This document discusses access control for RDF graphs using abstract models. It presents an abstract access control model defined using abstract tokens and operators to model the computation of access labels for inferred RDF triples. The model supports dynamic datasets and policies. Experiments show that annotation time increases with the number of implied triples, while evaluation time increases linearly with the total number of triples. The abstract model approach allows different concrete access control policies to be applied to the same dataset.
This document outlines the topics and subtopics that will be covered in an HP Education Services SCJP Oriented Core Java Program. The program will cover core Java concepts like OOP, generics, collections, threads, I/O and more across 36 topics. Each topic includes multiple subtopics that will be discussed to provide an in-depth understanding of Java programming.
The document discusses key concepts in object-oriented programming including classes, methods, interfaces, properties, and nested classes. It provides examples of class definitions in various languages like Java, C++, and C# to illustrate concepts like encapsulation, visibility modifiers, constructors, and accessor/mutator methods. It also covers topics like separation of definition and implementation, interfaces, properties, and class data fields.
Managing Binary Compatibility in Scala (Scala Lift Off 2011)mircodotta
Slides of my Scala Lift Off 2011 talk. The content of the presentation is mostly similar to the one presented at Scala Days 2011, with a few additions. Particularly, lazy values are discussed.
Presented in : JIST2015, Yichang, China
Prototype: http://rc.lodac.nii.ac.jp/rdf4u/
Video: https://www.youtube.com/watch?v=z3roA9-Cp8g
Abstract: It is known that Semantic Web and Linked Open Data (LOD) are powerful technologies for knowledge management, and explicit knowledge is expected to be presented by RDF format (Resource Description Framework), but normal users are far from RDF due to technical skills required. As we learn, a concept-map or a node-link diagram can enhance the learning ability of learners from beginner to advanced user level, so RDF graph visualization can be a suitable tool for making users be familiar with Semantic technology. However, an RDF graph generated from the whole query result is not suitable for reading, because it is highly connected like a hairball and less organized. To make a graph presenting knowledge be more proper to read, this research introduces an approach to sparsify a graph using the combination of three main functions: graph simplification, triple ranking, and property selection. These functions are mostly initiated based on the interpretation of RDF data as knowledge units together with statistical analysis in order to deliver an easily-readable graph to users. A prototype is implemented to demonstrate the suitability and feasibility of the approach. It shows that the simple and flexible graph visualization is easy to read, and it creates the impression of users. In addition, the attractive tool helps to inspire users to realize the advantageous role of linked data in knowledge management.
The document provides examples of representing data in RDF formats including RDF/XML, Notation 3, Turtle and triples. It shows how to represent basic statements and relationships between resources as well as more complex data structures like bags, sequences and collections. Examples are given for converting between the different RDF syntaxes and representing graphs in RDF/XML.
Fosdem 2011 - A Common Graph Database Access Layer for .Net and MonoAchim Friedland
This document discusses developing a common graph database access layer for .NET and Mono. It proposes a property graph model interface that allows adding vertices and edges to an in-memory graph and setting their properties. Pipes are introduced as a way to query and transform graph elements in a data flow framework. An ad hoc query language is suggested as a user-friendly way to explore a property graph. Finally, exposing graphs over HTTP/REST is covered as a method for accessing remote graphs.
This document outlines the topics and subtopics that will be covered in an HP Education Services SCJP Oriented Core Java Program. The program will cover core Java concepts like OOP, generics, collections, threads, I/O and more across 36 topics. Each topic includes multiple subtopics that will be discussed to provide an in-depth understanding of Java programming.
The document discusses key concepts in object-oriented programming including classes, methods, interfaces, properties, and nested classes. It provides examples of class definitions in various languages like Java, C++, and C# to illustrate concepts like encapsulation, visibility modifiers, constructors, and accessor/mutator methods. It also covers topics like separation of definition and implementation, interfaces, properties, and class data fields.
Managing Binary Compatibility in Scala (Scala Lift Off 2011)mircodotta
Slides of my Scala Lift Off 2011 talk. The content of the presentation is mostly similar to the one presented at Scala Days 2011, with a few additions. Particularly, lazy values are discussed.
Presented in : JIST2015, Yichang, China
Prototype: http://rc.lodac.nii.ac.jp/rdf4u/
Video: https://www.youtube.com/watch?v=z3roA9-Cp8g
Abstract: It is known that Semantic Web and Linked Open Data (LOD) are powerful technologies for knowledge management, and explicit knowledge is expected to be presented by RDF format (Resource Description Framework), but normal users are far from RDF due to technical skills required. As we learn, a concept-map or a node-link diagram can enhance the learning ability of learners from beginner to advanced user level, so RDF graph visualization can be a suitable tool for making users be familiar with Semantic technology. However, an RDF graph generated from the whole query result is not suitable for reading, because it is highly connected like a hairball and less organized. To make a graph presenting knowledge be more proper to read, this research introduces an approach to sparsify a graph using the combination of three main functions: graph simplification, triple ranking, and property selection. These functions are mostly initiated based on the interpretation of RDF data as knowledge units together with statistical analysis in order to deliver an easily-readable graph to users. A prototype is implemented to demonstrate the suitability and feasibility of the approach. It shows that the simple and flexible graph visualization is easy to read, and it creates the impression of users. In addition, the attractive tool helps to inspire users to realize the advantageous role of linked data in knowledge management.
