The formulation of constraints and the validation of RDF data against these constraints is a common requirement and a much sought-after feature, particularly as this is taken for granted in the XML world. Recently, RDF validation as a research field gained speed due to shared needs of data practitioners from a variety of domains. For constraint formulation and RDF data validation, several languages exist or are currently developed. Yet, none of the languages is able to meet all requirements raised by data professionals.
We have published a set of constraint types that are required by diverse stakeholders for data applications. We use these constraint types to gain a better understanding of the expressiveness of solutions, investigate the role that reasoning plays in practical data validation, and give directions for the further development of constraint languages.
We introduce a validation framework that enables to consistently execute RDF-based constraint languages on RDF data and to formulate constraints of any type in a way that mappings from high-level constraint languages to an intermediate generic representation can be created straight-forwardly. The framework reduces the representation of constraints to the absolute minimum, is based on formal logics, and consists of a very simple conceptual model with a small lightweight vocabulary. We demonstrate that using another layer on top of SPARQL ensures consistency regarding validation results and enables constraint transformations for each constraint type across RDF-based constraint languages.
RDF Constraint Checking using RDF Data Descriptions (RDD)Alexander Schätzle
Linked Open Data (LOD) sources on the Web are increas-
ingly becoming a mainstream method to publish and con-
sume data. For real-life applications, mechanisms to de-
scribe the structure of the data and to provide guarantees
are needed, as recently emphasized by the W3C in its Data
Shape Working Group. Using such mechanisms, data providers will be able to validate their data, assuring that it is structured in a way expected by data consumers. In turn, data consumers can design and optimize their applications to match the data format to be processed.
In this paper, we present several crucial aspects of RDD,
our language for expressing RDF constraints. We introduce
the formal semantics and describe how RDD constraints can be translated into SPARQL for constraint checking. Based on our fully working validator, we evaluate the feasibility and eciency of this checking process using two popular, state-of-the-art RDF triple stores. The results indicate that even a naive implementation of RDD based on SPARQL 1.0 will incur only a moderate overhead on the RDF loading process, yet some constraint types contribute an outsize share and scale poorly. Incorporating several preliminary optimizations, some of them based on SPARQL 1.1, we provide insights on how to overcome these limitations.
EDF2012 Irini Fundulaki - Abstract Access Control Models for Dynamic RDF Da...European Data Forum
This document presents an abstract access control model for controlling access to dynamic RDF datasets. The model uses abstract tokens and operators to represent how access labels of inferred triples are computed, avoiding the need to recompute labels when updates occur. It describes annotation models that associate access labels with triples, and how RDFS inference rules can be applied to infer new triples and labels. The abstract model is evaluated by mapping abstract expressions to concrete access control policies and operators. Pros include flexibility to experiment with policies and efficiency during updates, while cons include increased storage overhead. Future work involves implementing the approach in a database engine.
This talk was given by FORTH, Greece, at the European Data Forum (EDF) 2012 took place on June 6-7, 2012 in Copenhagen (Denmark) at the Copenhagen Business School (CBS).
Abstract:
Given the increasing amount of sensitive RDF data available on the Web, it becomes increasingly critical to guarantee secure access to this content. Access control is complicated when RDFS inference rules and other dependencies between access permissions of triples need to be considered; this is necessary, e.g., when we want to associate the access permissions of inferred triples with the ones that implied it. In this paper we advocate the use of abstract provenance models that are defined by means of abstract tokens operators to support fine grained access control for RDF graphs. The access label of a triple is a complex expression that encodes how said label was produced (i.e., the triples that contributed to its computation). This feature allows us to know exactly the effects of any possible change, thereby avoiding a complete recomputation of the labels when a change occurs. In addition, the same application can choose to enforce different access control policies or, different applications can enforce different policies on the same data, avoiding the recomputation of the label of a triple. Preliminary experiments have shown the applicability and benefits of our approach.
Semantic Web: From Representations to ApplicationsGuus Schreiber
This document discusses semantic web representations and applications. It provides an overview of the W3C Web Ontology Working Group and Semantic Web Best Practices and Deployment Working Group, including their goals and key issues addressed. Examples of semantic web applications are also described, such as using ontologies to integrate information from heterogeneous cultural heritage sources.
The document discusses the role of reasoning for RDF validation. It finds that:
1. Reasoning can resolve or cause validation violations and reduce redundancy. Around 43% of constraint types benefit from reasoning.
2. Validating with reasoning is more computationally expensive, ranging from PTIME to N2EXPTIME complexity depending on the reasoning type.
3. Around 57% of constraint types are independent of the closed-world assumption, while 67% depend on the unique name assumption when validating. Validation results differ depending on the semantics assumed.
The document provides an overview of the Semantic Web including definitions of key concepts like RDF, RDFS, OWL, and applications. It describes the Semantic Web as extending the current web to give data well-defined meaning enabling computers and people to better cooperate. The layers of the Semantic Web are outlined including XML, RDF, RDFS, OWL, and how each builds on the previous. Examples of RDF graphs and syntax are given. Semantic Web applications like Swoogle, DBpedia, and Flickr are also mentioned.
This document provides an overview of SHACL (Shapes Constraint Language), a W3C recommendation for defining constraints on RDF graphs. It defines key SHACL concepts like shapes, targets, node shapes, property shapes and constraint components. Examples are provided to illustrate shape definitions and how validation of an RDF graph works against the defined shapes. The document summarizes the motivation for SHACL and inputs that influenced its development.
Semantic Web technologies (such as RDF and SPARQL) excel at bringing together diverse data in a world of independent data publishers and consumers. Common ontologies help to arrive at a shared understanding of the intended meaning of data.
However, they don’t address one critically important issue: What does it mean for data to be complete and/or valid? Semantic knowledge graphs without a shared notion of completeness and validity quickly turn into a Big Ball of Data Mud.
The Shapes Constraint Language (SHACL), an upcoming W3C standard, promises to help solve this problem. By keeping semantics separate from validity, SHACL makes it possible to resolve a slew of data quality and data exchange issues.
Presented at the Lotico Berlin Semantic Web Meetup.
RDF Constraint Checking using RDF Data Descriptions (RDD)Alexander Schätzle
Linked Open Data (LOD) sources on the Web are increas-
ingly becoming a mainstream method to publish and con-
sume data. For real-life applications, mechanisms to de-
scribe the structure of the data and to provide guarantees
are needed, as recently emphasized by the W3C in its Data
Shape Working Group. Using such mechanisms, data providers will be able to validate their data, assuring that it is structured in a way expected by data consumers. In turn, data consumers can design and optimize their applications to match the data format to be processed.
In this paper, we present several crucial aspects of RDD,
our language for expressing RDF constraints. We introduce
the formal semantics and describe how RDD constraints can be translated into SPARQL for constraint checking. Based on our fully working validator, we evaluate the feasibility and eciency of this checking process using two popular, state-of-the-art RDF triple stores. The results indicate that even a naive implementation of RDD based on SPARQL 1.0 will incur only a moderate overhead on the RDF loading process, yet some constraint types contribute an outsize share and scale poorly. Incorporating several preliminary optimizations, some of them based on SPARQL 1.1, we provide insights on how to overcome these limitations.
EDF2012 Irini Fundulaki - Abstract Access Control Models for Dynamic RDF Da...European Data Forum
This document presents an abstract access control model for controlling access to dynamic RDF datasets. The model uses abstract tokens and operators to represent how access labels of inferred triples are computed, avoiding the need to recompute labels when updates occur. It describes annotation models that associate access labels with triples, and how RDFS inference rules can be applied to infer new triples and labels. The abstract model is evaluated by mapping abstract expressions to concrete access control policies and operators. Pros include flexibility to experiment with policies and efficiency during updates, while cons include increased storage overhead. Future work involves implementing the approach in a database engine.
This talk was given by FORTH, Greece, at the European Data Forum (EDF) 2012 took place on June 6-7, 2012 in Copenhagen (Denmark) at the Copenhagen Business School (CBS).
