This document proposes a new OWL profile called OWL LD (Linked Data) based on an analysis of ontology language usage in the Billion Triple Challenge dataset. The analysis found that RDFS features were most prominent, while only certain OWL features expressed in single triples were widely used. OWL LD is defined as a subset of OWL RL that includes only these single-triple features to balance expressiveness with easier implementation. Rules and a grammar are also defined to allow OWL LD ontologies to take advantage of existing OWL reasoning tools.
FOAF (Friend of a Friend) is a vocabulary for describing people, their activities and their relationships. It allows personal profile pages to be interlinked to form a social web of machine-readable data. The FOAF ontology defines terms like Person, Agent and their properties like name, email and knows. FOAF documents must be written in RDF and can link to each other to form a semantic web of relationships between people.
The document discusses ontologies and the RDF-S/OWL languages for defining ontologies. It defines ontologies as formal, explicit specifications of shared conceptualizations and describes some key parts of ontologies including concepts, relations, instances, and axioms. It provides an example ontology about artists and the works they create. RDF-S semantics are discussed for defining subclasses, subproperties, domains, and ranges within an ontology.
This document discusses RDF and SPARQL. It provides an introduction to RDF, including the basic RDF data model of subject-predicate-object triples. It then discusses SPARQL, the query language for retrieving and manipulating RDF data, including basic SPARQL syntax examples. It also briefly mentions the SPARQL protocol for accessing RDF data via HTTP endpoints.
This document provides an overview of SPARQL, the query language for the Semantic Web. SPARQL allows querying RDF data by matching triple patterns and combining them with operations like optional and union patterns. Key features discussed include the anatomy of SPARQL queries, matching RDF literals and numerical values, filtering solutions, and defining datasets with the FROM clause. The document also covers SPARQL result forms and resources for learning more about SPARQL implementations and extensions.
The forth lecture of the course I'm giving on "Interoperability and Semantic Technologies" at Politecnico di Milano in the academic year 2015-16. It presents an introduction to RDF. It starts presenting the data model. Then it presents the turtle serialization. It compares XML vs. RDF. Finally, it provides few informations about RDFa and Linked Data.
This document provides an overview of software architectures for semantic web applications, including local access, mixed access, and remote access architectures. Local access architectures involve storing and querying RDF data locally using a triplestore and API. Remote access architectures involve querying RDF data owned by a third party using the SPARQL Protocol over HTTP or SOAP. The SPARQL Protocol is an abstract specification for remotely executing SPARQL queries in a standards-based way.
This document provides an overview of the Resource Description Framework (RDF). It begins with background information on RDF including URIs, URLs, IRIs and QNames. It then describes the RDF data model, noting that RDF is a schema-less data model featuring unambiguous identifiers and named relations between pairs of resources. It also explains that RDF graphs are sets of triples consisting of a subject, predicate and object. The document also covers RDF syntax using Turtle and literals, as well as modeling with RDF. It concludes with a brief overview of common RDF tools including Jena.
FOAF (Friend of a Friend) is a vocabulary for describing people, their activities and their relationships. It allows personal profile pages to be interlinked to form a social web of machine-readable data. The FOAF ontology defines terms like Person, Agent and their properties like name, email and knows. FOAF documents must be written in RDF and can link to each other to form a semantic web of relationships between people.
The document discusses ontologies and the RDF-S/OWL languages for defining ontologies. It defines ontologies as formal, explicit specifications of shared conceptualizations and describes some key parts of ontologies including concepts, relations, instances, and axioms. It provides an example ontology about artists and the works they create. RDF-S semantics are discussed for defining subclasses, subproperties, domains, and ranges within an ontology.
This document discusses RDF and SPARQL. It provides an introduction to RDF, including the basic RDF data model of subject-predicate-object triples. It then discusses SPARQL, the query language for retrieving and manipulating RDF data, including basic SPARQL syntax examples. It also briefly mentions the SPARQL protocol for accessing RDF data via HTTP endpoints.
