Shapes Constraint Language Research Articles

Formalizing and validating Wikidata’s property constraints using SHACL and SPARQL

In this paper, we delve into the crucial role of constraints in maintaining data integrity in knowledge graphs with a specific focus on Wikidata, one of the most extensive collaboratively maintained open data knowledge graphs on the Web. The World Wide Web Consortium (W3C) recommends the Shapes Constraint Language (SHACL) as the constraint language for validating Knowledge Graphs, which comes in two different levels of expressivity, SHACL-Core, as well as SHACL-SPARQL. Despite the availability of SHACL, Wikidata currently represents its property constraints through its own RDF data model, which relies on Wikidata’s specific reification mechanism based on authoritative namespaces, and – partially ambiguous – natural language definitions. In the present paper, we investigate whether and how the semantics of Wikidata property constraints, can be formalized using SHACL-Core, SHACL-SPARQL, as well as directly as SPARQL queries. While the expressivity of SHACL-Core turns out to be insufficient for expressing all Wikidata property constraint types, we present SPARQL queries to identify violations for all 32 current Wikidata constraint types. We compare the semantics of this unambiguous SPARQL formalization with Wikidata’s violation reporting system and discuss limitations in terms of evaluation via Wikidata’s public SPARQL query endpoint, due to its current scalability. Our study, on the one hand, sheds light on the unique characteristics of constraints defined by the Wikidata community, in order to improve the quality and accuracy of data in this collaborative knowledge graph. On the other hand, as a “byproduct”, our formalization extends existing benchmarks for both SHACL and SPARQL with a challenging, large-scale real-world use case.

Enhancing the viewing, browsing and searching of knowledge graphs with virtual properties

Purpose Adequate means for easily viewing, browsing and searching knowledge graphs (KGs) are a crucial, still limiting factor. Therefore, this paper aims to present virtual properties as valuable user interface (UI) concept for ontologies and KGs able to improve these issues. Virtual properties provide shortcuts on a KG that can enrich the scope of a class with other information beyond its direct neighborhood. Design/methodology/approach Virtual properties can be defined as enhancements of shapes constraint language (SHACL) property shapes. Their values are computed on demand via protocol and RDF query language (SPARQL) queries. An approach is demonstrated that can help to identify suitable virtual property candidates. Virtual properties can be realized as integral functionality of generic, frame-based UIs, which can automatically provide views and masks for viewing and searching a KG. Findings The virtual property approach has been implemented at Bosch and is usable by more than 100,000 Bosch employees in a productive deployment, which proves the maturity and relevance of the approach for Bosch. It has successfully been demonstrated that virtual properties can significantly improve KG UIs by enriching the scope of a class with information beyond its direct neighborhood. Originality/value SHACL-defined virtual properties and their automatic identification are a novel concept. To the best of the author’s knowledge, no such approach has been established nor standardized so far.

Enhancing Semantic Web Technologies Using Lexical Auditing Techniques for Quality Assurance of Biomedical Ontologies

Semantic web technologies (SWT) represent data in a format that is easier for machines to understand. Validating the knowledge in data graphs created using SWT is critical to ensure that the axioms accurately represent the so-called “real” world. However, data graph validation is a significant challenge in the semantic web domain. The Shapes Constraint Language (SHACL) is the latest W3C standard developed with the goal of validating data-graphs. SHACL (pronounced as shackle) is a relatively new standard and hitherto has predominantly been employed to validate generic data graphs like WikiData and DBPedia. In generic data graphs, the name of a class does not affect the shape of a class, but this is not the case with biomedical ontology data graphs. The shapes of classes in biomedical ontology data graphs are highly influenced by the names of the classes, and the SHACL shape creation methods developed for generic data graphs fail to consider this characteristic difference. Thus, the existing SHACL shape creation methods do not perform well for domain-specific biomedical ontology data graphs. Maintaining the quality of biomedical ontology data graphs is crucial to ensure accurate analysis in safety-critical applications like Electronic Health Record (EHR) systems referencing such data graphs. Thus, in this work, we present a novel method to create enhanced SHACL shapes that consider the aforementioned characteristic difference to better validate biomedical ontology data graphs. We leverage the knowledge available from lexical auditing techniques for biomedical ontologies and incorporate this knowledge to create smart SHACL shapes. We also create SHACL shapes (baseline SHACL graph) without incorporating the lexical knowledge of the class names, as is performed by existing methods, and compare the performance of our enhanced SHACL shapes with the baseline SHACL shapes. The results demonstrate that the enhanced SHACL shapes augmented with lexical knowledge of the class names identified 176 violations which the baseline SHACL shapes, void of this lexical knowledge, failed to detect. Thus, the enhanced SHACL shapes presented in this work significantly improve the validation performance of biomedical ontology data graphs, thereby reducing the errors present in such data graphs and ensuring safe use in the life-critical applications referencing them.