Abstract:
Given the increasing amount of sensitive RDF data available on the Web, it becomes increasingly critical to guarantee secure access to this content. Access control is complicated when RDFS inference rules and other dependencies between access permissions of triples need to be considered; this is necessary, e.g., when we want to associate the access permissions of inferred triples with the ones that implied it. In this paper we advocate the use of abstract provenance models that are defined by means of abstract tokens operators to support fine grained access control for RDF graphs. The access label of a triple is a complex expression that encodes how said label was produced (i.e., the triples that contributed to its computation). This feature allows us to know exactly the effects of any possible change, thereby avoiding a complete recomputation of the labels when a change occurs. In addition, the same application can choose to enforce different access control policies or, different applications can enforce different policies on the same data, avoiding the recomputation of the label of a triple. Preliminary experiments have shown the applicability and benefits of our approach.
Semantic Web: From Representations to ApplicationsGuus Schreiber
This document discusses semantic web representations and applications. It provides an overview of the W3C Web Ontology Working Group and Semantic Web Best Practices and Deployment Working Group, including their goals and key issues addressed. Examples of semantic web applications are also described, such as using ontologies to integrate information from heterogeneous cultural heritage sources.
The document discusses the role of reasoning for RDF validation. It finds that:
1. Reasoning can resolve or cause validation violations and reduce redundancy. Around 43% of constraint types benefit from reasoning.
2. Validating with reasoning is more computationally expensive, ranging from PTIME to N2EXPTIME complexity depending on the reasoning type.
3. Around 57% of constraint types are independent of the closed-world assumption, while 67% depend on the unique name assumption when validating. Validation results differ depending on the semantics assumed.
The document provides an overview of the Semantic Web including definitions of key concepts like RDF, RDFS, OWL, and applications. It describes the Semantic Web as extending the current web to give data well-defined meaning enabling computers and people to better cooperate. The layers of the Semantic Web are outlined including XML, RDF, RDFS, OWL, and how each builds on the previous. Examples of RDF graphs and syntax are given. Semantic Web applications like Swoogle, DBpedia, and Flickr are also mentioned.
This document provides an overview of SHACL (Shapes Constraint Language), a W3C recommendation for defining constraints on RDF graphs. It defines key SHACL concepts like shapes, targets, node shapes, property shapes and constraint components. Examples are provided to illustrate shape definitions and how validation of an RDF graph works against the defined shapes. The document summarizes the motivation for SHACL and inputs that influenced its development.
Semantic Web technologies (such as RDF and SPARQL) excel at bringing together diverse data in a world of independent data publishers and consumers. Common ontologies help to arrive at a shared understanding of the intended meaning of data.
However, they don’t address one critically important issue: What does it mean for data to be complete and/or valid? Semantic knowledge graphs without a shared notion of completeness and validity quickly turn into a Big Ball of Data Mud.
The Shapes Constraint Language (SHACL), an upcoming W3C standard, promises to help solve this problem. By keeping semantics separate from validity, SHACL makes it possible to resolve a slew of data quality and data exchange issues.
Presented at the Lotico Berlin Semantic Web Meetup.
A hands on overview of the semantic webMarakana Inc.
This document provides an overview of the Semantic Web. It defines the Semantic Web as linking data to data using technologies like RDF, RDFS, OWL and SPARQL. It explains that RDF represents information as subject-predicate-object statements that can be queried using SPARQL. RDFS allows defining schemas and classes for RDF data, while OWL adds more expressiveness for defining complex ontologies. The document outlines popular Semantic Web tools, public ontologies, and companies working in this domain. It positions the Semantic Web as a way to represent and share data universally on the web.
The document discusses the W3C stack for representing metadata, with XML providing syntax but no semantics, RDF and RDF Schema defining a data model for relations between resources and a vocabulary definition language, and OWL adding more expressivity with concepts such as classes, properties, and cardinality restrictions. It also covers RDF syntaxes like Turtle and XML, and how RDF can represent implied claims from XML and facilitate interoperability between systems through its abstract model.
The document discusses the need for level-agnostic modeling languages and tools that can work across different levels of models, types, and meta-models. It proposes an approach where everything is modeled as an object, with types defined as constraints within models. It presents an example modeling language implemented using this approach and shows how a constraint checking tool could work uniformly on objects, types, and meta-types. The authors claim this approach provides a level-agnostic modeling language and tools.
The document summarizes the key changes and additions in the RDF 1.1 specification, including:
1) Support for named graphs, also known as RDF datasets or quads, to represent multiple RDF graphs each with a unique name;
2) Additional datatypes like durations and date/time stamps;
3) JSON-LD as a new syntax for serializing RDF in JSON; and
4) Some controversial proposals like deprecating features and allowing literals as subjects that did not make the final specification.
SPIN is a vocabulary that represents SPARQL queries and constraints as RDF triples. This allows SPARQL queries to be stored and shared on the semantic web. SPIN can be used to define SPARQL constraints, rules, functions and reusable query templates. Storing SPARQL queries as RDF triples provides benefits like referential integrity, managing namespaces centrally, and facilitating the easy sharing of queries on the semantic web.
This document summarizes a draft specification for the SHACL (Shapes Constraint Language) language. It provides an overview of the key components and design principles of SHACL, including examples of how SHACL can be used to define shapes and constraints for validating RDF data. The draft aims to define a simple yet fully extensible language for validating RDF graphs in a consistent and future-proof manner.
This presentation was given at the Balisage 2017 conference, and provides an overview of three key RDF standards for constraint modeling, annotation and the use of data frames and cubes in RDF.
2016.02 - Validating RDF Data Quality using Constraints to Direct the Develop...Dr.-Ing. Thomas Hartmann
For research institutes, data libraries, and data
archives, RDF data validation according to predefined constraints
is a much sought-after feature, particularly as this is taken
for granted in the XML world. Based on our work in the
DCMI RDF Application Profiles Task Group and in cooperation
with the W3C Data Shapes Working Group, we identified and
published by today 81 types of constraints that are required
by various stakeholders for data applications. In this paper,
in collaboration with several domain experts we formulate 115
constraints on three different vocabularies (DDI-RDF, QB, and
SKOS) and classify them according to (1) the severity of an
occurring violation and (2) the complexity of the constraint
expression in common constraint languages. We evaluate the
data quality of 15,694 data sets (4.26 billion triples) of research
data for the social, behavioral, and economic sciences obtained
from 33 SPARQL endpoints. Based on the results, we formulate
several findings to direct the further development of constraint
languages.
The document describes the SHACL Test-Suite, which provides a framework for testing SHACL shape schemas and validators. The test-suite structure includes a main manifest file that includes other test folders, each with their own manifest. Manifest files follow the W3C standard practice and describe test entries that validate data against schemas, match nodes to shapes, and test schema formatting. The test-suite is available online and on GitHub, and the working group is seeking contributions to expand the included tests.
The document provides an overview of the work done at DERI Galway, including developing technologies like SIOC, ActiveRDF, and BrowseRDF to interconnect online communities and enable semantic applications. It also describes JeromeDL, a digital library system that uses semantic metadata and services to allow users to collaboratively browse and share knowledge.
Understanding RDF: the Resource Description Framework in Context (1999)Dan Brickley
Dan Brickley, 3rd European Commission Metadata Workshop, Luxemburg, April 12th 1999
Understanding RDF: the Resource Description Framework in Context
http://ilrt.org/discovery/2001/01/understanding-rdf/
Rdf And Rdf Schema For Ontology Specificationchenjennan
The document discusses RDF (Resource Description Framework) and RDF Schema for ontology specification on the Semantic Web. It provides an introduction to RDF and how it uses URIs to identify resources and assertions. It then discusses RDF applications for mobile terminals, RDF graph models, RDF/XML syntax, RDF vocabularies and schemas, and the RDF Schema language. It concludes with an overview of how OWL (Web Ontology Language) and OWL-S (Web Service Ontology) build upon RDF Schema to facilitate ontology specification and automation of web services.
1) The Semantic Web technologies OWL 2 and Rule Interchange Format (RIF) have recently been finalized, while technical work is ongoing for SPARQL 1.1, RDFa 1.1, and connecting relational databases to RDF.
2) A workshop will discuss a possible revision to RDF to address issues like deprecation of features and addition of new constructs like named graphs.
3) The standards organization W3C is working on finalizing current technologies while exploring new areas like provenance and revisions to the core RDF standard based on discussion at the workshop.
The OWL Web Ontology Language enables software engineers to define ontologies of domain knowledge which can be queried and reasoned over by software agents. OWL facilitates greater machine interpretability of content than that supported by XML, RDF, and RDF Schema by providing additional vocabulary along with formal semantics.