The document provides examples of representing data in RDF formats including RDF/XML, Notation 3, Turtle and triples. It shows how to represent basic statements and relationships between resources as well as more complex data structures like bags, sequences and collections. Examples are given for converting between the different RDF syntaxes and representing graphs in RDF/XML.
Fosdem 2011 - A Common Graph Database Access Layer for .Net and MonoAchim Friedland
This document discusses developing a common graph database access layer for .NET and Mono. It proposes a property graph model interface that allows adding vertices and edges to an in-memory graph and setting their properties. Pipes are introduced as a way to query and transform graph elements in a data flow framework. An ad hoc query language is suggested as a user-friendly way to explore a property graph. Finally, exposing graphs over HTTP/REST is covered as a method for accessing remote graphs.
Linked data presentation to AALL 2012 bostonDiane Hillmann
The document discusses how traditional cataloging practices assume a "closed world" approach while the semantic web assumes an "open world". It notes that digital identities need clearer definition than physical resources. It also discusses bridging XML and RDF approaches, expressing AACR2 in technical ways, and ensuring RDA vocabularies can be extended and mapped to other schemas in specialized domains.
This document discusses ontology mapping. It begins with an introduction to the semantic web and ontologies. Ontology mapping is important for allowing different ontologies to be aligned and related. There are different types of ontology mapping including alignment, merging, and mapping. The document then surveys some popular ontology mapping techniques including GLUE, PROMPT, and QOM. It evaluates these techniques and discusses their inputs, outputs, and approaches. The document concludes that semantic web research is important for advancing web technologies and realizing the goals of web 3.0. Future work could involve developing new ontology mapping techniques and publishing research on existing mapping methods.
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.
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.
This document discusses several inference engines that can be used for semantic web applications: Pellet, FaCT, FaCT++, RacerPro, Kaon2, and HermiT. It analyzes and compares these inference engines based on their expressivity, algorithms, interfaces, and other features. The key purpose of inference engines is to infer new knowledge and relationships from existing semantic data using rules and ontologies. The document concludes that a comparative analysis of inference engines can help select the most appropriate one for a given semantic web application or research.
Towards Virtual Knowledge Graphs over Web APIsSpeck&Tech
ABSTRACT: Knowledge Graphs (KGs) are an emerging, highly flexible and Web-friendly technology for integrating, representing, and querying semi-structured data in a semantically rich model formalized by an Ontology. KGs may be built using specialized data management software (e.g., triplestores) or, by leveraging suitable mappings and query rewriting techniques, as "Virtual Knowledge Graph" (VKG) views over some legacy data source, such as a relational database. In this talk, we provide background information on VKGs and their underlying technologies, with particular emphasis on the open-source Ontop VKG engine, and we discuss ongoing research and development efforts towards their extension to Web APIs as a non-relational data source of practical relevance. This extension, supported by the HIVE and OntoCRM projects, would also enable transparent access to both static relational data and dynamically-computed Web API data as part of a regular VKG query.
BIO: Francesco Corcoglioniti is a researcher at the Free University of Bozen-Bolzano, Italy, where he contributes to research, development, and project collaborations related to Virtual Knowledge Graphs (VKG), their extensions, and their implementation in the open-source Ontop system.
This document provides an overview of a presentation on representing and connecting language data and metadata using linked data. It discusses the technological background of linked data and the collaborative research opportunities it provides for linguistics. It also outlines prospects for using linked data in linguistics by connecting annotated corpora, lexical-semantic resources, and linguistic databases to build a linguistic linked open data cloud.
The document summarizes Ivan Herman's presentation on semantic technology and business applications at the 5th June 2012 Semantic Technology & Business Conference in San Francisco. The presentation covered several topics relating to semantic technologies including knowledge graphs, linked data, ontologies, semantic search, semantic data integration, standards like RDF, OWL, and SPARQL, and applications of semantic technologies in domains like life sciences, publishing, and government. It also discussed ongoing and future work at the W3C relating to areas like provenance, access control, and constraints on semantic web data.
The document discusses Chapter 21 of an object database course. It provides an overview of object database standards, languages, and conceptual design. Specifically, it outlines the Object Data Management Group (ODMG) standard, which includes the Object Definition Language (ODL) and Object Query Language (OQL). It also describes the ODMG object model and how relationships, inheritance, and operations are handled differently in object databases compared to relational databases.
The document discusses object-oriented programming concepts in Java, including classes, objects, inheritance, encapsulation, and polymorphism. It provides examples and definitions of key OOP concepts like class, object, inheritance, abstraction, encapsulation, polymorphism, and the SOLID principles (single responsibility, open/closed, Liskov substitution, interface segregation, and dependency inversion). It also covers Java specifics like access modifiers, variables, and how to create objects in Java.
Mapping of extensible markup language-to-ontology representation for effectiv...IAESIJAI
Extensible markup language (XML) is well-known as the standard for data exchange over the internet. It is flexible and has high expressibility to express the relationship between the data stored. Yet, the structural complexity and the semantic relationships are not well expressed. On the other hand, ontology models the structural, semantic and domain knowledge effectively. By combining ontology with visualization effect, one will be able to have a closer view based on respective user requirements. In this paper, we propose several mapping rules for the transformation of XML into ontology representation. Subsequently, we show how the ontology is constructed based on the proposed rules using the sample domain ontology in University of Wisconsin-Milwaukee (UWM) and mondial datasets. We
also look at the schemas, query workload, and evaluation, to derive the extended knowledge from the existing ontology. The correctness of the ontology representation has been proven effective through supporting various types of complex queries in simple protocol and resource description framework query language (SPARQL) language.