Semantic rule checking of cross-domain building data in information containers for linked document delivery using the shapes constraint language

Information exchange is a crucial process throughout the lifecycle of built assets. Building Information Modeling (BIM) and standardized information delivery enable exchanging, storing, and linking cross-domain information bundled in containers such as the standardized Information Container for Linked Document Delivery (ICDD). While assessing the quality of the interlinking and metadata is essential to allow all parties to interpret an information container correctly, most research efforts have focused on code compliance of domain-specific models. In order to address this gap, this paper proposes an approach for validating ICDD information containers using the Shapes Constraint Language (SHACL), the W3C recommendation for Resource Description Framework (RDF) data validation. For optimized querying and validation, a case study applies SHACL inference rules to the linking structure of ICDD to infer predicates between linked entities in four ICDD containers in varying dataset sizes and different linking methods. Afterward, the validation of these containers is analyzed using three types of validation rules with increasing complexity for another SHACL implementation. Results show the feasibility of cross-domain building information validation in ICDD with SHACL and how performance enhancements can be obtained on an inferred dataset compared to the original dataset.

Learning SHACL shapes from knowledge graphs

Knowledge Graphs (KGs) have proliferated on the Web since the introduction of knowledge panels to Google search in 2012. KGs are large data-first graph databases with weak inference rules and weakly-constraining data schemes. SHACL, the Shapes Constraint Language, is a W3C recommendation for expressing constraints on graph data as shapes. SHACL shapes serve to validate a KG, to underpin manual KG editing tasks, and to offer insight into KG structure. Often in practice, large KGs have no available shape constraints and so cannot obtain these benefits for ongoing maintenance and extension. We introduce Inverse Open Path (IOP) rules, a predicate logic formalism which presents specific shapes in the form of paths over connected entities that are present in a KG. IOP rules express simple shape patterns that can be augmented with minimum cardinality constraints and also used as a building block for more complex shapes, such as trees and other rule patterns. We define formal quality measures for IOP rules and propose a novel method to learn high-quality rules from KGs. We show how to build high-quality tree shapes from the IOP rules. Our learning method, SHACLearner, is adapted from a state-of-the-art embedding-based open path rule learner (Oprl). We evaluate SHACLearner on some real-world massive KGs, including YAGO2s (4M facts), DBpedia 3.8 (11M facts), and Wikidata (8M facts). The experiments show that our SHACLearner can effectively learn informative and intuitive shapes from massive KGs. The shapes are diverse in structural features such as depth and width, and also in quality measures that indicate confidence and generality.

Property Assertion Constraints for ontologies and knowledge graphs

PurposeThe curation of ontologies and knowledge graphs (KGs) is an essential task for industrial knowledge-based applications, as they rely on the contained knowledge to be correct and error-free. Often, a significant amount of a KG is curated by humans. Established validation methods, such as Shapes Constraint Language, Shape Expressions or Web Ontology Language, can detect wrong statements only after their materialization, which can be too late. Instead, an approach that avoids errors and adequately supports users is required.Design/methodology/approachFor solving that problem, Property Assertion Constraints (PACs) have been developed. PACs extend the range definition of a property with additional logic expressed with SPARQL. For the context of a given instance and property, a tailored PAC query is dynamically built and triggered on the KG. It can determine all values that will result in valid property value assertions.FindingsPACs can avoid the expansion of KGs with invalid property value assertions effectively, as their contained expertise narrows down the valid options a user can choose from. This simplifies the knowledge curation and, most notably, relieves users or machines from knowing and applying this expertise, but instead enables a computer to take care of it.Originality/valuePACs are fundamentally different from existing approaches. Instead of detecting erroneous materialized facts, they can determine all semantically correct assertions before materializing them. This avoids invalid property value assertions and provides users an informed, purposeful assistance. To the author's knowledge, PACs are the only such approach.