The document discusses the Web Ontology Language (OWL). It provides an overview of OWL, describing its three sublanguages - OWL Lite, OWL DL, and OWL Full - and their increasing expressiveness and reasoning complexity. The document also reviews the requirements for ontology languages and how OWL builds upon XML, RDF, and RDF Schema as the ontology language for the Semantic Web.
The document provides an overview of the Resource Description Framework (RDF). It describes RDF as a standard for describing web resources using metadata. RDF uses a simple data model based on making statements about resources in the form of subject-predicate-object expressions. This allows data to be shared across different applications. The document discusses key RDF concepts including resources, properties, and statements. It provides examples of RDF statements and illustrates the RDF triple format. The goal of RDF is to enable the encoding, exchange, and reuse of structured metadata about Web resources between applications.
Linked Data Quality assessment applied and integrated to the Linked Data generation and publication workflow. Presented at the Data Quality tutorial, satellite event at SEMANTICS2016.
Lecture Notes by Mustafa Jarrar at Birzeit University, Palestine.
See the course webpage at: http://jarrar-courses.blogspot.com/2014/01/owl-web-ontology-language.html
and http://www.jarrar.info
you may also watch this lecture at: http://www.youtube.com/watch?v=5Kr4JzqDO_w
The lecture covers:
- Introduction to OWL
- OWL Basics
- Class Expression Axioms
- Property Axioms
- Assertions
- Class Expressions -Propositional Connectives and Enumeration of Individuals
- Class Expressions? -Property Restrictions
- Class Expressions? -Cardinality Restrictions
The document discusses the need for semantic technologies like ontologies to help address information overload by allowing machines to extract knowledge. It describes the evolution of semantic technologies, starting with XML providing syntactic interoperability, RDF providing a semantic grammar through assertions and relationships, and RDFS providing semantic interoperability through hierarchies and taxonomies for defining vocabulary. However, RDFS is not expressive enough to model all ontologies, so OWL was created by W3C to further extend RDFS while addressing complexity through different profiles like OWL Lite, DL, and Full.
ShEx is a language for validating RDF data. It allows defining shapes that specify constraints on nodes and triples. ShEx expressions can be used to validate if RDF graphs conform to the defined shapes. The ShEx language is inspired by languages like RelaxNG and provides different serialization formats like ShExC, ShExJ, and ShExR. There are open-source implementations of ShEx validators in languages like JavaScript, Scala, Ruby, Python, and Java. ShEx provides a concise way to define RDF shapes and validate instance data against those shapes.
This document discusses the software architecture for MISSY, a software tool for managing study documentation. It outlines several key requirements for developers, including reusability, stability, extensibility, and use of modern technologies. The proposed architecture follows a multitier model-view-controller pattern. It separates the data model from persistence strategies and business logic. The data model is based on the DDI-RDF Discovery Vocabulary (DISCO) but allows customization. Persistence can be implemented through different strategies like relational databases or XML. The architecture is designed to be abstract and modular to support flexibility and reuse across projects.
A hands on overview of the semantic webMarakana Inc.
This document provides an overview of the Semantic Web. It defines the Semantic Web as linking data to data using technologies like RDF, RDFS, OWL and SPARQL. It explains that RDF represents information as subject-predicate-object statements that can be queried using SPARQL. RDFS allows defining schemas and classes for RDF data, while OWL adds more expressiveness for defining complex ontologies. The document outlines popular Semantic Web tools, public ontologies, and companies working in this domain. It positions the Semantic Web as a way to represent and share data universally on the web.
The document discusses the W3C stack for representing metadata, with XML providing syntax but no semantics, RDF and RDF Schema defining a data model for relations between resources and a vocabulary definition language, and OWL adding more expressivity with concepts such as classes, properties, and cardinality restrictions. It also covers RDF syntaxes like Turtle and XML, and how RDF can represent implied claims from XML and facilitate interoperability between systems through its abstract model.
The document discusses the need for level-agnostic modeling languages and tools that can work across different levels of models, types, and meta-models. It proposes an approach where everything is modeled as an object, with types defined as constraints within models. It presents an example modeling language implemented using this approach and shows how a constraint checking tool could work uniformly on objects, types, and meta-types. The authors claim this approach provides a level-agnostic modeling language and tools.
The document summarizes the key changes and additions in the RDF 1.1 specification, including:
1) Support for named graphs, also known as RDF datasets or quads, to represent multiple RDF graphs each with a unique name;
2) Additional datatypes like durations and date/time stamps;
3) JSON-LD as a new syntax for serializing RDF in JSON; and
4) Some controversial proposals like deprecating features and allowing literals as subjects that did not make the final specification.
SPIN is a vocabulary that represents SPARQL queries and constraints as RDF triples. This allows SPARQL queries to be stored and shared on the semantic web. SPIN can be used to define SPARQL constraints, rules, functions and reusable query templates. Storing SPARQL queries as RDF triples provides benefits like referential integrity, managing namespaces centrally, and facilitating the easy sharing of queries on the semantic web.
This document summarizes a draft specification for the SHACL (Shapes Constraint Language) language. It provides an overview of the key components and design principles of SHACL, including examples of how SHACL can be used to define shapes and constraints for validating RDF data. The draft aims to define a simple yet fully extensible language for validating RDF graphs in a consistent and future-proof manner.
This presentation was given at the Balisage 2017 conference, and provides an overview of three key RDF standards for constraint modeling, annotation and the use of data frames and cubes in RDF.
2016.02 - Validating RDF Data Quality using Constraints to Direct the Develop...Dr.-Ing. Thomas Hartmann
For research institutes, data libraries, and data
archives, RDF data validation according to predefined constraints
is a much sought-after feature, particularly as this is taken
for granted in the XML world. Based on our work in the
DCMI RDF Application Profiles Task Group and in cooperation
with the W3C Data Shapes Working Group, we identified and
published by today 81 types of constraints that are required
by various stakeholders for data applications. In this paper,
in collaboration with several domain experts we formulate 115
constraints on three different vocabularies (DDI-RDF, QB, and
SKOS) and classify them according to (1) the severity of an
occurring violation and (2) the complexity of the constraint
expression in common constraint languages. We evaluate the
data quality of 15,694 data sets (4.26 billion triples) of research
data for the social, behavioral, and economic sciences obtained
from 33 SPARQL endpoints. Based on the results, we formulate
several findings to direct the further development of constraint
languages.
The document describes the SHACL Test-Suite, which provides a framework for testing SHACL shape schemas and validators. The test-suite structure includes a main manifest file that includes other test folders, each with their own manifest. Manifest files follow the W3C standard practice and describe test entries that validate data against schemas, match nodes to shapes, and test schema formatting. The test-suite is available online and on GitHub, and the working group is seeking contributions to expand the included tests.
The document provides an overview of the work done at DERI Galway, including developing technologies like SIOC, ActiveRDF, and BrowseRDF to interconnect online communities and enable semantic applications. It also describes JeromeDL, a digital library system that uses semantic metadata and services to allow users to collaboratively browse and share knowledge.
Understanding RDF: the Resource Description Framework in Context (1999)Dan Brickley
Dan Brickley, 3rd European Commission Metadata Workshop, Luxemburg, April 12th 1999
Understanding RDF: the Resource Description Framework in Context
http://ilrt.org/discovery/2001/01/understanding-rdf/
Rdf And Rdf Schema For Ontology Specificationchenjennan
The document discusses RDF (Resource Description Framework) and RDF Schema for ontology specification on the Semantic Web. It provides an introduction to RDF and how it uses URIs to identify resources and assertions. It then discusses RDF applications for mobile terminals, RDF graph models, RDF/XML syntax, RDF vocabularies and schemas, and the RDF Schema language. It concludes with an overview of how OWL (Web Ontology Language) and OWL-S (Web Service Ontology) build upon RDF Schema to facilitate ontology specification and automation of web services.
1) The Semantic Web technologies OWL 2 and Rule Interchange Format (RIF) have recently been finalized, while technical work is ongoing for SPARQL 1.1, RDFa 1.1, and connecting relational databases to RDF.
2) A workshop will discuss a possible revision to RDF to address issues like deprecation of features and addition of new constructs like named graphs.
