As schema languages for RDF data become more mature, we are seeing efforts to extend them with recursive semantics, applying diverse ideas from logic programming and description logics. While ShEx has an official recursive semantics based on greatest fixpoints (GFP), the discussion for SHACL is ongoing and seems to be converging towards least fixpoints (LFP). A practical study we perform shows that, indeed, ShEx validators implement GFP, whereas SHACL validators are more heterogeneous. This situation creates tension between ShEx and SHACL, as their semantic commitments appear to diverge, potentially undermining interoperability and predictability. We aim to clarify this design space by comparing the main semantic options in a principled yet accessible way, hoping to engage both theoreticians and practicioners, especially those involved in developing tools and standards. We present a unifying formal semantics that treats LFP, GFP, and supported model semantics (SMS), clarifying their relationships and highlighting a duality between LFP and GFP on stratified fragments. Next, we investigate to which extent the directions taken by SHACL and ShEx are compatible. We show that, although ShEx and SHACL seem to be going in different directions, they include large fragments with identical expressive power. Moreover, there is a strong correspondence between these fragments through the aforementioned principle of duality. Finally, we present a complete picture of the data and combined complexity of ShEx and SHACL validation under LFP, GFP, and SMS, showing that SMS comes at a higher computational cost under standard complexity-theoretic assumptions.

Property graphs are widely used to model complex, interconnected data, but flexible property annotations often undermine consistency due to weak schema support. We introduce PG-Schema-PC, an extension of PG-Schema for constraints over property sets, covering structural, cardinality, and range constraints (including alternative groupings, multiplicity control, and value conditions). We formalize its abstract grammar and illustrate its use with practical examples.

Graphs have emerged as a foundation for a variety of applications, including capturing factual knowledge, semantic data integration, social networks, and informing machine learning algorithms. Formalising properties of the data and ensuring data quality requires describing schemas of such graphs. Driven by diverse applications, the Semantic Web and database communities developed not only different graph data models-RDF and property graphs-but also different graph schema languages-SHACL, ShEx, and PG-Schema. Each language has its unique approach to defining constraints and validating graph data, leaving potential users in the dark about their commonalities and differences. In this paper, we provide concise formal definitions of the core components of these languages, employ a uniform framework to facilitate a comprehensive comparison between them, and identify a common set of functionalities, shedding light on both overlapping and distinctive features.

Several problems arise due to the differences between dentistry and general medicine. The storage of dental data in information silos, the incompatibility of data between different dental clinics or institutions from other medical areas are the most significant ones. The authors propose a decentralized architecture that combines FHIR archetypes, shape expressions, and personal online datastores (PODs) to tackle those issues as follows: FHIR archetypes are used to express the data, shape expressions are used to handle data structure and data access requests, and PODs are used to store information in a decentralized and safe manner that let the owner of the information stored to handle data access. The system allows the patient to store dental information from heterogeneous data sources transparently and respecting the patient's right to autonomy and consent. In this paper, the authors develop this architecture proposal and discuss its relevance and feasibility in the area of dental health.

AppWesomed is a website developed to test the suggestion of SNOMED CT terms in medical reports. When the user is writing the report, the web shows the terms that match the last written word. The use of SNOMED CT in medical applications is important because it can facilitate the integration of medical records. The tests were conducted with twenty-five people, divided into two groups, fifteen doctors and eleven non-doctors. The average age of the groups was 45 years and 32 years, respectively. A survey designed according to the Likert scale was used to test the website usability, the results of the survey were used to calculate the Cronbach's Alpha whose value was 0.894. According to the results, 44% of the participants considered the website usability as "Good", 36% as "Excellent" and 20% as "Regular". The app has been positively evaluated by the surveyed doctors. In response to the general experience, doctors highlight the great application utility, especially in their own research studies for the data analysis in medical reports.

This paper describes the system architecture for generating the History of the Law developed for the Chilean National Library of Congress (BCN). The production system uses Semantic Web technologies, Akoma-Ntoso, and tools that automate the marking of plain text to XML, enriching and linking documents. These documents semantically annotated allow to develop specialized political and legislative services, and to extract knowledge for a Legal Knowledge Base for public use. We show the strategies used for the implementation of the automatic markup tools, as well as describe the knowledge graph generated from semantic documents. Finally, we show the contrast between the time of document processing using semantic technologies versus manual tasks, and the lessons learnt in this process, installing a base for the replication of a technological model that allows the generation of useful services for diverse contexts.