The document discusses the DCMI Metadata Framework, which includes the DCMI Abstract Model (DCAM) and other components. DCAM defines core metadata constructs like properties and vocabularies, and provides a basis for defining interoperable vocabularies, profiles, and syntaxes. It also discusses challenges around cross-framework interoperability and terminology used for different types of metadata specifications.
This document provides an overview of RDF, RDFS, and OWL, which are graph data models used to represent data on the Semantic Web. It describes the core components of RDF, including URIs, triples, and data types. It also explains how RDF graphs can be represented in N-Triples format or XML. Additionally, it covers RDF Schema (RDFS) and how it adds a type system to RDF through classes, subclasses, domains, and ranges of properties. The document concludes by noting some limitations of RDF and RDFS in modeling complex constraints and relationships.
This document provides an introduction to the Semantic Web, covering topics such as what the Semantic Web is, how semantic data is represented and stored, querying semantic data using SPARQL, and who is implementing Semantic Web technologies. The presentation includes definitions of key concepts, examples to illustrate technical aspects, and discussions of how the Semantic Web compares to other technologies. Major companies implementing aspects of the Semantic Web are highlighted.
The JISC DC Application Profiles: Some thoughts on requirements and scopeEduserv Foundation
- The JISC has funded the development of Dublin Core Application Profiles (DCAPs) for specific resource types like scholarly works, images, and geospatial data.
- There is a tension between creating DCAPs that are highly specific to resource types versus more general profiles that allow for linking and querying across types.
- Existing conceptual models like FRBR provide a possible "core" model that DCAPs could harmonize with to facilitate integration and querying across resource types.
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.
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.
The document discusses the semantic web and case-based reasoning. It provides an overview of key concepts like ontology languages, RDF, OWL, and describes how case-based reasoning works and how it can be applied to the semantic web through a conversational case-based reasoning approach and prototype. The document also includes references for further information.
This document describes a Contextualized Knowledge Repository (CKR) framework that allows for representing and reasoning with contextual knowledge on the Semantic Web. The CKR extends the description logic SROIQ-RL to include defeasible axioms in the global context. Defeasible axioms can be overridden by local contexts, allowing exceptions. The CKR is composed of two layers - a global context containing metadata and defeasible axioms, and local contexts containing object knowledge with references. An interpretation of a CKR maps local contexts to descriptions logic interpretations over the object vocabulary, respecting references between contexts.
The document describes a Contextualized Knowledge Repository (CKR) framework for representing and reasoning with contextual knowledge on the Semantic Web. It discusses the need to make context explicit in the Semantic Web in order to represent knowledge that holds in specific contextual spaces like time, location, or topic. The CKR is presented as a formalism based on description logics that defines contexts as first-class objects and allows associating knowledge with contexts. It describes a prototype CKR implementation and outlines how a CKR could be used to represent open data about the Trentino region with contextual metadata.
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Similar to Access Control for RDF graphs using Abstract Models
Linked data presentation to AALL 2012 bostonDiane Hillmann
The document discusses how traditional cataloging practices assume a "closed world" approach while the semantic web assumes an "open world". It notes that digital identities need clearer definition than physical resources. It also discusses bridging XML and RDF approaches, expressing AACR2 in technical ways, and ensuring RDA vocabularies can be extended and mapped to other schemas in specialized domains.
This document discusses ontology mapping. It begins with an introduction to the semantic web and ontologies. Ontology mapping is important for allowing different ontologies to be aligned and related. There are different types of ontology mapping including alignment, merging, and mapping. The document then surveys some popular ontology mapping techniques including GLUE, PROMPT, and QOM. It evaluates these techniques and discusses their inputs, outputs, and approaches. The document concludes that semantic web research is important for advancing web technologies and realizing the goals of web 3.0. Future work could involve developing new ontology mapping techniques and publishing research on existing mapping methods.
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.
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.
This document discusses several inference engines that can be used for semantic web applications: Pellet, FaCT, FaCT++, RacerPro, Kaon2, and HermiT. It analyzes and compares these inference engines based on their expressivity, algorithms, interfaces, and other features. The key purpose of inference engines is to infer new knowledge and relationships from existing semantic data using rules and ontologies. The document concludes that a comparative analysis of inference engines can help select the most appropriate one for a given semantic web application or research.
Towards Virtual Knowledge Graphs over Web APIsSpeck&Tech
ABSTRACT: Knowledge Graphs (KGs) are an emerging, highly flexible and Web-friendly technology for integrating, representing, and querying semi-structured data in a semantically rich model formalized by an Ontology. KGs may be built using specialized data management software (e.g., triplestores) or, by leveraging suitable mappings and query rewriting techniques, as "Virtual Knowledge Graph" (VKG) views over some legacy data source, such as a relational database. In this talk, we provide background information on VKGs and their underlying technologies, with particular emphasis on the open-source Ontop VKG engine, and we discuss ongoing research and development efforts towards their extension to Web APIs as a non-relational data source of practical relevance. This extension, supported by the HIVE and OntoCRM projects, would also enable transparent access to both static relational data and dynamically-computed Web API data as part of a regular VKG query.