3) The standards organization W3C is working on finalizing current technologies while exploring new areas like provenance and revisions to the core RDF standard based on discussion at the workshop.
The OWL Web Ontology Language enables software engineers to define ontologies of domain knowledge which can be queried and reasoned over by software agents. OWL facilitates greater machine interpretability of content than that supported by XML, RDF, and RDF Schema by providing additional vocabulary along with formal semantics.
The document discusses the Web Ontology Language (OWL). It provides an overview of OWL, describing its three sublanguages - OWL Lite, OWL DL, and OWL Full - and their increasing expressiveness and reasoning complexity. The document also reviews the requirements for ontology languages and how OWL builds upon XML, RDF, and RDF Schema as the ontology language for the Semantic Web.
The document provides an overview of the Resource Description Framework (RDF). It describes RDF as a standard for describing web resources using metadata. RDF uses a simple data model based on making statements about resources in the form of subject-predicate-object expressions. This allows data to be shared across different applications. The document discusses key RDF concepts including resources, properties, and statements. It provides examples of RDF statements and illustrates the RDF triple format. The goal of RDF is to enable the encoding, exchange, and reuse of structured metadata about Web resources between applications.
Linked Data Quality assessment applied and integrated to the Linked Data generation and publication workflow. Presented at the Data Quality tutorial, satellite event at SEMANTICS2016.
Lecture Notes by Mustafa Jarrar at Birzeit University, Palestine.
See the course webpage at: http://jarrar-courses.blogspot.com/2014/01/owl-web-ontology-language.html
and http://www.jarrar.info
you may also watch this lecture at: http://www.youtube.com/watch?v=5Kr4JzqDO_w
The lecture covers:
- Introduction to OWL
- OWL Basics
- Class Expression Axioms
- Property Axioms
- Assertions
- Class Expressions -Propositional Connectives and Enumeration of Individuals
- Class Expressions? -Property Restrictions
- Class Expressions? -Cardinality Restrictions
The document discusses the need for semantic technologies like ontologies to help address information overload by allowing machines to extract knowledge. It describes the evolution of semantic technologies, starting with XML providing syntactic interoperability, RDF providing a semantic grammar through assertions and relationships, and RDFS providing semantic interoperability through hierarchies and taxonomies for defining vocabulary. However, RDFS is not expressive enough to model all ontologies, so OWL was created by W3C to further extend RDFS while addressing complexity through different profiles like OWL Lite, DL, and Full.
ShEx is a language for validating RDF data. It allows defining shapes that specify constraints on nodes and triples. ShEx expressions can be used to validate if RDF graphs conform to the defined shapes. The ShEx language is inspired by languages like RelaxNG and provides different serialization formats like ShExC, ShExJ, and ShExR. There are open-source implementations of ShEx validators in languages like JavaScript, Scala, Ruby, Python, and Java. ShEx provides a concise way to define RDF shapes and validate instance data against those shapes.
This document discusses the software architecture for MISSY, a software tool for managing study documentation. It outlines several key requirements for developers, including reusability, stability, extensibility, and use of modern technologies. The proposed architecture follows a multitier model-view-controller pattern. It separates the data model from persistence strategies and business logic. The data model is based on the DDI-RDF Discovery Vocabulary (DISCO) but allows customization. Persistence can be implemented through different strategies like relational databases or XML. The architecture is designed to be abstract and modular to support flexibility and reuse across projects.
Doctoral Examination at the Karlsruhe Institute of Technology (08.07.2016)Dr.-Ing. Thomas Hartmann
In this thesis, a validation framework is introduced that enables to consistently execute RDF-based constraint languages on RDF data and to formulate constraints of any type. The framework reduces the representation of constraints to the absolute minimum, is based on formal logics, consists of a small lightweight vocabulary, and ensures consistency regarding validation results and enables constraint transformations for each constraint type across RDF-based constraint languages.
This document introduces the DDI-RDF Discovery Vocabulary, which is a metadata vocabulary for documenting research and survey data as linked data on the web. It provides a conceptual model and overview of the vocabulary, which was developed by mapping concepts from the established DDI standard for social science data documentation to RDF. The vocabulary aims to improve discovery, publishing and linking of microdata by representing DDI metadata as linked data. It was developed by an international community of statistics and linked data experts over multiple workshops.
A Hands On Overview Of The Semantic WebShamod Lacoul
The document provides an overview of the Semantic Web and introduces key concepts such as RDF, RDFS, SPARQL, OWL, and Linked Open Data. It begins with defining what the Semantic Web is, why it is useful, and how it differs from the traditional web by linking data rather than documents. It then covers RDF for representing data, RDFS for defining schemas, and SPARQL for querying RDF data. The document also discusses OWL for building ontologies and Linked Open Data initiatives that have published billions of RDF triples on the web.
This document summarizes and compares four prominent RDF query languages: RDQL, SPARQL, SeRQL, and XsRQL. It evaluates each language based on seven key features: support for data types, path expressions, closure, semantics, optional values, aggregate functions, and advanced set operations. The document finds that while no language is complete, SPARQL shows the most potential as the future W3C standard due to its iterative development process incorporating feedback from the W3C working group. RDQL provides basic functionality but was not intended for complex queries. SeRQL has strong open source support. XsRQL extends existing XML query approaches.
This document provides an overview of the Web Ontology Language (OWL). It discusses OWL's purpose in extending RDF Schema to provide a full knowledge representation language for the web. It outlines OWL's key features such as logical expressions, cardinality constraints, enumerated classes, and property characteristics. It also describes OWL's three sublanguages - OWL Lite, OWL DL, and OWL Full - which differ in their expressiveness and computational guarantees. The document concludes by discussing the Rule Interchange Format (RIF) and its role in defining rule languages for the semantic web.
The document discusses Resource Description and Access (RDA), a new cataloging standard that aims to improve findability, identification, and interoperability of library resources. RDA is based on FRBR (Functional Requirements for Bibliographic Records) and FRAD (Functional Requirements for Authority Data) models. It defines cataloging entities and relationships using Semantic Web technologies like URIs, RDF, and SKOS to make metadata more reusable and linkable on the global scale. The document outlines how RDA entities, elements, and vocabularies are being registered in the NSDL Metadata Registry to enable their representation and sharing using Semantic Web formats.
The document provides an introduction to RDF (Resource Description Framework). It discusses that RDF is a framework for describing resources using statements with a subject, predicate, and object. RDF identifies resources with URIs and describes resources and their properties and property values. An example RDF document is provided that describes CDs with properties like artist, country, and price.
The document discusses Resource Description Framework (RDF), a W3C standard for describing web resources. RDF uses a graph-based data model consisting of subjects, predicates, and objects, known as triples. It provides a common framework for describing resources, along with their properties and relationships. RDF Schema builds upon RDF by defining additional vocabulary terms like class, subClassOf, and domain to organize RDF vocabularies and semantically relate terms. While useful, RDF Schema has limitations, leading to the development of OWL as a more expressive ontology language.
Presentation at the ESWC 2011 PhD Symposium in May 2011, by Michael Schneider, FZI. Included are backup slides that have not been presented at the event. The corresponding PhD proposal can be found in the ESWC proceedings at <http: />. Alternatively, the PhD proposal can be downloaded from <http: />.
The document defines key terms related to semantic technologies and the semantic web including:
- Linked Open Data (LOD) which publishes open data according to semantic web standards and links it to other sources to create a web of data.
- LOD2, an EU project developing infrastructure for building LOD.
- OWL, a language for more expressive semantic modeling.
- R2RML, a standard for mapping data in relational databases to RDF.
- RDF, the standard data model using triples to represent information.
Re-using Media on the Web: Media fragment re-mixing and playoutMediaMixerCommunity
A number of novel application ideas will be introduced based on the media fragment creation, specification and rights management technologies. Semantic search and retrieval allows us to organize sets of fragments by topical or conceptual relevance. These fragment sets can then be played out in a non-linear fashion to create a new media re-mix. We look at a server-client implementation supporting Media Fragments, before allowing the participants to take the sets of media they have selected and create their own re-mix.