This document provides an overview of SPARQL, the query language for the Semantic Web. SPARQL allows querying RDF data by matching triple patterns and combining them with operations like optional and union patterns. Key features discussed include the anatomy of SPARQL queries, matching RDF literals and numerical values, filtering solutions, and defining datasets with the FROM clause. The document also covers SPARQL result forms and resources for learning more about SPARQL implementations and extensions.
The forth lecture of the course I'm giving on "Interoperability and Semantic Technologies" at Politecnico di Milano in the academic year 2015-16. It presents an introduction to RDF. It starts presenting the data model. Then it presents the turtle serialization. It compares XML vs. RDF. Finally, it provides few informations about RDFa and Linked Data.
This document provides an overview of software architectures for semantic web applications, including local access, mixed access, and remote access architectures. Local access architectures involve storing and querying RDF data locally using a triplestore and API. Remote access architectures involve querying RDF data owned by a third party using the SPARQL Protocol over HTTP or SOAP. The SPARQL Protocol is an abstract specification for remotely executing SPARQL queries in a standards-based way.
This document provides an overview of the Resource Description Framework (RDF). It begins with background information on RDF including URIs, URLs, IRIs and QNames. It then describes the RDF data model, noting that RDF is a schema-less data model featuring unambiguous identifiers and named relations between pairs of resources. It also explains that RDF graphs are sets of triples consisting of a subject, predicate and object. The document also covers RDF syntax using Turtle and literals, as well as modeling with RDF. It concludes with a brief overview of common RDF tools including Jena.
This document provides an introduction to XSPARQL, a language for transforming between RDF and XML. It discusses how transformations between RDF and XML can be challenging due to different syntaxes and serializations used to represent the same RDF graph. It notes that while SPARQL is good for querying RDF, it does not provide a way to produce arbitrary XML output. The document then introduces XSPARQL as a transformation language that combines XML, RDF, XQuery and SPARQL to allow lifting and lowering between XML and RDF formats in a single language.
This document provides an overview of querying linked data using SPARQL. It begins with an introduction and motivation for querying linked data. It then covers the basics of SPARQL including its components like prefixes, query forms, and solution modifiers. Several examples are provided demonstrating how to construct ASK, SELECT, and other types of SPARQL queries. The document also discusses SPARQL algebra and updating linked data with SPARQL 1.1.
This document provides an overview of SPARQL 1.0, the W3C recommendation for querying RDF data. It describes the main components of SPARQL queries including graph patterns used to match subgraphs, basic graph patterns using triple patterns, and optional, union, and constraint graph patterns. It provides examples of SPARQL queries and describes how variables, blank nodes, and filter expressions are used in constraints on query solutions.
The document discusses representing data in the Resource Description Framework (RDF). It describes how relational data can be represented as RDF triples with rows becoming subjects, columns becoming properties, and values becoming objects. It also discusses using URIs instead of internal IDs and names to allow data integration. The document then covers serializing RDF data in different formats like RDF/XML, N-Triples, N3, and Turtle and describes syntax for representing literals, language tags, and abbreviating subject and predicate pairs.
The Semantic Web #9 - Web Ontology Language (OWL)Myungjin Lee
This is a lecture note #9 for my class of Graduate School of Yonsei University, Korea.
It describes Web Ontology Language (OWL) for authoring ontologies.
SPARQL 1.1 introduced several new features including:
- Updated versions of the SPARQL Query and Protocol specifications
- A SPARQL Update language for modifying RDF graphs
- A protocol for managing RDF graphs over HTTP
- Service descriptions for describing SPARQL endpoints
- Basic federated query capabilities
- Other minor features and extensions
The document discusses the RDF data model. The key points are:
1. RDF represents data as a graph of triples consisting of a subject, predicate, and object. Triples can be combined to form an RDF graph.
2. The RDF data model has three types of nodes - URIs to identify resources, blank nodes to represent anonymous resources, and literals for values like text strings.
3. RDF graphs can be merged to integrate data from multiple sources in an automatic way due to RDF's compositional nature.