BIO: Francesco Corcoglioniti is a researcher at the Free University of Bozen-Bolzano, Italy, where he contributes to research, development, and project collaborations related to Virtual Knowledge Graphs (VKG), their extensions, and their implementation in the open-source Ontop system.
This document provides an overview of a presentation on representing and connecting language data and metadata using linked data. It discusses the technological background of linked data and the collaborative research opportunities it provides for linguistics. It also outlines prospects for using linked data in linguistics by connecting annotated corpora, lexical-semantic resources, and linguistic databases to build a linguistic linked open data cloud.
The document summarizes Ivan Herman's presentation on semantic technology and business applications at the 5th June 2012 Semantic Technology & Business Conference in San Francisco. The presentation covered several topics relating to semantic technologies including knowledge graphs, linked data, ontologies, semantic search, semantic data integration, standards like RDF, OWL, and SPARQL, and applications of semantic technologies in domains like life sciences, publishing, and government. It also discussed ongoing and future work at the W3C relating to areas like provenance, access control, and constraints on semantic web data.
The document discusses Chapter 21 of an object database course. It provides an overview of object database standards, languages, and conceptual design. Specifically, it outlines the Object Data Management Group (ODMG) standard, which includes the Object Definition Language (ODL) and Object Query Language (OQL). It also describes the ODMG object model and how relationships, inheritance, and operations are handled differently in object databases compared to relational databases.
The document discusses object-oriented programming concepts in Java, including classes, objects, inheritance, encapsulation, and polymorphism. It provides examples and definitions of key OOP concepts like class, object, inheritance, abstraction, encapsulation, polymorphism, and the SOLID principles (single responsibility, open/closed, Liskov substitution, interface segregation, and dependency inversion). It also covers Java specifics like access modifiers, variables, and how to create objects in Java.
Mapping of extensible markup language-to-ontology representation for effectiv...IAESIJAI
Extensible markup language (XML) is well-known as the standard for data exchange over the internet. It is flexible and has high expressibility to express the relationship between the data stored. Yet, the structural complexity and the semantic relationships are not well expressed. On the other hand, ontology models the structural, semantic and domain knowledge effectively. By combining ontology with visualization effect, one will be able to have a closer view based on respective user requirements. In this paper, we propose several mapping rules for the transformation of XML into ontology representation. Subsequently, we show how the ontology is constructed based on the proposed rules using the sample domain ontology in University of Wisconsin-Milwaukee (UWM) and mondial datasets. We
also look at the schemas, query workload, and evaluation, to derive the extended knowledge from the existing ontology. The correctness of the ontology representation has been proven effective through supporting various types of complex queries in simple protocol and resource description framework query language (SPARQL) language.
The document discusses the DCMI Metadata Framework, which includes the DCMI Abstract Model (DCAM) and other components. DCAM defines core metadata constructs like properties and vocabularies, and provides a basis for defining interoperable vocabularies, profiles, and syntaxes. It also discusses challenges around cross-framework interoperability and terminology used for different types of metadata specifications.
This document provides an overview of RDF, RDFS, and OWL, which are graph data models used to represent data on the Semantic Web. It describes the core components of RDF, including URIs, triples, and data types. It also explains how RDF graphs can be represented in N-Triples format or XML. Additionally, it covers RDF Schema (RDFS) and how it adds a type system to RDF through classes, subclasses, domains, and ranges of properties. The document concludes by noting some limitations of RDF and RDFS in modeling complex constraints and relationships.
This document provides an introduction to the Semantic Web, covering topics such as what the Semantic Web is, how semantic data is represented and stored, querying semantic data using SPARQL, and who is implementing Semantic Web technologies. The presentation includes definitions of key concepts, examples to illustrate technical aspects, and discussions of how the Semantic Web compares to other technologies. Major companies implementing aspects of the Semantic Web are highlighted.
The JISC DC Application Profiles: Some thoughts on requirements and scopeEduserv Foundation
- The JISC has funded the development of Dublin Core Application Profiles (DCAPs) for specific resource types like scholarly works, images, and geospatial data.
- There is a tension between creating DCAPs that are highly specific to resource types versus more general profiles that allow for linking and querying across types.
- Existing conceptual models like FRBR provide a possible "core" model that DCAPs could harmonize with to facilitate integration and querying across resource types.
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.
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.
The document discusses the semantic web and case-based reasoning. It provides an overview of key concepts like ontology languages, RDF, OWL, and describes how case-based reasoning works and how it can be applied to the semantic web through a conversational case-based reasoning approach and prototype. The document also includes references for further information.
Similar to Access Control for RDF graphs using Abstract Models (20)
This document describes a Contextualized Knowledge Repository (CKR) framework that allows for representing and reasoning with contextual knowledge on the Semantic Web. The CKR extends the description logic SROIQ-RL to include defeasible axioms in the global context. Defeasible axioms can be overridden by local contexts, allowing exceptions. The CKR is composed of two layers - a global context containing metadata and defeasible axioms, and local contexts containing object knowledge with references. An interpretation of a CKR maps local contexts to descriptions logic interpretations over the object vocabulary, respecting references between contexts.