This document discusses semantic technologies and digital data processing. It provides an overview of semantics and the semantic web, including XML, RDF, OWL, SPARQL, ontologies, and data models. It also discusses capturing semantics in XML documents, OWL, RDF schema, semantic web applications like cartographic searching, SKOS for knowledge organization systems, and the SKOS Play visualization tool.
The document provides an overview of the semantic web including:
1. It describes the key technologies that power the semantic web such as RDF, RDFS, OWL, and SPARQL which allow data to be shared and reused across applications.
2. It discusses semantic web themes like linked data, vocabularies, and inference which enable data from multiple sources to be integrated and new insights to be discovered.
3. It outlines current and future applications of the semantic web such as in e-commerce, online advertising, and government where semantic technologies can enhance search, personalization and data sharing.
Presentation done* at the 13th International Semantic Web Conference (ISWC) in which we approach a compressed format to represent RDF Data Streams. See the original article at: http://dataweb.infor.uva.es/wp-content/uploads/2014/07/iswc14.pdf
* Presented by Alejandro Llaves (http://www.slideshare.net/allaves)
This document provides an outline for a WWW 2012 tutorial on schema mapping with SPARQL 1.1. The outline includes sections on why data integration is important, schema mapping, translating RDF data with SPARQL 1.1, and common mapping patterns. Mapping patterns discussed include simple renaming, structural patterns like renaming based on property existence or value, value transformation using SPARQL functions, and aggregation. The tutorial aims to show how SPARQL 1.1 can be used to express executable mappings between different data schemas and representations.
The document discusses faceted search over ontology-enhanced RDF data. It formalizes faceted interfaces for querying RDF graphs that capture ontological information. It studies the expressivity and complexity of queries represented by faceted interfaces, and algorithms for generating and updating interfaces based on the underlying RDF and ontology information. The goal is to provide rigorous theoretical foundations for faceted search in the context of RDF and OWL 2 ontologies.
Abstract:
An increasing number of applications rely on RDF, OWL 2, and SPARQL for storing and querying data. SPARQL, however, is not targeted towards end-users, and suitable query interfaces are needed. Faceted search is a prominent approach for end-user data access, and several RDF-based faceted search systems have been developed. There is, however, a lack of rigorous theoretical underpinning for faceted search in the context of RDF and OWL 2. In this paper, we provide such solid foundations. We formalise faceted interfaces for this context, identify a fragment of first-order logic capturing the underlying queries, and study the complexity of answering such queries for RDF and OWL 2 profiles. We then study interface generation and update, and devise efficiently implementable algorithms. Finally, we have implemented and tested our faceted search algorithms for scalability, with encouraging results.
OWL 2 adds several new features to OWL including:
1) Cleaner language design with axiom-centered structural specification and functional style syntax.
2) Increased expressiveness through properties such as property chains, qualified cardinality restrictions, and datatype restrictions on properties.
3) Enhanced datatypes including new datatypes, datatype definitions, and data range combinations.
3) Profiles such as OWL 2 EL, QL, and RL that provide different tradeoffs between expressiveness and reasoning complexity.
A non-technical explanation of the main ideas and notions in OWL.This talk was also recorded on video, and is available on-line at http://videolectures.net/koml04_harmelen_o/
a system called natural language interface which transforms user's natural language question into SPARQL query
find related papers here https://sites.google.com/site/fadhlinams81/publication
Similar to KIT Graduiertenkolloquium 11.05.2016 (20)
Recently, RDF validation as a research field gained speed due to common needs of data practitioners. A typical example is the library domain that co-developed and adopted Linked Data principles very early. Although, there are multiple constraint languages (having different syntaxes and semantics) which can be used to express RDF constraints such as cardinality restrictions, there is no constraint language which can be seen as the standard. The five most promising ones on being the standard are Description Set Profiles (DSP), Resource Shapes (ReSh), Shape Expressions (ShEx), the SPARQL Inferencing Notation (SPIN), and the Web Ontology Language (OWL 2). SPARQL is generally seen as the method of choice to validate RDF data according to certain constraints. We use SPIN, a SPARQL-based way to formulate and check constraints, as basis to define a validation environment (available at http://purl.org/net/rdfval-demo) to validate RDF data according to constraints expressed by arbitrary constraint languages. Additionally, the RDF Validator can be used to validate RDF data to ensure correct syntax and intended semantics of vocabularies such as Disco, Data Cube, DCAT, and SKOS. We present how to express typical RDF constraints by multiple constraint languages and how to actually validate RDF data conforming to these constraints using the RDF Validator. The workshop participants are encouraged to use the RDF Validator during this session (only an internet browser is needed) in order to express RDF constraints they need for their individual purposes.
- Existing controlled vocabularies (CVs) from DDI are available in RDF format, represented using SKOS, with each CV as a skos:ConceptScheme and entries as skos:Concepts. Versioning is supported.
- Mappings are defined between DDI and other vocabularies like PROV-O, Data Cube and DCAT to represent relationships between statistical data, studies, datasets and catalogs.
- Work is ongoing to map DDI-XML to the Disco ontology to represent statistical metadata in RDF, with the aim of enabling the publication and linking of statistical data as Linked Data on the web.
This document provides an overview of the Disco data discovery specification. It summarizes key aspects like studies, variables, datasets, files, metadata, and statistics. Disco allows searching for metadata and data according to criteria like topic, location, and time. Summary statistics and category statistics are presented for variables.
The document discusses various ways to formulate and validate constraints on RDF data, including using OWL 2, SHACL, SPIN, and SPARQL. It provides examples of constraints for class-specific property disjointness, data type ranges for literal values, pattern matching on literals, required properties, transitive properties, and more. Each example includes an RDF constraint, sample valid and invalid data, and a link to an online validator for testing.
2014.10 - Requirements on RDF Constraint Formulation and Validation (DC 2014)Dr.-Ing. Thomas Hartmann
This document discusses RDF validation requirements and describes several RDF validation techniques including constraint languages like DSP and OWL2, validators, and ways to contribute to an RDF validation database. It provides examples of constraints for required properties and disjoint property groups for a specific class, along with matching and non-matching RDF data.
The New Microdata Information System (MISSY) - Integration of DDI-based Data ...Dr.-Ing. Thomas Hartmann
The New Microdata Information System (MISSY) - Integration of DDI-based Data Models, an Open-Source Software Architecture, and Independent Persistence Service Implementations
Use Cases and Vocabularies Related to the DDI-RDF Discovery Vocabulary (EDDI ...Dr.-Ing. Thomas Hartmann
The document discusses using DDI (Data Documentation Initiative) as linked data to enable discovery of metadata and data across different sources. It presents an RDF vocabulary to represent DDI concepts like studies, instruments, variables, analysis units, and datasets. This vocabulary aims to allow users to search for microdata, aggregated data, and associated datasets based on specific metadata, understand how aggregated data is derived from microdata, and discover statistics about variables and categories. The vocabulary is intended to facilitate finding data created by particular research institutes.
Towards the Discovery of Person-Level Data (SemStats, ISWC 2013) [2013.10]Dr.-Ing. Thomas Hartmann
This document summarizes an international workshop on using statistical metadata as linked open data to facilitate the discovery of person-level and aggregated data. It presents an overview of a proposed model for publishing study, instrument, variable and dataset information as linked data using DDI (Data Documentation Initiative) and other vocabularies. The model would support use cases like searching for specific microdata or aggregated data, determining the datasets associated with search results, and tracing the derivation of aggregate data from source microdata. The workshop brought together experts from statistics and linked data to develop recommendations on representing statistical metadata in a machine-readable format.
The document discusses sharing DDI-related software modules between projects. It describes the Microdata Information System (MISSY) which documents studies on a variable level, including several German and EU studies. It proposes reusable modules based on a multitier architecture and the DDI Discovery Vocabulary data model. Modules would follow patterns like MVC and be shared in a GitHub repository.
This document summarizes a presentation on developing a next generation version of MISSY, a software system for documenting metadata about microdata surveys in Germany. Key points include:
- The current MISSY system will be expanded to include additional surveys and implement a modern web architecture using MVC pattern and Apache Maven.
- A DDI-RDF Discovery Vocabulary is being developed to publish microdata and metadata as linked open data to increase visibility, reuse and harmonization of microdata.
- The next generation MISSY will have a modular architecture separating presentation, business logic, and data access layers to improve maintainability and flexibility. Data models will be implemented following the DDI standards.