Two graph data models : RDF and Property Graphsandyseaborne
This document provides an overview of two graph data models: RDF and Property Graphs. It describes the key components of each model, including triples for RDF and nodes/edges/properties for Property Graphs. It also discusses Apache projects that work with each model like Apache Jena for RDF and Apache TinkerPop, Spark, Giraph and Flink for Property Graphs. Finally, it notes that while the models have different focuses, they could potentially share technologies like storage and query capabilities.
Bernhard Haslhofer is a postdoc researcher at Cornell University studying linked data, user-contributed data, and data interoperability. He discusses Linked (Open) Data, which uses URIs and RDF to publish and link structured data on the web. The key principles are using URIs to identify things, providing useful information about those URIs when dereferenced, and including links to other URIs. Enabling technologies include URIs, RDF, RDFS/OWL for vocabularies, SPARQL for querying, and best practices for publishing vocabularies and data. Useful tools are also presented.
What is the fuzz on triple stores? Will triple stores eventually replace relational databases? This talk looks at the big picture, explains the technology and tries to look at the road ahead.
SPARQL is a standard query language for RDF that has undergone two iterations (1.0 and 1.1) through the W3C process. SPARQL 1.1 includes updates to RDF stores, subqueries, aggregation, property paths, negation, and remote querying. It also defines separate specifications for querying, updating, protocols, graph store protocols, and federated querying. Apache Jena provides implementations of SPARQL 1.1 and tools like Fuseki for deploying SPARQL servers.
Lightening talk for Semantic Web in Libraries (SWIB13) conference at 2013-11-27 about another method of expressing RDF data. See http://gbv.github.io/aREF/ for a preliminary specification.
This document provides an overview of a training course on RDF, SPARQL and semantic repositories. The training course took place in August 2010 in Montreal as part of the 3rd GATE training course. The document outlines the modules covered in the course, including introductions to RDF/S and OWL semantics, querying RDF data with SPARQL, semantic repositories and benchmarking triplestores.
- SPARQL is a query language for retrieving and manipulating data stored in RDF format. It is similar to SQL but for RDF data.
- SPARQL queries contain prefix declarations, specify a dataset using FROM, and include a graph pattern in the WHERE clause to match triples.
- The main types of SPARQL queries are SELECT, ASK, DESCRIBE, and CONSTRUCT. SELECT returns variable bindings, ASK returns a boolean, DESCRIBE returns a description of a resource, and CONSTRUCT generates an RDF graph.
This document discusses programming with Linked Open Data (LOD) using the Ruby programming language. It provides an overview of LOD principles and demonstrates how to read, write, load, merge and query RDF data using the RDF.rb library in Ruby. Code examples are provided to illustrate how to retrieve and inspect RDF statements from DBpedia, serialize and write RDF in different formats, load RDF graphs from multiple sources, and perform basic SPARQL queries.
This document summarizes SPARQL, the SPARQL query language used for querying and retrieving data stored in RDF format. It discusses key concepts such as RDF, terms, syntax, patterns, and constraints. RDF represents information as subject-predicate-object triples that can be queried using SPARQL. SPARQL allows constructing basic and complex graph patterns to match against the RDF graph. It also supports value filters, ordering, pagination and other solution modifiers. The document provides examples of SPARQL queries to retrieve data from RDF graphs based on different conditions and constraints.
The document discusses using RDFS and OWL reasoning to integrate heterogeneous linked data by addressing issues like terminology and naming heterogeneity. It presents an approach using a subset of OWL 2 RL rules to reason over a billion triple corpus in a scalable way, handling the TBox separately from the ABox to avoid quadratic inferences. It also describes augmenting the reasoning with annotations to track trustworthiness and using this to filter inferences, detect inconsistencies and perform a light repair of the data. Consolidation is discussed as rewriting URIs to canonical identifiers based on owl:sameAs relations. Performance results show the different techniques taking between 1-20 hours to run over the corpus distributed across 9 machines.
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: />.