The document describes a Contextualized Knowledge Repository (CKR) framework for representing and reasoning with contextual knowledge on the Semantic Web. It discusses the need to make context explicit in the Semantic Web in order to represent knowledge that holds in specific contextual spaces like time, location, or topic. The CKR is presented as a formalism based on description logics that defines contexts as first-class objects and allows associating knowledge with contexts. It describes a prototype CKR implementation and outlines how a CKR could be used to represent open data about the Trentino region with contextual metadata.
This document discusses leveraging crowdsourcing techniques and consistency constraints to optimize the reconciliation of schema matching networks. It proposes:
1) Defining consistency constraints within schema matching networks and designing validation questions for crowdsourced workers.
2) Using consistency constraints to reduce reconciliation error rates and the monetary cost of asking additional validation questions.
3) Modeling a crowdsourcing process for schema matching networks that aims to minimize cost while maximizing accuracy through the application of consistency constraints.
This document discusses privacy-preserving schema reuse. It introduces the challenges of defining privacy constraints, generating an anonymized schema from multiple schemas while satisfying privacy constraints, defining a utility function for anonymized schemas, and solving the optimization problem of finding the anonymized schema with the highest utility that satisfies all privacy constraints. Experimental results demonstrate the trade-off between privacy enforcement and utility loss. The solution presents an approach for generating anonymized schemas from multiple schemas in a privacy-preserving manner.
Authros: Nguyen Quoc Viet Hung (1), Nguyen Thanh Tam (1), Zoltán Miklós (2), Karl Aberer (1),
Avigdor Gal (3), and Matthias Weidlich (4)
1 École Polytechnique Fédérale de Lausanne
2 Université de Rennes 1
3 Technion – Israel Institute of Technology
4 Imperial College London
This document summarizes a demo of using SPARQLstream and Morphstreams to visualize transport data from Madrid's public transport company (EMT) in a tablet application. Static EMT data like bus stop locations are extracted and mapped to RDF, while live bus waiting time data streams are transformed and queried in real-time. This allows a Map4RDF iOS app to retrieve bus stop information and lookup estimated arrival times using SPARQL and SPARQLstream queries. The demo illustrates how standards like SSN and R2RML can integrate static and streaming sensor data for web-based applications.
The document discusses the need for a W3C community group on RDF stream processing. It notes there is currently heterogeneity in RDF stream models, query languages, implementations, and operational semantics. The speaker proposes creating a W3C community group to better understand these differences, requirements, and potentially develop recommendations. The group's mission would be to define common models for producing, transmitting, and continuously querying RDF streams. The presentation provides examples of use cases and outlines a template for describing them to collect more cases to understand requirements.
by Irene Celino, Simone Contessa, Marta Corubolo, Daniele Dell’Aglio, Emanuele Della Valle, Stefano Fumeo and Thorsten Krüger
CEFRIEL – Politecnico di Milano – SIEMENS
This document describes SciQL, a language that bridges the gap between science and relational database management systems (DBMS). SciQL allows for the seamless integration of relational and array paradigms within DBMSs. It defines arrays and tables as first-class citizens and supports named dimensions, flexible structure-based grouping, and the distinction between arrays and tables. SciQL aims to lower the barrier for scientists to use DBMSs for array-based data while revealing new optimization opportunities for databases.
by G. Larkou, J. Metochi, G. Chatzimilioudis and D. Zeinalipour-Yazti
Presented at: 1st IEEE International Workshop on Mobile Data Management Mining and Computing on Social Networks, collocated with IEEE MDM'13
This document summarizes research on implementing defeasible logic, a non-monotonic reasoning method, in a distributed manner using the MapReduce framework. Defeasible logic allows commonsense reasoning over low-quality data and has low computational complexity. However, existing implementations did not scale to huge datasets. The researchers developed a multi-argument MapReduce implementation of defeasible logic that distributes the reasoning process. Experimental evaluation on large datasets showed this approach provides scalable defeasible reasoning over distributed data. Future work will address challenges with non-stratified rulesets and test the approach on additional real-world applications and knowledge representation methods.
This document discusses data and knowledge evolution on the semantic web. It begins by explaining the limitations of the current web in representing semantic content and introduces the semantic web as a way to give data well-defined meaning. It then discusses how ontologies and datasets are used to describe semantic data and how datasets are dynamic and change over time. It also introduces linked open data as a way to interconnect datasets and the challenges this presents. Finally, it outlines the scope of the talk, which is to survey research areas related to managing dynamic linked datasets, including remote change management, repair, and data/knowledge evolution.
This document discusses evolving workflow provenance information in the presence of custom inference rules. It presents three inference rules for provenance data, including that actors are associated with all subactivities if one activity, objects and their parts are used together, and information objects are present where physical objects carrying them are. It examines handling updates to provenance knowledge bases using these rules either by deleting all inferred facts or only as needed, and considers complexity of different approaches.
Here are a few ways SciQL could help with this seismology use case:
1. The mseed array allows storing and querying the large seismic data in an efficient columnar format.
2. Window-based aggregation with dimensional grouping enables filtering signals by station/LTA ratios over time windows.
3. Views and queries on dimensional groups facilitate removing false positives by comparing signals across nearby stations over time.
4. Further window-based grouping and UDFs can extract signal windows for additional heuristic analysis.
By integrating the array and relational models, SciQL provides a declarative way to analyze large multidimensional scientific datasets like seismic signals interactively.
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.
This talk has been given at the 13th International Conference on Principles of Knowledge Representation and Reasoning (KR 2012) to be held in Rome, Italy, June 10-14, 2012 by Ilias Tahmazidis (FORTH).