The document provides an introduction to the Microdata Information System (MISSY) and Data Documentation Initiative (DDI) metadata standard with a technical perspective. It outlines MISSY and key DDI structures like DDIInstance, StudyUnit, ConceptualComponent, LogicalProduct and DataCollection. It describes use cases in MISSY like viewing variable details and comparisons between variables. Finally, it discusses DDI concepts like ResourcePackage, Group and Comparison in more detail.
The document discusses sharing DDI-related software modules between projects by developing a backend architecture with reusable modules. It proposes using the DDI Discovery Vocabulary and MISSY data models as a basis, developing the modules following a multitier architecture using patterns like MVC and DAO, and hosting the code on a GitHub repository. This would allow documentation of microdata studies to be shared while allowing customization through project-specific models.
1. Missy is a microdata information system that provides detailed information about household survey datasets in Europe, including statistics on employment, income, and other demographic information.
2. The next generation of Missy, called Missy 3, will integrate additional surveys and implement a web-based editor application for documentation.
3. Requirements for developers include a complex data model to represent use cases, a flexible and service-oriented architecture, and reusable frameworks.
The document discusses using a model-view-controller (MVC) architecture to manage data modeling projects. It describes using an abstract data module based on the DDI ontology with concrete modules for each project that inherit and extend the abstract module. A RESTful interface is proposed to access resources identified by URIs. The abstract data model is implemented as domain classes with attributes and relations according to MVC and can generate views, storage models, and abstract persistence APIs. Extending the DDI ontology allows projects to add custom fields while maintaining compatibility. Sharing source code and data modules between projects via version control is described.
The document proposes a novel approach to speed up the time-consuming process of designing domain ontologies by reusing information from existing XML schemas (XSDs). The approach uses SWRL rules to automatically derive domain ontologies from XSDs on the terminological and assertional knowledge level, transforming the syntactic structure and vocabulary of XSDs into the more expressive OWL format. The novelty and effectiveness of the proposed approach will be evaluated by comparing it to traditional manual ontology design through a user study involving typical ontology engineering tasks on multiple domains.
The document discusses reusing information from XML Schemas to help speed up the process of designing domain ontologies. Traditionally, ontology engineers work closely with domain experts to manually create domain ontologies, which requires significant time and effort. The proposed approach is to automatically generate initial domain ontologies based on existing XML Schemas in order to expedite the design process. The main research question is how to speed up ontology design by leveraging available XML Schema information. A user study will evaluate the traditional manual approach versus the proposed semi-automatic approach. The goal is to generate ontologies from various domains to test the generalizability of the approach.
The document provides an overview of UML 2.4 and describes how to model systems using class diagrams and activity diagrams. It explains key components of class diagrams like classes, attributes, associations, and generalizations. It also describes important elements of activity diagrams such as partitions, flows, actions, and interrupts. The document uses examples from statistical data to demonstrate how to model studies, variables, questionnaires, and the process of publishing variable lists.
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KIT Graduiertenkolloquium 11.05.2016
1. KIT – Die Forschungsuniversität in der Helmholtz-Gemeinschaft www.kit.edu
Validation Framework
for RDF-based Constraint Languages
M.Sc. (TUM) Thomas Hartmann
Graduiertenkolloquium, 11.05.2016
4. 4
common needs of data practitioners
W3C RDF Validation Workshop
2 international working groups on RDF validation
constraint languages
SPARQL Query Language for RDF
SPARQL Inferencing Notation (SPIN)
Web Ontology Language (OWL)
Shape Expressions (ShEx)
Resource Shapes (ReSh)
Description Set Profiles (DSP)
no clear favorite
RDF validation as research field
problem statement
5. 5
Which types of research data and related metadata
are not yet representable in RDF and
how to adequately model them
to be able to validate RDF data
against constraints extractable from these vocabularies?
research question 1
RQ1
LDOW (WWW 2013)
SemStats (ISWC 2013)
DC 2012
IASSIST Quarterly, 38(4) & 39(1), 7-16
IASSIST Quarterly, 38(4) & 39(1), 17-24
IASSIST Quarterly, 38(4) & 39(1), 25-37
IASSIST Quarterly, 38(4) & 39(1), 38-46
ESWC 2011 (Poster)
6. 6
development of 3 RDF vocabularies:
1. DDI-RDF Discovery Vocabulary (DDI-RDF)
to support the discovery of metadata on unit-record data
2. Physical Data Description (PHDD)
to describe data in tabular format and its physical properties
3. The SKOS Extension for Statistics (XKOS)
to describe the structure and textual properties of
formal statistical classifications
to describe relations between classifications and
concepts and among concepts
contribution 1
RQ1
8. 8
XML, XML Schema (XSD)
RDF, Web Ontology Language (OWL)
XML Schemas > OWL ontologies
time-consuming work designing domain ontologies from scratch by hand
reuse information contained in XML Schemas
designing OWL domain ontologies
RQ2
9. 9
How to directly validate XML data
on semantically rich OWL axioms
using common RDF validation tools
when XML Schemas, adequately representing particular domains,
have already been designed?
research question 2
RQ2
10. 10
sub-class relationships
OWL hasValue restrictions on data properties
OWL universal restrictions on object properties
semantically rich OWL axioms
<library>
<book year="February 1890">
<author>
<name>Arthur Conan Doyle</name>
</author>
<title>The Sign of the Four</title>
</book>
</library>
Title ⊑ value.string
Year ⊑ value.integer
RQ2
11. 11
on formal logics based transformations
OWL axioms extracted out of XML Schemas
Explicitly
Implicitly
formally underpin transformations
to formally define and model semantics in a semantically correct way
complete extraction of XML Schemas' structural information
XML can directly be validated against semantically rich OWL axioms
any XML Schema is convertible to OWL
minimized effort designing OWL domain ontologies
contributions
IJMSO, 8(3)
RQ2
13. 13
1. step of approach
executed generic test cases created out of the XML Schema meta-model
transformed XML Schemas of 6 XML standards
2. step of approach
specified SWRL rules for 3 OWL domain ontologies
verified hypothesis
determined effort for traditional manual approach
estimated effort for semi-automatic approach
DDI-RDF serves as OWL domain ontology
The effort and the time needed to deliver high quality domain ontologies from scratch
by reusing information of already existing XML Schemas is much less than
creating domain ontologies completely manually and from the ground up.
evaluation
IJMSO, 8(3)
RQ2
16. 16
Which types of constraints
must be expressible by constraint languages to meet
all collaboratively and comprehensively identified requirements
to formulate constraints and validate RDF data?
research question 3
RQ3
17. 17
published 81 constraint types
constraints are instantiated from constraint types
each constraint type corresponds to a specific requirement
types of constraints on RDF data
RQ3
18. 18
expressivity of constraint languages
low-level implementation languages vs. high-level constraint languages
OWL 2 is the most expressive high-level constraint language
RQ3
19. 19
high-level constraint languages either
lack an implementation or
are based on different implementations
How to consistently validate RDF data
against constraints of any constraint type
expressed in any RDF-based constraint language?
research question 4-1
RQ4
20. 20
SPIN as basic validation framework
validation environment for RDF-based constraint languages
constraint languages are translated into SPARQL
represented in RDF in form of a SPIN mapping
a SPIN mapping contains one SPIN construct template
for each supported constraint type
consistent validation across
RDF-based constraint languages
DC 2014
RQ4
23. 23
full implementations for
all OWL 2 and DSP language constructs
all constraint types expressible in OWL 2 and DSP
major constraint types representable by ShEx and ReSh
validation environment
http://purl.org/net/rdfval-demo
RQ4
24. 24
constraints and constraint language constructs
must be representable in RDF
constraint languages and supported constraint types
must be expressible in SPARQL
limitations
RQ4
25. 25
How to represent constraints of any constraint type and
how to reduce the representation of
constraints of any constraint type
to the absolute minimum?
research question 4-2
RQ4
26. 26
abstraction layer
enables to express each constraint type
straight-forward mappings from high-level constraint languages
based on formal logics
validation framework
for RDF-based constraint languages
RQ4
32. 32
framework is solely based on the abstract definitions of constraint types
just 1 SPIN mapping for each constraint type
How to ensure for any constraint type that
RDF data is consistently validated against
semantically equivalent constraints of the same constraint type
across RDF-based constraint languages?
research question 4-3
RQ4
33. 33
mappings from constraint languages to the abstraction layer and back
enable…
How to ensure for any constraint type that
semantically equivalent constraints of the same constraint type
can be transformed
from one RDF-based constraint language to another?