This document provides an introduction to XSPARQL, a language for transforming between RDF and XML. It discusses how transformations between RDF and XML can be challenging due to different syntaxes and serializations used to represent the same RDF graph. It notes that while SPARQL is good for querying RDF, it does not provide a way to produce arbitrary XML output. The document then introduces XSPARQL as a transformation language that combines XML, RDF, XQuery and SPARQL to allow lifting and lowering between XML and RDF formats in a single language.
This document provides an overview of querying linked data using SPARQL. It begins with an introduction and motivation for querying linked data. It then covers the basics of SPARQL including its components like prefixes, query forms, and solution modifiers. Several examples are provided demonstrating how to construct ASK, SELECT, and other types of SPARQL queries. The document also discusses SPARQL algebra and updating linked data with SPARQL 1.1.
This document provides an overview of SPARQL 1.0, the W3C recommendation for querying RDF data. It describes the main components of SPARQL queries including graph patterns used to match subgraphs, basic graph patterns using triple patterns, and optional, union, and constraint graph patterns. It provides examples of SPARQL queries and describes how variables, blank nodes, and filter expressions are used in constraints on query solutions.
The document discusses representing data in the Resource Description Framework (RDF). It describes how relational data can be represented as RDF triples with rows becoming subjects, columns becoming properties, and values becoming objects. It also discusses using URIs instead of internal IDs and names to allow data integration. The document then covers serializing RDF data in different formats like RDF/XML, N-Triples, N3, and Turtle and describes syntax for representing literals, language tags, and abbreviating subject and predicate pairs.
The Semantic Web #9 - Web Ontology Language (OWL)Myungjin Lee
This is a lecture note #9 for my class of Graduate School of Yonsei University, Korea.
It describes Web Ontology Language (OWL) for authoring ontologies.
SPARQL 1.1 introduced several new features including:
- Updated versions of the SPARQL Query and Protocol specifications
- A SPARQL Update language for modifying RDF graphs
- A protocol for managing RDF graphs over HTTP
- Service descriptions for describing SPARQL endpoints
- Basic federated query capabilities
- Other minor features and extensions
The document discusses the RDF data model. The key points are:
1. RDF represents data as a graph of triples consisting of a subject, predicate, and object. Triples can be combined to form an RDF graph.
2. The RDF data model has three types of nodes - URIs to identify resources, blank nodes to represent anonymous resources, and literals for values like text strings.
3. RDF graphs can be merged to integrate data from multiple sources in an automatic way due to RDF's compositional nature.
Two graph data models : RDF and Property Graphsandyseaborne
This document provides an overview of two graph data models: RDF and Property Graphs. It describes the key components of each model, including triples for RDF and nodes/edges/properties for Property Graphs. It also discusses Apache projects that work with each model like Apache Jena for RDF and Apache TinkerPop, Spark, Giraph and Flink for Property Graphs. Finally, it notes that while the models have different focuses, they could potentially share technologies like storage and query capabilities.
Bernhard Haslhofer is a postdoc researcher at Cornell University studying linked data, user-contributed data, and data interoperability. He discusses Linked (Open) Data, which uses URIs and RDF to publish and link structured data on the web. The key principles are using URIs to identify things, providing useful information about those URIs when dereferenced, and including links to other URIs. Enabling technologies include URIs, RDF, RDFS/OWL for vocabularies, SPARQL for querying, and best practices for publishing vocabularies and data. Useful tools are also presented.
What is the fuzz on triple stores? Will triple stores eventually replace relational databases? This talk looks at the big picture, explains the technology and tries to look at the road ahead.
SPARQL is a standard query language for RDF that has undergone two iterations (1.0 and 1.1) through the W3C process. SPARQL 1.1 includes updates to RDF stores, subqueries, aggregation, property paths, negation, and remote querying. It also defines separate specifications for querying, updating, protocols, graph store protocols, and federated querying. Apache Jena provides implementations of SPARQL 1.1 and tools like Fuseki for deploying SPARQL servers.