Abstract:
We are witnessing an explosion of available data from the Web, government authorities, scientific databases, sensors and more. Such datasets could benefit from the introduction of rule sets encoding commonly accepted rules or facts, application- or domain-specific rules, commonsense knowledge etc. This raises the question of whether, how, and to what extent knowledge representation methods are capable of handling the vast amounts of data for these applications. In this paper, we consider nonmonotonic reasoning, which has traditionally focused on rich knowledge structures. In particular, we consider defeasible logic, and analyze how parallelization, using the MapReduce framework, can be used to reason with defeasible rules over huge data sets. Our experimental results demonstrate that defeasible reasoning with billions of data is performant, and has the potential to scale to trillions of facts.
The presentation was delivered during the 1st International Conference on Health Information Science (HIS 2012) on April 9th, 2012 in Beijing, China.
Abstract:
In cytomics bookkeeping of the data generated during lab experiments is crucial. The current approach in cytomics is to conduct High-Throughput Screening (HTS) experiments so that cells can be tested under many different experimental conditions. Given the large amount of different conditions and the readout of the conditions through images, it is clear that the HTS approach requires a proper data management system to reduce the time needed for experiments and the chance of man-made errors. As different types of data exist, the experimental conditions need to be linked to the images produced by the HTS experiments with their metadata and the results of further analysis. Moreover, HTS experiments never stand by themselves, as more experiments are lined up, the amount of data and computations needed to analyze these increases rapidly. To that end cytomic experiments call for automated and systematic solutions that provide convenient and robust features for scientists to manage and analyze their data. In this paper, we propose a platform for managing and analyzing HTS images resulting from cytomics screens taking the automated HTS workflow as a starting point. This platform seamlessly integrates the whole HTS workflow into a single system. The platform relies on a modern relational database system to store user data and process user requests, while providing a convenient web interface to end-users. By implementing this platform, the overall workload of HTS experiments, from experiment design to data analysis, is reduced significantly. Additionally, the platform provides the potential for data integration to accomplish genotype-to-phenotype modeling studies.
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Abstract:
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Access Control for RDF graphs using Abstract Models
1. ACCESS CONTROL FOR
RDF GRAPHS USING
ABSTRACT MODELS
Vassilis Papakonstantinou
(papv@ics.forth.gr)
Joint work with:
Maria Michou, Irini Fundulaki,
Giorgos Flouris, Grigoris Antoniou
SACMAT 2012
2. MOTIVATION
June 20-22, 2012
Why RDF Data?
RDF is the de-facto standard for publishing data in
the Linked Open Data Cloud
E-Science (astronomy, life
sciences, earth sciences)
Public Government Data
(US, UK, The Netherlands, … )
Social Networks
SACMAT-2012
DBPedia, CIA World FactBook, …
Why Access Control?
Crucial for sensitive content since it ensures the
2
selective exposure of information to different
classes of users
3. MAIN CONTRIBUTIONS
June 20-22, 2012
Fine-grained Access Control Model for RDF
defined at the level of RDF triples
focus on read-only permissions
with support for RDFS inference to infer new
knowledge
encodes how an access label has been computed
Supports dynamic datasets
SACMAT-2012
Supports dynamic access control policies
Implementation and experiments on top of
MonetDB and PostgreSQL
3
4. OUTLINE
June 20-22, 2012
Preliminaries: RDF and RDF Schema
Current models: Access Control Annotations
Our approach: Abstract Access Control Models
Implementation
Experiments
SACMAT-2012
4
5. RESOURCE DESCRIPTION
FRAMEWORK (RDF)
June 20-22, 2012
General-purpose language for representing
information in the Semantic Web
Information represented using triples
(s, p, o) [subject, predicate, object]
s, p, o: URIs or literals
Example: (&a, firstName, “Alice”)
SACMAT-2012
firstName
&a “Alice”
An entity being A property of the entity The value of the
described (first name) predicate
(the first name)
[subject] [predicate] [object] 5
6. RDF SCHEMA
June 20-22, 2012
RDF Schema is a Vocabulary Agent
Description Language
Used to define the vocabulary used
sc
in an RDF graph. (Class, Property,
subClassOf, subPropertyOf,
domain, range) Person sc
Semantics add simple reasoning
SACMAT-2012
sc
capabilities
e.g. inference rules for subClass or Student
subProperty relations
(sc rdfs:subClassOf)
6
7. CURRENT MODELS: ACCESS
CONTROL ANNOTATIONS
June 20-22, 2012
Access control provided at the level of RDF triples
Represented by RDF quadruples (s,p,o,l)
subject predicate object label
Student sc Person Accessible
Person sc Agent Inaccessible
SACMAT-2012
In implied triples semantics are applied directly to
give them labels
subject predicate object label
Acc.∧ Inacc.