RQ4
research question 4-4
34. 34
What is the role reasoning plays in practical data validation?
research question 5-1
RQ5
SEMANTiCS 2015
38. 38
For which constraint types validation results differ
(1) if the CWA or the OWA and
(2) if the UNA or the nUNA is assumed?
CWA dependent: 56.8%
UNA dependent: 66.6%
research question 5-3
RQ5
40. 40
collected 115 constraints
from vocabularies or domain experts
on 3 common vocabularies
well-established (QB, SKOS)
under development (DDI-RDF)
classified constraints
implemented constraints
evaluation
evaluation
ICSC 2016
33 SPARQL endpoints
41. 41
classification of constraint types
RDFS/OWL based
constraint language based
SPARQL based
classification of constraints
informational
warning
error
evaluation
classification
42. 42
C (constraints), CV (constraint violations)
values in %
evaluation
main finding
C CV
SPARQL 63.2 78.2
CL 34.7 21.8
RDFS/OWL 35.6 21.8
44. 44
RQ1: future work
publication of RDF vocabularies
DDI Alliance specifications
W3C recommendation for DDI-RDF
DDI-Lifecycle MD (Model-Driven)
new requirements based on experiences with DDI-RDF
international working group: DDI Moving Forward Project
individual contributions
formalize conceptual model (using UML 2)
conceptualize and implement diverse model serializations (e.g., RDFS/OWL)
future work
45. 45
aligning PHDD and CSV on the WEB
overlap in the description of tabular data in CSV format
broader scope of PHDD
description of tabular data with fixed record length
description of tabular data with multiple records per case
evaluation for use in DDI-Lifecycle MD
RQ1: future work
future work
46. 46
RQ2: future work
bidirectional transformations from models of any meta-model to OWL
generalize from XSD meta-model based unidirectional transformations
from XSD models into OWL models
enable to validate any data against constraints extractable from models of
any meta-model using common RDF validation tools
future work
47. 47
RQ3: future work
maintain and extend RDF validation database
collect case studies and use cases
extract requirements
publish constraint types
future work
48. 48
RQ4: future work
SPIN mappings for constraint languages not expressible in SPARQL
keep framework and constraining elements in sync
combine the framework with SHACL
derive SHACL extensions with SPARQL bodies
define mappings from SHACL to the abstraction layer and back
synchronize consistent implementations of constraint types
future work
49. 49
acknowledgements, publications, research data
29 publications
5 journal articles, 9 conference articles, 3 workshop articles,
2 specifications, 10 technical reports
1. author of all (except 1) journal articles, conference articles, workshop articles
research data
KIT research data repository: http://dx.doi.org/10.5445/BWDD/11
GitHub repository: https://github.com/github-thomas-hartmann/phd-thesis
international working groups
DCMI RDF Application Profiles Task Group
part of the editorial board
RDF Vocabularies Working Group
editor for DDI-RDF and PHDD
W3C RDF Data Shapes Working Group
DDI Moving Forward Project
50. 50
outlook and summary of main contributions
provide a basis for continued research
incorporate findings of this thesis into the working groups
RDF vocabularies
RDFication of XML
set of constraint types
validation framework for RDF-based constraint languages
role of reasoning for data validation
THANK YOU!
52. 52
publications: journal articles
1. Bosch, Thomas & Mathiak, B. (2015). Use Cases Related to an Ontology of the Data Documentation
Initiative. IASSIST Quarterly, 38(4) & 39(1), 25–37. http://iassistdata.org/iq/issue/38/4
2. Bosch, Thomas, Olsson, O., Gregory, A., & Wackerow, J. (2015c). DDI-RDF Discovery - A Discovery Model
for Microdata. IASSIST Quarterly, 38(4) & 39(1), 17–24. http://iassistdata.org/iq/issue/38/4
3. Bosch, Thomas & Zapilko, B. (2015). Semantic Web Applications for the Social Sciences. IASSIST Quarterly,
38(4) & 39(1), 7–16. http://iassistdata.org/iq/issue/38/4
4. Schaible, J., Zapilko, B., Bosch, Thomas, & Zenk-Möltgen, W. (2015). Linking Study Descriptions to the
Linked Open Data Cloud. IASSIST Quarterly, 38(4) & 39(1), 38–46. http://iassistdata.org/iq/issue/38/4
5. Bosch, Thomas & Mathiak, B. (2013b). How to Accelerate the Process of Designing Domain Ontologies
based on XML Schemas. International Journal of Metadata, Semantics and Ontologies - Special Issue on
Metadata, Semantics and Ontologies for Web Intelligence, 8(3), 254 – 266.
http://www.inderscience.com/info/inarticle.php?artid=57760
Please note that in 2015, my last name changed from Bosch to Hartmann.
53. 53
publications: articles in conference proceedings
1. Hartmann, Thomas, Zapilko, B., Wackerow, J., & Eckert, K. (2016). Validating RDF Data Quality using
Constraints to Direct the Development of Constraint Languages. In Proceedings of the 10th International
Conference on Semantic Computing (ICSC 2016) Laguna Hills, California, USA: IEEE.
http://www.ieee-icsc.com/
2. Bosch, Thomas & Eckert, K. (2015). Guidance, Please! Towards a Framework for RDF-based Constraint
Languages. In Proceedings of the 15th DCMI International Conference on Dublin Core and Metadata
Applications (DC 2015) São Paulo, Brazil.
http://dcevents.dublincore.org/IntConf/dc-2015/paper/view/386/368
3. Bosch, Thomas, Acar, E., Nolle, A., & Eckert, K. (2015a). The Role of Reasoning for RDF Validation. In
Proceedings of the 11th International Conference on Semantic Systems (SEMANTiCS 2015) (pp. 33–40).
Vienna, Austria: ACM. http://doi.acm.org/10.1145/2814864.2814867
4. Bosch, Thomas & Eckert, K. (2014a). Requirements on RDF Constraint Formulation and Validation. In
Proceedings of the 14th DCMI International Conference on Dublin Core and Metadata Applications (DC 2014)
Austin, Texas, USA. http://dcevents.dublincore.org/IntConf/dc-2014/paper/view/257
5. Bosch, Thomas & Eckert, K. (2014b). Towards Description Set Profiles for RDF using SPARQL as
Intermediate Language. In Proceedings of the 14th DCMI International Conference on Dublin Core and
Metadata Applications (DC 2014) Austin, Texas, USA. http://dcevents.dublincore.org/IntConf/dc-
2014/paper/view/270
Please note that in 2015, my last name changed from Bosch to Hartmann.
54. 54
publications: articles in conference proceedings
6. Bosch, Thomas, Cyganiak, R., Wackerow, J., & Zapilko, B. (2012). Leveraging the DDI Model for Linked
Statistical Data in the Social, Behavioural, and Economic Sciences. In Proceedings of the 12th DCMI
International Conference on Dublin Core and Metadata Applications (DC 2012) Kuching, Sarawak, Malaysia.
http://dcpapers.dublincore.org/pubs/article/view/3654
7. Bosch, Thomas (2012). Reusing XML Schemas’ Information as a Foundation for Designing Domain
Ontologies. In P. Cudré-Mauroux, J. Heflin, E. Sirin, T. Tudorache, J. Euzenat, M. Hauswirth, J. Parreira, J.
Hendler, G. Schreiber, A. Bernstein, & E. Blomqvist (Eds.), The Semantic Web - ISWC 2012, volume 7650 of
Lecture Notes in Computer Science (pp. 437–440). Springer Berlin Heidelberg.
http://dx.doi.org/10.1007/978-3-642-35173-0_34
8. Bosch, Thomas & Mathiak, B. (2012). XSLT Transformation Generating OWL Ontologies Automatically
Based on XML Schemas. In Proceedings of the 6th International Conference for Internet Technology and
Secured Transactions (ICITST 2011), IEEE Xplore Digital Library (pp. 660–667). Abu Dhabi, United Arab
Emirates. http://edas.info/web/icitst2011/program.html
9. Bosch, Thomas, Wira-Alam, A., & Mathiak, B. (2011). Designing an Ontology for the Data Documentation
Initiative. In Proceedings of the 8th Extended Semantic Web Conference (ESWC 2011), Poster-Session
Heraklion, Greece. http://www.eswc2011.org/content/accepted-posters.html
Please note that in 2015, my last name changed from Bosch to Hartmann.