Abstract We discuss Shape Expressions (ShEx), a concise, formal, modeling and validation language for RDF structures. For instance, a Shape Expression could prescribe that subjects in a given RDF graph that fall into the shape “Paper” are expected to have a section called “Abstract”, and any ShEx implementation can confirm whether that is indeed the case for all such subjects within a given graph or subgraph. There are currently five actively maintained ShEx implementations. We discuss how we use the JavaScript, Scala and Python implementations in RDF data validation workflows in distinct, applied contexts. We present examples of how ShEx can be used to model and validate data from two different sources, the domain-specific Fast Healthcare Interoperability Resources (FHIR) and the domain-generic Wikidata knowledge base, which is the linked database built and maintained by the Wikimedia Foundation as a sister project to Wikipedia. Example projects that are using Wikidata as a data curation platform are presented as well, along with ways in which they are using ShEx for modeling and validation. When reusing RDF graphs created by others, it is important to know how the data is represented. Current practices of using human-readable descriptions or ontologies to communicate data structures often lack sufficient precision for data consumers to quickly and easily understand data representation details. We provide concrete examples of how we use ShEx as a constraint and validation language that allows humans and machines to communicate unambiguously about data assets. We use ShEx to exchange and understand data models of different origins, and to express a shared model of a resource’s footprint in a Linked Data source. We also use ShEx to agilely develop data models, test them against sample data, and revise or refine them. The expressivity of ShEx allows us to catch disagreement, inconsistencies, or errors efficiently, both at the time of input, and through batch inspections. ShEx addresses the need of the Semantic Web community to ensure data quality for RDF graphs. It is currently being used in the development of FHIR/RDF. The language is sufficiently expressive to capture constraints in FHIR, and the intuitive syntax helps people to quickly grasp the range of conformant documents. The publication workflow for FHIR tests all of these examples against the ShEx schemas, catching non-conformant data before they reach the public. ShEx is also currently used in Wikidata projects such as Gene Wiki and WikiCite to develop quality-control pipelines to maintain data integrity and incorporate or harmonize differences in data across different parts of the pipelines.

In this paper, we propose an architecture that combines Big Data and Stream Processing which can be applied to the Real Estate Domain. Our approach consists of a specialisation of Lambda architecture and it is inspired by some aspects of Kappa architecture. As a proof of this solution, we show a prototype developed following it and a comparison of the three architecture quality models. Finally, we highlight the differences between the proposed architecture and similar ones and draw some future lines following the present approach.

The proliferation of cost-effective sensors and devices which continually generate data as well as the ecosystem of tools and software applications has led to the term concept of big data. Although...

In this paper, the authors describe Musical Entities Reconciliation Architecture (MERA), an architecture designed to link music-related databases adapting the reconciliation techniques to each particular case. MERA includes mechanisms to manage third party sources to improve the results and it makes use of semantic technologies, storing and organizing the information in RDF graphs. They have implemented a prototype of their approach and have used it to link sources with different levels of data quality. The prototype has been effective in more than 94% of the cases under the conditions of their experiments. The authors have also compared their prototype with a well-known music-specialized search engine, outperforming the search results in the two experiments that they performed.

Chilecompra is the Chilean governmental agency in charge of Chile's Public Procurement System. One of its main current problems is that around 54% (supply indicator) of the public procurements that are submitted get less than three bids, which is the minimum required so that the procurement process may be free of technical, legal and ethical questionings. This article presents a method based on Natural Language Processing and Semantic Analysis in order to automatically identify prospective bidding candidates from a historical application dataset with a software tool which implements this method. The results are proven by using a dataset of procurement data and historical applications, biddings and adjudications of the suppliers. We compared the number of suppliers that applied for a tender and the number of prospective bidders identified by this method, and the domain procurement experts proved that the identified bidders were relevant to the new procurement.

Graph data abstractions are often assumed to be intuitive, but experience shows that they are not equally understandable or usable in practice. In this vision and challenges paper, we examine the human-centricity of contemporary graph data abstractions through four lenses: researchability, usability, teachability, and societal impact. Drawing on diverse real-world use cases, ranging from clinical data and collaborative knowledge bases to biological and pangenomic graphs, we distill insights from database research, human-computer interaction, and education. Based on this analysis, we identify open research challenges that must be addressed to make graph abstractions easier to study, use, learn, and reason about.

This paper aims to describe a public procurement information platform which provides a unified pan-European system that exploits the aggregation of tender notices using linking open data and semantic web technologies. This platform requires a step-based method to deal with the requirements of the public procurement sector and the open government data initiative: (1) modeling the unstructured information included in public procurement notices (contracting authorities, organizations, contracts awarded, etc.); (2) enriching that information with the existing product classification systems and the linked data vocabularies; (3) publishing relevant information extracted out of the notices following the linking open data approach; (4) implementing enhanced services based on advanced algorithms and techniques like query expansion methods to exploit the information in a semantic way. Taking into account that public procurement notices contain different kinds of data like types of contract, region, duration, total amount, target enterprise, etc., various methods can be applied to expand user queries easing the access to the information and providing a more accurate information retrieval system. Nevertheless expanded user queries can involve an extra-time in the process of retrieving notices. That is why a performance evaluation is outlined to tune up the semantic methods and the generated queries providing a scalable and time-efficient system. Moreover, this platform is supposed to be especially relevant for SMEs that want to tender in the European Union (EU), easing their access to the information of the notices and fostering their participation in cross-border public procurement processes across Europe. Finally an example of use is provided to evaluate and compare the goodness and the improvement of the proposed platform with regard to the existing ones.

Automated medical diagnosis systems based on knowledge-oriented descriptions have gained momentum with the emergence of Semantic Descriptions. However, soundness and efficiency of the underlying logics in these descriptions are critical to harness the potential of these systems. In this paper, we provide a well-structured ontology for automated diagnosis and a three-fold formalization based on Predicate Logic, Description Logic and Rules.

We describe the main features of an Internet based distance learning environment that we are developing to teach a course on logic and functional programming for computer science students. The main goal of the system is to provide a minimal set of capabilities for a generic interpreter that will be instantiated for different programming languages and paradigms. The architecture of the system is based on the use of XML and web services to facilitate the integration and independence between the different development platforms and systems.