Inaccessible 7
Student sc Agent
8. PROBLEMS OF ACCESS CONTROL
ANNNOTATIONS
June 20-22, 2012
Easy, but not amenable to changes
If one access label of one triple changes, it has
cascading effects to implied labels of other triples
Cannot know which labels/triples are affected
Re-computation of access labels is necessary (for the
entire dataset)
If the access label of one triple changes
If a triple is deleted, modified or added
SACMAT-2012
If the semantics according to which labels of inferred triples
are computed change
If the policy changes (e.g. a liberal policy becomes
conservative)
8
9. OUR APPROACH: ABSTRACT ACCESS
CONTROL MODELS
June 20-22, 2012
Abstract Access Control Model defined by a set
of abstract tokens and abstract operators to
model
computation of access labels of implied RDF triples
propagation of access labels
Access Control Authorizations associate triples in
the RDF/S graph with abstract tokens: quadruples
SACMAT-2012
RDFS inference rules for computing the access
labels of implied quadruples
Propagation rules to specify how access labels are
propagated along the subClassOf and
9
subPropertyOf relations
10. ABSTRACT ACCESS CONTROL
MODELS
June 20-22, 2012
Abstract Access Control Model defined by a set
of abstract tokens and abstract operators
⊙: binary operator over access tokens to model RDFS
inference
computes the label of implied RDF triples for the
subClassOf/subPropertyOf and type hierarchies
SACMAT-2012
(A1, sc, A2, l1) (A2, sc, A3, l2) (A1, sc, A3, l1 ⊙ l2)
10
11. ABSTRACT ACCESS CONTROL
MODELS
June 20-22, 2012
Abstract Access Control Model defined by a set
of abstract tokens and abstract operators
⊗ : unary operator over multi-sets of access tokens to
model propagation of access labels
propagates the access labels along the subclass/subproperty and
type hierarchies
the subclasses of a class inherit the label of its superclass, the
instances of a class inherit the label of its superclass, etc.
SACMAT-2012
(A1, type, class, l1) (A2, sc, A1, l2) (A2, type, class, l3) (A2, type, class, ⊗ (l1 ))
11
12. ANNOTATION - DETERMINING THE
ABSTRACT EXPRESSIONS (1/3)
June 20-22, 2012
Apply authorizations Authorizations (Query, access token)
we are going from A1: (construct {?x sc ?y}, at1)
triples to quadruples A2: (construct {?x type Student }, at2)
A3: (construct {?x type class}, at3)
A4: (construct {?x ?p Person}, at4)
id S p o id s p o l
t1 Student sc Person q1 Student sc Person at1
SACMAT-2012
t2 Person sc Agent q2 Person sc Agent at1
t3 &a type Student q3 &a type Student at2
t4 &a lastName “Smith” q4 &a lastName “Smith” ⊥
t5 Agent type Class q5 Agent type Class at3 12
q6 Student sc Person at4
13. ANNOTATION - DETERMINING THE
ABSTRACT EXPRESSIONS (2/3)
June 20-22, 2012
Apply RDFS id s p o l
inference rules q1 Student sc Person at1
New quadruples Person sc Agent at1
q2
produced
q3 &a type Student at2
R1 q6 Student sc Person at4
(A, sc, B, l1)
(A, sc, C, l1⊙l2) …
(B, sc, C, l2)
SACMAT-2012
q7 Student sc Agent q1 q2
R2
(x, type, A, l1) q8 Student sc Agent q6 q2
(x, type, B, l1⊙l2)
(A, sc, B, l2) q9 &a type Person q3 q1
q10 &a type Agent q3 (q1 q2 )
… 13
14. ANNOTATION - DETERMINING THE
ABSTRACT EXPRESSIONS (3/3)
June 20-22, 2012
Apply propagation id s p o l
rules Agent type Class at3
q5
Add new labels to
q10 &a type Agent q3 (q1 q2 )
existing triples
…
e.g. classes propagate
q11 &a type Agent ⊗q5
labels to their
…
instances and
SACMAT-2012
subclasses R5
R6 (B, type, class, l1)
(A, type, class, l1) (A, sc, B, l2) (x, type, class, ⊗l1)
(x, type, A, ⊗l1)
(x, type, A, l2) (A, type, class, l3)
14
15. EVALUATION - DETERMINING
ACCESSIBILITY
June 20-22, 2012
We have to define
Set of Concrete Tokens and a Mapping from
abstract to concrete tokens
Set of Concrete operators that implement the
abstract ones
Conflict resolution operator to resolve ambiguous
SACMAT-2012
labels
Access Function to decide when a triple is
accessible
15
17. EVALUATION FOR CP1 (1/3)
COMPUTE LABELS
June 20-22, 2012
Concrete policy 1 id s p o l
LP = {true, false} q1 Student sc Person at1
true
∧⊙
q2 Person sc Agent true
at1
IDL ⊗
q5 Agent type Class false
at3
∧⊕
q6 Student sc Person false
at4
q7 Student sc Agent true⊙q2
qtrue
1 ∧ true
Map abstract tokens
SACMAT-2012
q8 Student sc Agent q6⊙ true
false∧q2
false
to concrete q11 &a type Agent false
⊗q5
true at1, at2
false at3, at4
17
18. EVALUATION FOR CP1 (2/3)
AMBIGUOUS LABELS REMOVAL
June 20-22, 2012
Back from quadruples to triples
subject predicate object label
Student sc Person true
Student sc Person false
SACMAT-2012
subject predicate object label
Student sc Person true∧ false
false
18
19. EVALUATION FOR CP1 (3/3)
DETERMINING ACCESSIBILITY
June 20-22, 2012
The essence of access control:
subject predicate object label
Student sc Person false