Satisfiability and containment of recursive SHACL

The Shapes Constraint Language (SHACL) is the recent W3C recommendation language for validating RDF data, by verifying certain shapes on graphs. Previous work has largely focused on the validation problem, while the standard decision problems of satisfiability and containment, crucial for design and optimisation purposes, have only been investigated for simplified versions of SHACL. Moreover, the SHACL specification does not define the semantics of recursively-defined constraints, which led to several alternative recursive semantics being proposed in the literature. The interaction between these different semantics and important decision problems has not been investigated yet. In this article we provide a comprehensive study of the different features of SHACL, by providing a translation to a new first-order language, called SCL, that precisely captures the semantics of SHACL. We also present MSCL, a second-order extension of SCL, which allows us to define, in a single formal logic framework, the main recursive semantics of SHACL. Within this language we also provide an effective treatment of filter constraints which are often neglected in the related literature. Using this logic we provide a detailed map of (un)decidability and complexity results for the satisfiability and containment decision problems for different SHACL fragments. Notably, we prove that both problems are undecidable for the full language, but we present decidable combinations of interesting features, even in the face of recursion.

SHACL-Based Report Quality Evaluation for Health IT-Induced Medication Errors.

Patient safety event (PSE) reports are an important source of information for analyzing risks in healthcare processes. However, the reports' quality is often low due to missing or imprecise information. We work towards an automatic analysis of reports and quality evaluation. To leverage a suitable data representation of health IT-induced medication error reports, we apply the Shapes Constraint Language (SHACL). We define an ontology representing these reports and construct a corresponding SHACL graph. Three authors manually annotate and transform 20 textual reports to the SHACL representation. Furthermore, we use this representation to compute a quality score for each report. The results indicate the suitability of SHACL as a representation of health IT-induced medication error reports, which paves a path of automatically extracting information from PSE reports using text mining and transform them to SHACL for quality evaluation.

Magic shapes for SHACL validation

A key prerequisite for the successful adoption of the Shapes Constraint Language (SHACL)---the W3C standardized constraint language for RDF graphs---is the availability of automated tools that efficiently validate targeted constraints (known as shapes graphs ) over possibly very large RDF graphs. There are already significant efforts to produce optimized engines for SHACL validation, but they focus on restricted fragments of SHACL. For unrestricted SHACL, that is SHACL with unrestricted recursion and negation, there is no validator beyond a proof-of-concept prototype, and existing techniques are inherently incompatible with the goal-driven approaches being pursued by existing validators. Instead they require a global computation on the entire data graph that is not only computationally very costly, but also brittle, and can easily result in validation failures due to conflicts that are irrelevant to the validation targets. To address these challenges, we present a 'magic' transformation---based on Magic Sets as known from Logic Programming---that transforms a SHACL shapes graph S into a new shapes graph S' whose validation considers only the relevant neighbourhood of the targeted nodes. The new S' is equivalent to S whenever there are no conflicts between the constraints and the data, and in case the validation of S fails due to conflicts that are irrelevant to the target, S' may still admit a lazy, target-oriented validation. We implement the algorithm and run preliminary experiments, showing our approach can be a stepping stone towards validators for full SHACL, and that it can significantly improve the performance of the only prototype validator that currently supports full recursion and negation.

Methods and techniques for management of ontolodgy-based knowledge representation models in the context of BIG data

Ontology-based knowledge representation models in the context of big data are one way to reduce complexity for data processing across methods of semantic description. This research paper aims at providing an overview of the methods and techniques for efficient management of the ontology-based models that improve big data systems. For this case, the shapes constraint language (SHACL) for information validation was reviewed as the key method. The knowledge representation systems and reasoners are studied and reviewed in the paper as well. It describes approaches based on ontologies in the context of big data. The proper management of ontology-based knowledge representation models through offered methods and techniques brings improved data integration, big data quality, and business process integration.