Lightening talk for Semantic Web in Libraries (SWIB13) conference at 2013-11-27 about another method of expressing RDF data. See http://gbv.github.io/aREF/ for a preliminary specification.
This document provides an overview of a training course on RDF, SPARQL and semantic repositories. The training course took place in August 2010 in Montreal as part of the 3rd GATE training course. The document outlines the modules covered in the course, including introductions to RDF/S and OWL semantics, querying RDF data with SPARQL, semantic repositories and benchmarking triplestores.
- SPARQL is a query language for retrieving and manipulating data stored in RDF format. It is similar to SQL but for RDF data.
- SPARQL queries contain prefix declarations, specify a dataset using FROM, and include a graph pattern in the WHERE clause to match triples.
- The main types of SPARQL queries are SELECT, ASK, DESCRIBE, and CONSTRUCT. SELECT returns variable bindings, ASK returns a boolean, DESCRIBE returns a description of a resource, and CONSTRUCT generates an RDF graph.
This document discusses programming with Linked Open Data (LOD) using the Ruby programming language. It provides an overview of LOD principles and demonstrates how to read, write, load, merge and query RDF data using the RDF.rb library in Ruby. Code examples are provided to illustrate how to retrieve and inspect RDF statements from DBpedia, serialize and write RDF in different formats, load RDF graphs from multiple sources, and perform basic SPARQL queries.
This document summarizes SPARQL, the SPARQL query language used for querying and retrieving data stored in RDF format. It discusses key concepts such as RDF, terms, syntax, patterns, and constraints. RDF represents information as subject-predicate-object triples that can be queried using SPARQL. SPARQL allows constructing basic and complex graph patterns to match against the RDF graph. It also supports value filters, ordering, pagination and other solution modifiers. The document provides examples of SPARQL queries to retrieve data from RDF graphs based on different conditions and constraints.
The document discusses using RDFS and OWL reasoning to integrate heterogeneous linked data by addressing issues like terminology and naming heterogeneity. It presents an approach using a subset of OWL 2 RL rules to reason over a billion triple corpus in a scalable way, handling the TBox separately from the ABox to avoid quadratic inferences. It also describes augmenting the reasoning with annotations to track trustworthiness and using this to filter inferences, detect inconsistencies and perform a light repair of the data. Consolidation is discussed as rewriting URIs to canonical identifiers based on owl:sameAs relations. Performance results show the different techniques taking between 1-20 hours to run over the corpus distributed across 9 machines.
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: />.
Federated data stores using semantic web technologySteve Ray
Semantic web, or linked data technology can help address interoperability problems in the internet, and particularly in support of the Internet of Things. This is an simple introduction to this technology.
Presentation of the paper "Reasoning in the OWL 2 Full Ontology Language using First-Order Automated Theorem Proving" by Michael Schneider, FZI Karlsruhe, and Geoff Sutcliffe, University of Miami, at the 23rd International Conference on Automated Deduction (CADE 23), August 2011.
This document provides an overview of semantic web technologies for publishing data. It introduces the semantic web and describes semantic web languages like RDF, RDF Schema, and OWL. These languages allow modeling data as graphs and defining ontologies to provide unambiguous meaning to information. The document discusses using these languages to publish structured data on the web in ways that enable semantic annotation, integration, and reasoning across interconnected data sources.
This document provides an introduction to the Resource Description Framework (RDF). RDF is a framework for describing resources on the web using Uniform Resource Identifiers (URIs) and properties. It represents data as a directed labeled graph consisting of triples with a subject, predicate, and object. Examples are provided to demonstrate how RDF can be used to describe resources and their properties. Key concepts explained include URIs, triples, and representing RDF data as a graph.
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 the Web Ontology Language (OWL). It discusses the requirements for ontology languages, the three species of OWL (Lite, DL, Full), the syntactic forms of OWL, and key elements of OWL including classes, properties, restrictions, and boolean combinations. It also covers special properties, datatypes, and versioning information. OWL builds on RDF and RDF Schema to provide a stronger language for defining ontologies with greater machine interpretability on the semantic web.
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.