55. 55
publications: articles in workshop proceedings
Please note that in 2015, my last name changed from Bosch to Hartmann.
1. Bosch, Thomas, Cyganiak, R., Gregory, A., & Wackerow, J. (2013a). DDI-RDF Discovery Vocabulary: A
Metadata Vocabulary for Documenting Research and Survey Data. In Proceedings of the 6th Workshop on
Linked Data on the Web (LDOW 2013), 22nd International World Wide Web Conference (WWW 2013),
volume 996 Rio de Janeiro, Brazil. http://ceur-ws.org/Vol-996/
2. Bosch, Thomas, Zapilko, B., Wackerow, J., & Gregory, A. (2013b). Towards the Discovery of Person-Level
Data - Reuse of Vocabularies and Related Use Cases. In Proceedings of the 1st International Workshop on
Semantic Statistics (SemStats 2013), 12th International Semantic Web Conference (ISWC 2013), Sydney,
Australia. http://semstats.github.io/2013/proceedings
3. Bosch, Thomas & Mathiak, B. (2011). Generic Multilevel Approach Designing Domain Ontologies Based on
XML Schemas. In Proceedings of the 1st Workshop Ontologies Come of Age in the Semantic Web (OCAS 2011),
10th International Semantic Web Conference (ISWC 2011) (pp. 1–12). Bonn, Germany.
http://ceur-ws.org/Vol-809/
56. 56
publications: specifications
Please note that in 2015, my last name changed from Bosch to Hartmann.
1. Bosch, Thomas, Cyganiak, R., Wackerow, J., & Zapilko, B. (2016). DDI-RDF Discovery Vocabulary: A
Vocabulary for Publishing Metadata about Data Sets (Research and Survey Data) into the Web of Linked Data.
DDI Alliance Specification, DDI Alliance. http://rdf-vocabulary.ddialliance.org/discovery
2. Wackerow, J., Hoyle, L., & Bosch, Thomas (2016). Physical Data Description. DDI Alliance Specification, DDI
Alliance. http://rdf-vocabulary.ddialliance.org/phdd.html
57. 57
publications: technical reports
Please note that in 2015, my last name changed from Bosch to Hartmann.
1. Hartmann, Thomas (2016a). Validation Framework for RDF-based Constraint Languages - PhD Thesis
Appendix. Karlsruhe Institute of Technology (KIT), Karlsruhe. http://dx.doi.org/10.5445/IR/1000054062
2. Vompras, J., Gregory, A., Bosch, Thomas, & Wackerow, J. (2015). Scenarios for the DDI-RDF Discovery
Vocabulary. DDI Working Paper Series. http://dx.doi.org/10.3886/DDISemanticWeb02
3. Alonen, M., Bosch, Thomas, Charles, V., Clayphan, R., Coyle, K., Dröge, E., Isaac, A., Matienzo, M., Pohl, A.,
Rühle, S., & Svensson, L. (2015b). Report on Validation Requirements. DCMI Draft, Dublin Core Metadata
Initiative (DCMI). http://wiki.dublincore.org/index.php/RDF_Application_Profiles/Requirements
4. Alonen, M., Bosch, Thomas, Charles, V., Clayphan, R., Coyle, K., Dröge, E., Isaac, A., Matienzo, M., Pohl, A.,
Rühle, S., & Svensson, L. (2015a). Report on the Current State: Use Cases and Validation Requirements. DCMI
Draft, Dublin Core Metadata Initiative (DCMI).
http://wiki.dublincore.org/index.php/RDF_Application_Profiles/UCR_Deliverable
5. Bosch, Thomas, Nolle, A., Acar, E., & Eckert, K. (2015b). RDF Validation Requirements - Evaluation and
Logical Underpinning. Computing Research Repository (CoRR), abs/1501.03933.
http://arxiv.org/abs/1501.03933
58. 58
publications: technical reports
Please note that in 2015, my last name changed from Bosch to Hartmann.
6. Hartmann, Thomas, Zapilko, B., Wackerow, J., & Eckert, K. (2015a). Constraints to Validate RDF Data
Quality on Common Vocabularies in the Social, Behavioral, and Economic Sciences. Computing Research
Repository (CoRR), abs/1504.04479. http://arxiv.org/abs/1504.04479
7. Hartmann, Thomas, Zapilko, B., Wackerow, J., & Eckert, K. (2015b). Evaluating the Quality of RDF Data
Sets on Common Vocabularies in the Social, Behavioral, and Economic Sciences. Computing Research
Repository (CoRR), abs/1504.04478. http://arxiv.org/abs/1504.04478
8. Bosch, Thomas, Wira-Alam, A., & Mathiak, B. (2014). Designing an Ontology for the Data Documentation
Initiative. Computing Research Repository (CoRR), abs/1402.3470. http://arxiv.org/abs/1402.3470
9. Bosch, Thomas & Mathiak, B. (2013a). Evaluation of a Generic Approach for Designing Domain Ontologies
Based on XML Schemas. Gesis Technical Report 08, Gesis - Leibniz Institute for the Social Sciences,
Mannheim, Germany. http://www.gesis.org/publikationen/archiv/gesis-technical-reports/
10. Block, W., Bosch, Thomas, Fitzpatrick, B., Gillman, D., Greenfield, J., Gregory, A., Hebing, M., Hoyle, L.,
Humphrey, C., Johnson, J., Linnerud, J., Mathiak, B., McEachern, S., Radler, B., Risnes, Ø., Smith, D., Thomas,
W., Wackerow, J., Wegener, D., & Zenk-Möltgen, W. (2012). Developing a Model-Driven DDI Specification.
DDI Working Paper Series
59. 59
research questions
1. Which types of research data and related metadata are not yet representable in RDF and how
to adequately model them to be able to validate RDF data against constraints extractable
from these vocabularies?
2. How to directly validate XML data on semantically rich OWL axioms using common RDF
validation tools when XML Schemas, adequately representing particular domains, have
already been designed?
3. Which types of constraints must be expressible by constraint languages to meet all
collaboratively and comprehensively identified requirements to formulate constraints and
validate RDF data?
4. How to ensure for any constraint type that (1) RDF data is consistently validated against
semantically equivalent constraints of the same constraint type across RDF-based constraint
languages and (2) semantically equivalent constraints of the same constraint type can be
transformed from one RDF-based constraint language to another?
5. What is the role reasoning plays in practical data validation and for which constraint types
reasoning may be performed prior to validation to enhance data quality?
appendix
60. 60
summary of contributions
1. Development of three RDF vocabularies (1) to represent all types of research data and related metadata in
RDF and (2) to validate RDF data against constraints extractable from these vocabularies
2. Direct validation of XML data using common RDF validation tools against semantically rich OWL axioms
extracted from XML Schemas properly describing certain domains
3. Publication of 81 types of constraints that must be expressible by constraint languages to meet all jointly
and extensively identified requirements to formulate constraints and validate RDF data against constraints
4.1 Consistent validation across RDF-based constraint languages
4.2 Minimal representation of constraints of any type
4.3 For any constraint type, RDF data is consistently validated against semantically equivalent constraints of
the same constraint type across RDF-based constraint languages
4.4 For any constraint type, semantically equivalent constraints of the same constraint type can be
transformed from one RDF-based constraint language to another
5. We delineate the role reasoning plays in practical data validation and investigated for each constraint
type (1) if reasoning may be performed prior to validation to enhance data quality, (2) how efficient in
terms of runtime validation is performed with and without reasoning, and (3) if validation results depend
on different underlying semantics
6. Evaluation of the Usability of Constraint Types for Assessing RDF Data Quality
appendix
61. 61
summary of limitations
1. XML Schemas must adequately represent particular domains in a syntactically and semantically correct way
2. Constraints of supported constraint types must be representable in RDF
3. Constraint languages and supported constraint types must be expressible in SPARQL
4. The generality of the findings of the large-scale evaluation has to be proved for all vocabularies
appendix