A semantic common model for product data in the water industry

The issue of interoperability in the Architecture, Engineering, and Construction (AEC) industry represents a challenge on a scale that spans across the project life cycle. This is predominant in the infrastructure sector that usually comprises a more versatile Operations and Maintenance (O&M) phase in comparison with the buildings sector. To this end, an important stage in the information life cycle is the asset information capture and validation during product procurement at the O&M phase. The water industry in the United Kingdom relies on Product Data Templates (PDTs) to fulfil such task, which is usually an error prone manual process. This paper presents an ongoing research, which investigates the application of Semantic Web Technologies (SWT) for improving product data exchange during product procurement at the O&M phase for the water industry in the United Kingdom (UK). Therefore, focus group sessions with industry experts were held to discuss current inefficiencies and solution requirements. Based on these results, a semantic common model named Asset Specification Ontology (ASO) was developed to capture and validate asset information during product procurement at the O&M phase. The common model (ontology) is based on available technologies, namely Web Ontology Language (OWL) and Shapes Constraint Language (SHACL). This gives the advantage of semantically rich data which can be linked and queried in a meaningful way to facilitate the exchange and validation of water industry assets’ data. The uniqueness of this paper is manifested in the issue it tackles, as efficient product procurement, and hence, data exchange in the water industry is an industrial challenge that is seldom researched. Results from the focus group sessions showed that information exchange within the UK water industry is impeded due to the lack of structured and semantic data. However, for a robust semantic interoperability, there needs to be a robust semantic data infrastructure, which would require semantic mappings from standards to product properties, from standards to other standards, and from standards to dictionaries. These conclusions were further supported by the common model, which was created from existing schemas, standards, and dictionaries. Generally, this paper recommends a common model/product library for phase-specific product data exchange in the water industry.

Automatic Detection of Metadata Errors in a Registry of Clinical Studies Using Shapes Constraint Language (SHACL) Graphs.

Registries of clinical studies such as ClinicalTrials.gov are an important source of information. However, the process of manually entering metadata is prone to errors which impedes their use and thereby the overall usefulness of the registry. In this work, we propose a generic approach towards detection of errors in the metadata by using the Shapes Constraint Language for defining rule templates covering constraints regarding value type and cardinality. We developed a Python 3 algorithm for the automatic validation of 15 rule instances applied to the whole ClinicalTrials.gov database (355,862 studies; 27th October 2020) resulting in more than 5 million metadata verifications. Our results show a large number of errors in different metadata fields, such as i) missing values, ii) values not coming from a predefined set or iii) wrong cardinalities, can be detected using this approach. Since 2015 approximately 5% of all studies contain one or more errors. In the future, we will apply this technique to other registries and develop more complex rules by focusing on the semantics of the metadata. This could render the possibility of automatically correcting entries, increasing the value of registries of clinical studies.

BIM quality control based on requirement linked data

This article discusses a BIM Quality Control Ecosystem that is based on Requirement Linked Data in order to create a framework where automated BIM compliance checking methods can be widely used. The meaning of requirements is analyzed in a building project context as a basis for data flow analysis: what are the main types of requirements, how they are handled, and what sources they originate from. A literature review has been conducted to find the present development directions in quality checking, besides a market research on present, already widely used solutions. With the conclusions of these research and modern data management theory, the principles of a holistic approach have been defined for quality checking in the Architecture, Engineering and Construction (AEC) industry. A comparative analysis has been made on current BIM compliance checking solutions according to our review principles. Based on current practice and ongoing research, a state-of-the-art BIM quality control ecosystem is proposed that is open, enables automation, promotes interoperability, and leaves the data governing responsibility at the sources of the requirements. In order to facilitate the flow of requirement and quality data, we propose a model for requirements as Linked Data and provide example for quality checking using Shapes Constraint Language (SHACL). As a result, an opportunity is given for better quality and cheaper BIM design methods to be implemented in the industry.