Linking Open, Big Data Using Semantic Web Technologies - An IntroductionRonald Ashri
The Physics Department of the University of Cagliari and the Linkalab Group invited me to talk about the Semantic Web and Linked Data - this is simply an introduction to the technologies involved.
This document discusses RDFS (Resource Description Framework Schema), which is a standard ontology language for the Semantic Web. RDFS introduces predefined meanings for resources through axioms and allows for basic inferences over RDF data through mechanisms like type propagation between classes and relationships. The document provides examples of how RDFS can be used to classify resources in an RDF graph and automatically infer additional types and relationships through the use of RDFS properties like rdfs:subClassOf and rdfs:domain.
This document discusses RDFS (Resource Description Framework Schema), which is a standard ontology language for the Semantic Web. RDFS introduces predefined meanings for resources through axioms and allows for basic inferences over RDF data through mechanisms like type propagation between classes and properties. The document provides examples of how RDFS can be used to classify resources in an RDF graph and automatically infer additional types for resources based on their properties and class memberships.
Piloting Linked Data to Connect Library and Archive Resources to the New Worl...Laura Akerman
Presentation for the CNI (Coalition for Networked Information) Fall Forum, December 2012. Describes Emory University Library’s first-hand experience in interlinking Civil War-related materials and other online resources by leveraging open linked data principles. The library has been actively evaluating linked data’s potential to replace current library processes and services (bibliographic services, finding aids, cataloging, and metadata work) as a more efficient and sustainable means, and one that could bring greater benefit to end users for research and learning. The Library’s initial focus was on workforce education and hands-on learning through real-time experiments: the Connections project was begun to prepare staff to work with linked data, a process that has culminated in a 3-month hands-on pilot to build and convert some data. The pilot introduced the concept to a wide range of staff, including subject liaisons, archivists, metadata librarians, and programmers. Emory’s “silos” of data were interlinked with other open data sources as a way to enhance user discovery and use of library materials on a very limited scale.
This document provides an overview of linked data and the SPARQL query language. It defines linked data as a method of publishing structured data on the web so that it can be interlinked and queried. The key aspects covered include linked data principles of using URIs to identify things and including links to other related data. SPARQL is introduced as the query language for retrieving and manipulating linked data.
Semantic Technologies and Triplestores for Business IntelligenceMarin Dimitrov
This document provides an introduction to semantic technologies and triplestores. It discusses the Semantic Web vision of making data on the web more accessible and linked. Key concepts covered include RDF, ontologies, OWL, SPARQL and Linked Data. It also introduces triplestores as RDF databases for storing and querying semantic data and compares their features to traditional databases.
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.
This document provides an overview of the Web Ontology Language (OWL). OWL is built on top of RDF and is used to process information on the web by computers. It allows for stronger constraints and rules than RDF. There are three sublanguages of OWL with varying expressiveness. OWL is written in XML and is a W3C standard, making it suitable for exchanging and processing web information across different systems.
Similar to OWL: Yet to arrive on the Web of Data? (20)
How to Make a Field Mandatory in Odoo 17Celine George
In Odoo, making a field required can be done through both Python code and XML views. When you set the required attribute to True in Python code, it makes the field required across all views where it's used. Conversely, when you set the required attribute in XML views, it makes the field required only in the context of that particular view.
বাংলাদেশের অর্থনৈতিক সমীক্ষা ২০২৪ [Bangladesh Economic Review 2024 Bangla.pdf] কম্পিউটার , ট্যাব ও স্মার্ট ফোন ভার্সন সহ সম্পূর্ণ বাংলা ই-বুক বা pdf বই " সুচিপত্র ...বুকমার্ক মেনু 🔖 ও হাইপার লিংক মেনু 📝👆 যুক্ত ..
আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
বিসিএস ও ব্যাংক এর লিখিত পরীক্ষা ...+এছাড়া মাধ্যমিক ও উচ্চমাধ্যমিকের স্টুডেন্টদের জন্য অনেক কাজে আসবে ...