Inaccessible
Person sc Agent true
Accessible
&a type Student true
Accessible
&a lastName “Smith” false
Inaccessible
SACMAT-2012
Agent type Class false
Inaccessible
19
20. PROS & CONS OF ABSTRACT ACCESS
CONTROL MODELS
June 20-22, 2012
Pros:
The same application can experiment with different
concrete policies over the same dataset
liberal vs conservative policies for different classes of users
Different applications can experiment with different
concrete policies for the same data
In the case of updates there is no need to re-
compute the inferred triples
SACMAT-2012
Cons:
overhead in the required storage space
algebraic expressions can become complex depending on the
structure of the dataset
20
21. IMPLEMENTATION
June 20-22, 2012
Used a relational schema to store quadruples
and their labels (including abstract expressions)
Using stored procedure mechanism through
which we perform annotation and evaluation
MonetDB
PostgreSQL
SACMAT-2012
21
22. EXPERIMENTS
June 20-22, 2012
Experiments
Experiment 1: annotation time (the time required
to compute the inferred triples with their labels and
the propagated labels)
Experiment 2: evaluation time (a) (the time
needed to compute for a concrete policy, the concrete
access labels of all RDF triples)
Experiment 3: evaluation time (b) (the time
SACMAT-2012
needed to compute for a concrete policy, the concrete
access label of a percentage of the RDF triples)
Datasets:
Synthetic Schemas produced with PowerGen
22
Real: CIDOC, GO
23. EXPERIMENTAL RESULTS
ANNOTATION TIME – MONETDB
(SYNTHETIC)
June 20-22, 2012
Annotation time
increases as the
number of implied
triples increases
Plunges are due to
changes in the
structure of the
SACMAT-2012
ontology
(reduction of the
depth)
152 Synthetic ontologies
100-1000 classes, 113-1635 properties, 124-50295 class instances 23
and 110-1321 property instances
Different depth for the sc and sp hierarchies (from 4 to 8)
24. EXPERIMENTAL RESULTS
EVALUATION TIME (FULL)
June 20-22, 2012
Evaluation time
increases linearly
as the number of
total triples
increases
MonetDB
outperforms
PostgreSQL
SACMAT-2012
Some of synthetic
datasets couldn’t
be evaluated
24
25. EXPERIMENTAL RESULTS
EVALUATION TIME (DATASET
PERCENTAGE) - MONETDB
June 20-22, 2012
Evaluation time
for largest dataset
that evaluated
successfully on
Experiment 2
Similar conclu-
sions as with
SACMAT-2012
Experiment 2
25
26. EXPERIMENTAL RESULTS - REAL
DATASETS
June 20-22, 2012
CIDOC
Annotation time
MonetDB: 69ms
PostgreSQL: 4000ms
Evaluation time (full)
MonetDB – CP1: 7775ms
MonetDB – CP2: 3923ms
GO
SACMAT-2012
Annotation time
MonetDB: 32s
PostgreSQL: 844s
Evaluation time (full) 26
Exceeded our set timeout
27. CONCLUSIONS
June 20-22, 2012
Proposed a new paradigm based on abstract
models and operators
Advantages
Flexibility and easy adaptation to change (no re-
computation necessary)
Easy experimentation with different access control
policies
SACMAT-2012
Disadvantages
Increased space requirements
Overhead at query time (for evaluation)
Suitable for dynamic datasets
27
29. EXPERIMENTAL RESULTS
ANNOTATION TIME –
POSTGRESQL(SYNTHETIC)
June 20-22, 2012
Annotation time
increases as the
number of implied
triples increases
One plunges are
due to change in
the structure of
SACMAT-2012
the ontology
(reduction of the
depth)
Up to 1000 classes, 1635 properties, 50167 class instances and 95
property instances before reaching the timeout. 29
30. IMPLEMENTATION
June 20-22, 2012
Used a relational schema to store quadruples
Quad(qid, s, p, o, propop, inferop, label)
inferop, propop: boolean values indicating whether the label
is obtained through propagation or inference
LabelStore(qid, qid_uses)
stores the access label of a triple
qid: the quadruple whose label is stored
qid_uses: the explict quadruple’s qid through which qid
SACMAT-2012
produced.
30
31. IMPLEMENTATION
id s p o l id s p o iop pop l
q1 Student sc Person at1 q1 Student sc Person f f at1
q2 Person sc Agent at1 q2 Person sc Agent f f at1
q3 &a type Student at2 q3 &a type Student f f at2
q5 Agent type Class at3 q5 Agent type Class f f at3
q6 Student sc Person at4 q6 Student sc Person f f at4
q7 Student sc Agent at1⊙at1 q7 Student sc Agent t f null
q10 &a type Agent at2⊙(at1⊙at1) q9 &a type Agent t f null
q11 &a type Agent ⊗at3 q10 Person Sc Agent f t null
Quadruples (Motivating example) Quad(qid,s,p,o,propop,inferop,label)
31
32. IMPLEMENTATION
June 20-22, 2012
id s p o l qid qid_uses
q1 Student sc Person at1 q7 q1
q2 Person sc Agent at1 q7 q2
q3 &a type Student at2 q10 q3
q5 Agent type Class at3 q10 q1
q6 Student sc Person at4 q10 q2
q7 Student sc Agent at1⊙at1 q11 q5
SACMAT-2012
q10 &a type Agent at2⊙(at1⊙at1)
q11 &a type Agent ⊗at3
Quadruples (Motivating example) Labelstore(qid,qid_uses)
32