Linked-Data based Constraint-Checking (LDCC) to support look-ahead planning in construction

In the construction sector, complex constraints are not usually modeled in conventional scheduling and 4D building information modeling software, as they are highly dynamic and span multiple domains. The lack of embedded constraint relationships in such software means that, as Automated Data Collection (ADC) technologies become used, it cannot automatically deduce the effect of deviations to schedule. This paper presents a novel method, using semantic web technologies, to model and validate complex scheduling constraints. It presents a Linked-Data based Constraint-Checking (LDCC) approach, using the Shapes Constraint Language (SHACL). A prototype web application is developed using this approach and evaluated using an OpenBIM dataset. Results demonstrate the potential of LDCC to check for constraint violation in distributed construction data. This novel method (LDCC) and its first prototype is a contribution that can be extended in future research in linked-data, BIM based rule-checking, lean construction and ADC.

The molecular entities in linked data dataset.

The Molecular Entities in Linked Data (MEiLD) dataset comprises data of distinct atoms, molecules, ions, ion pairs, radicals, radical ions, and others that can be identifiable as separately distinguishable chemical entities. The dataset is provided in a JSON-LD format and was generated by the SDFEater, a tool that allows parsing atoms, bonds, and other molecule data. MEiLD contains 349,960 of ‘small’ chemical entities. Our dataset is based on the SDF files and is enriched with additional ontologies and line notation data. As a basis, the Molecular Entities in Linked Data dataset uses the Resource Description Framework (RDF) data model. Saving the data in such a model allows preserving the semantic relations, like hierarchical and associative, between them. To describe chemical molecules, vocabularies such as Chemical Vocabulary for Molecular Entities (CVME) and Simple Knowledge Organization System (SKOS) are used. The dataset can be beneficial, among others, for people concerned with research and development tools for cheminformatics and bioinformatics. In this paper, we describe various methods of access to our dataset. In addition to the MEiLD dataset, we publish the Shapes Constraint Language (SHACL) schema of our dataset and the CVME ontology. The data is available in Mendeley Data.

From the web of bibliographic data to the web of bibliographic meaning: structuring, interlinking and validating ontologies on the semantic web

Bibliographic data sets have revealed good levels of technical interoperability observing the principles and good practices of linked data. However, they have a low level of quality from the semantic point of view, due to many factors: lack of a common conceptual framework for a diversity of standards often used together, reduced number of links between the ontologies underlying data sets, proliferation of heterogeneous vocabularies, underuse of semantic mechanisms in data structures, ontology hijacking (Feeney et al., 2018), point-to-point mappings, as well as limitations of semantic web languages for the requirements of bibliographic data interoperability. After reviewing such issues, a research direction is proposed to overcome the misalignments found by means of a reference model and a superontology, using Shapes Constraint Language (SHACL) to solve current limitations of RDF languages.

Converting OCL and CGMES Rules to SHACL in Smart Grids

Models are first-class elements in Model-Driven Engineering (MDE). In this paradigm, the most widespread approaches adopted by the development community are Object-Oriented and ontological, formalized using Unified Modeling Language (UML) and Resource Description Framework (RDF), respectively. However, Object Management Group (OMG) does not provide a specific standard language for validating UML models against Object Constraints Language (OCL) constraints; meanwhile, World Wide Web Consortium (W3C) has defined Shapes Constraint Language (SHACL) as a standard validation language. Although the transformation between UML and RDF can be performed at the structural level, no effort has been made to transform OCL to SHACL. This paper addresses the transformation of OCL and text-based constraints to SHACL shapes in the context of Common Grid Model Exchange Standard (CGMES), a UML-based standard for electric utilities in Europe. This paper presents several contributions to the software engineering community. First, solving the validation problem in a standardized way. Second, facilitating European Network of Transmission System Operators for Electricity (ENTSO-E) the construction of an ontology associated with the CGMES standard. Third, allowing developers to integrate the two complementary approaches. Finally, Promoting the adoption and integration of the ontological approach in the software community.