How to Build a Module in Odoo 17 Using the Scaffold MethodCeline George
Odoo provides an option for creating a module by using a single line command. By using this command the user can make a whole structure of a module. It is very easy for a beginner to make a module. There is no need to make each file manually. This slide will show how to create a module using the scaffold method.
Main Java[All of the Base Concepts}.docxadhitya5119
This is part 1 of my Java Learning Journey. This Contains Custom methods, classes, constructors, packages, multithreading , try- catch block, finally block and more.
How to Add Chatter in the odoo 17 ERP ModuleCeline George
In Odoo, the chatter is like a chat tool that helps you work together on records. You can leave notes and track things, making it easier to talk with your team and partners. Inside chatter, all communication history, activity, and changes will be displayed.
This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
How to Setup Warehouse & Location in Odoo 17 InventoryCeline George
In this slide, we'll explore how to set up warehouses and locations in Odoo 17 Inventory. This will help us manage our stock effectively, track inventory levels, and streamline warehouse operations.
The simplified electron and muon model, Oscillating Spacetime: The Foundation...RitikBhardwaj56
Discover the Simplified Electron and Muon Model: A New Wave-Based Approach to Understanding Particles delves into a groundbreaking theory that presents electrons and muons as rotating soliton waves within oscillating spacetime. Geared towards students, researchers, and science buffs, this book breaks down complex ideas into simple explanations. It covers topics such as electron waves, temporal dynamics, and the implications of this model on particle physics. With clear illustrations and easy-to-follow explanations, readers will gain a new outlook on the universe's fundamental nature.
Executive Directors Chat Leveraging AI for Diversity, Equity, and InclusionTechSoup
Let’s explore the intersection of technology and equity in the final session of our DEI series. Discover how AI tools, like ChatGPT, can be used to support and enhance your nonprofit's DEI initiatives. Participants will gain insights into practical AI applications and get tips for leveraging technology to advance their DEI goals.
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.
7. Linked Data, RDFS and OWL: Linked Vocabularies
…
…
Image from http://blog.dbtune.org/public/.081005_lod_constellation_m.jpg:; Giasson, Bergman
7
8. Naming in Linked Data: Linked Resources
Tim Berners-Lee: URIs dblp:100007
timbl:i
db:Tim-Berners_Lee
identica:45563
= owl:sameAs
fb:en.tim_berners-lee adv:timbl
…
8
9. Need (some) OWL reasoning…
mo:myspace
foaf:primaryTopic foaf:page
foaf:topic
doap:homepage
foaf:homepage
Gimme webpages foaf:isPrimaryTopicOf
relating to
Tim Berners-Lee
identica:45563
adv:timbl db:Tim-Berners_Lee
dblp:100007
fb:en.tim_berners-lee
timbl:i
timbl:i foaf:page ?pages .
... 7 x 6 = 42 possible patterns
9
10. …BUT OWL IS HARD
(…to learn, to understand, to implement, to compute, to teach,
to represent in RDF, to publish, to parse, to use appropriately...)
10 19.04.2012
12. So what OWL is used out there?
Looked at Billion Triple Challenge 2011 Dataset
2.1 billion quadruples, crawled from…
7.4 million RDF/XML documents, covering…
791 (pay-level) domains
Count OWL features used in the dataset:
Per use
Per document
Per domain
Can be skewed by data
Ranked OWL features using PageRank:
Rank documents based on dereferenceable links
For each OWL feature, sum the rank of documents using it
Intuition: Approximates probability of encountering an OWL feature
during a random walk of the data
12 19.04.2012
14. Results of ranking (see paper for all details)
…
16 owl:sameAs 7.29E-2 N.B.:
