropriately to various situations. This modeling approach enables the early development of awareness and facilitates decision-making. Ultimately, this article serves as a useful guide for improving existing frameworks and enhancing the overall user experience through situational awareness.

Collaborative innovation is a dynamic process that involves diverse individuals and organizations pooling resources and working together to develop new ideas, products, or services. Successful collaboration in networked innovation projects is challenging due to the need to cross the knowledge boundaries that exist between organizations, disciplines, and cognitive frames. This paper proposes an approach aimed at facilitating knowledge mobilization and fostering learning in the intricate landscape of networked innovation projects. Stored in a shared repository, scenarios serve as a foundation for collaborative processes, guiding participants toward a shared understanding and the construction of mutual meaning. A pivotal aspect of this approach is the inclusive engagement of Stakeholders in a collaborative decision-making process of scenario ranking that includes identifying and negotiating comparison criteria. Although the approach is presented with examples in the domain of agriculture, where validation of the constituent elements took place, its adaptability renders it domain-independent offering a robust framework for collaborative innovation across various sectors.

El diseño de casos de prueba es una de las actividades más desafiantes en el contexto de la ingeniería de requerimientos, dado que implica la colaboración de diferentes individuos con variados conocimientos en el dominio y perspectivas, con el objetivo de desarrollar un producto que satisfaga las expectativas y necesidades de los clientes. El modelo de desarrollo en V propone abordar el diseño de casos de prueba a partir de los requerimientos. Aunque constituye un punto de partida valioso, no siempre resulta sencillo obtener una especificación de requerimientos ordenada y consistente, que describa de manera integral toda la funcionalidad del sistema de software. Los escenarios se presentan como un artefacto efectivo para la especificación de requerimientos. Consisten en descripciones en lenguaje natural que delinean una secuencia de pasos desde un contexto o punto de partida específico hasta un objetivo o meta determinados. Los escenarios son atómicos, lo que implica que diferentes expertos pueden describir distintos escenarios en función de su conocimiento y perspectiva. Sin embargo, para el diseño efectivo de los casos de prueba, resulta crucial organizar los escenarios en una estructura jerárquica que facilite la identificación sistemática de todos los casos que necesitan ser evaluados. Este artículo propone una herramienta que permite la edición de escenarios y utiliza diversas técnicas de procesamiento de lenguaje natural para organizarlos en un árbol. Este enfoque se fundamenta en la relación “un escenario se describe con otro escenario”, lo que posibilita la generación de un árbol exhaustivo que abarque toda la funcionalidad del sistema. Posteriormente, la herramienta facilita la poda de ramas no deseadas para limitar el árbol a la funcionalidad que sea de interés evaluar. Finalmente, a partir del árbol resultante, se obtienen los casos de prueba. Es importante señalar que las bases teóricas y metodológicas para la generación de casos de prueba han sido presentadas en publicaciones previas; sin embargo, este trabajo se centra en el desarrollo y aplicación de una herramienta que automatiza dicho proceso, contribuyendo así a la eficiencia y precisión de la fase de pruebas en el ciclo de desarrollo de software.

Human or manual transcription is the task where a person reads a handwritten document and types in a digital environment the text she is reading. The manual transcription task is a long and time-consuming process for one person. However, the intelligence of the transcriber is provided with better results than automatic alternatives. This article introduces a collaborative transcription platform called Transcriptor, in which the community members can upload digitized manuscripts and collaborate in transcribing them, defining different transcription layers which will be represented using semantic web technologies and gamification techniques. The article provides two experimentation that shows that Transcriptor had a good acceptance and novelty.

Smart Enterprises, Smart Manufacturing, and Cyber-Physical Systems are gaining traction in many industry areas. On top of that, the amounts of available data grow rapidly, and organizations are eager to exploit their advantages. To accomplish that, it is mandatory to have a wide variety of methods and algorithms for knowledge extraction in order to fit the different needs and problems of the industry. In this study, we review and dissect the current state of the art in knowledge extraction applied to smart enterprises, smart manufacturing, and cyber-physical systems. More specifically, we provide a classification of the characteristics of the available methods in the literature according to their applications, and point out areas of improvement.

Se estudian acoplamientos anómalos del boson de Higgs con los bosónes vectoriales neutros Z y γ en el marco de una teoría efectiva parametrizada por medio de operadores de dimensión seis invariantes ante el grupo de simetrías SU (2) Y ⊗ U (1) L . Se acotan los parámetros fWW /Λ2 , f BB/Λ2, f BW/Λ2 y fΦ, 1 /Λ2 mediante resultados experimentales, y se estudian posibles desviaciones del modelo estándar originadas por la nueva física en los decaimientos H → γγ, H → γ Z.

Thanks to the internet of things (IoT) and cyber physical systems (CPS), we face an incremental growth of the available data, either on the internet or in private databases. This resulted in data min-ing techniques becoming an essential piece in the information retrieval process. Moreover, trends like the industry 4.0 encourages its usage to support data driven decisions, for instance. Formal Concept Analysis (FCA) is one of the most used techniques in the unsupervised data min-ing field due to its inherent ability to find patterns between concepts. As a consequence, many applications need the use of fast algorithms to perform the calculations to retrieve either the lattice or the association rules related with the data at their disposal. Due to this, scientists often rely on manually crafted benchmarks to compare how certain algorithms perform under different circumstances. In this work, we propose the ar-chitecture of a software to generalize these benchmarks independently of the algorithms, to be integrated in the open source data analysis software Orange3.