17 owl:equivalentClass 5.24E-2 owl:sameAs used
18 owl:InverseFunctionalProperty 4.79E-2 frequently, but not
19 owl:unionOf 3.15E-2 by highly ranked
vocabularies
20 owl:SymmetricProperty 3.13E-2
21 owl:TransitiveProperty 2.98E-2
22 owl:someValuesFrom 2.13E-2
23 rdf:_* 1.42E-2
24 owl:allValuesFrom 2.98E-3
25 owl:minCardinality 2.43E-3
26 owl:maxCardinality 2.14E-3
27 owl:cardinality 1.75E-3
28 owl:oneOf 4.13E-4
29 owl:hasValue 3.91E-4
30 owl:intersectionOf 3.37E-4
31 owl:NamedIndividual 3.37E-4
14 19.04.2012
15. Observations?
RDFS features amongst the most prominently used
OWL 2 features not yet used prominently
RDF | RDFS | OWL | OWL 2
x-axis is log-scale!
15 19.04.2012
16. Observations?
(OWL) Features expressed with a single RDF triple are most prominent
Roughly speaking, features not requiring blank nodes
e.g., sub-class/-property, inverse-of, equivalent property/class, sameas, domain/range, disjoint with, etc.
Not those requiring lists or n-ary predicate in RDF mapping
e.g., union, intersection, cardinalities, all-disjoint, some/all/has-value restrictions, hasKey, pCAs, etc.
Single Triple (No BNodes) [OWL 2 Single Triple] | Multi-Triple (Needs BNodes)
x-axis is log-scale!
16 19.04.2012
17. Datatype Analysis?
dateTime, boolean, integer, string, date, long, anyURI, int,
float, gYear in top ten (resp.)
Various OWL2 datatypes not used at all
Some sites use custom (but undefined) datatypes
See paper for details!
17 19.04.2012
19. Tool support?
OWL Libraries for parsing, etc., not much choice:
OWL API, Protegé, Jena … (all heavyweight Java libs)
Has to do with multi-triple axioms!
Query engines with reasoning support
Many support non-standard profiles or partially support a profile
Most use rule-based engines
Datatype support rarely complete (mostly by canonicalization)
19 19.04.2012
20. SO WHAT ABOUT THAT LITTLE OWL?
…that’s sufficient for current Linked Data trends.
20 19.04.2012
21. Introducing OWL LD (Linked Data)
Define a new sub-profile of OWL RL that includes only those features
expressible as a single triple:
Easy to parse / process tuple-at-a-time ✔
Easy to publish ✔
Easy to query ✔✔
Easy to validate / check well-formedness ✔✔
Covers most prominently used features! ✔✔✔
Miss some features, e.g., owl:unionOf(18) ✘(✘✘?)
OWL LD similar to other profiles (motivated from other perspectives)
RDFS-Plus(/RDFS 3.0): Allemang & Hendler. Semantic Web for the Working Ontologist.
L2: Fischer, Ünel, Bishop, Fensel. Towards a scalable, pragmatic knowledge representation
language for the web. In Ershov Memorial Conf., pages 124–134, 2009.
21 19.04.2012
22. Introducing OWL LD (Linked Data)
Define a subset of OWL 2 RL/RDF rules that apply for these
features and that can be applied for any RDF graph
(see paper for full rule-list; includes 47 rules)
http://semanticweb.org/OWLLD/
Define a subset of OWL 2 RL grammar such that a conformant
ontology/vocabulary can optionally be interpreted under Direct
Semantics (and thus tools like PelletDB, QuOnTo, DLEJena, Protegé,
HermiT, Racer, etc.)
http://semanticweb.org/OWLLD/
22 19.04.2012
24. Has OWL arrived on the Web of Data?
Partially…
RDFS features still most prominent
Many OWL features are prominently used
OWL 2 features currently not well adopted
Practical tool support emerging, but for different profiles of OWL
http://events.linkeddata.org/ldow2012/papers/ldow2012-paper-16.pdf
How about a new OWL profile for Linked Data?
OWL LD:
Single-triple expressible features only
Prominently used; easier to support
Rules defined as a subset of OWL 2 RL/RDF
Grammar defined (if needed for OWL Direct Semantics)
Similar to RDFS-Plus, RDFS 3.0 and L2
(but motivated here based on an empirical survey)
http://semanticweb.org/OWLLD/
24 19.04.2012