The document discusses two graph data models: RDF and Property Graphs. It provides an overview of each model, including examples and technologies used to access and query each type of graph. The key conclusions are that RDF emphasizes information modeling and knowledge graphs, while Property Graphs emphasize data syntax and graph algorithms. Simply layering the data models on top of each other leads to dissatisfaction, but the models could potentially share technologies while still keeping their separate focuses and tools.
SPARQL is a query language for retrieving and manipulating data stored in RDF format. It allows users to write queries against remote SPARQL endpoints to query RDF triples stored in a database. SPARQL queries are composed of triple patterns, similar to RDF triples, that can include variables to retrieve variable bindings from the queried data. Query results are returned as solutions that assign values to the variables. Common queries include SELECT, ASK, CONSTRUCT, and DESCRIBE. SPARQL endpoints provide programmatic access to issue SPARQL queries against remote SPARQL-accessible stores.
The document discusses using JSON-LD and RDF to add semantic meaning to web APIs while maintaining compatibility with existing JSON formats. It explains how RDF uses triples to make statements about resources, and how JSON-LD allows embedding RDF semantics in JSON without changing the format. This allows merging data from multiple sources and facilitates data interchange and evolution of schemas over time.
2011 4IZ440 Semantic Web – RDF, SPARQL, and software APIsJosef Petrák
The document discusses the Semantic Web and RDF data formats. It provides an overview of RDF syntaxes like RDF/XML, N3, N-Triples, RDF/JSON, and RDFa. It also discusses software APIs for working with RDF data in languages like Java, PHP, and Ruby. The document outlines handling RDF data using statement-centric, resource-centric, and ontology-centric models, as well as named graphs. It provides examples of reading RDF data from files and querying RDF data using SPARQL.
Semantic Web technologies (such as RDF and SPARQL) excel at bringing together diverse data in a world of independent data publishers and consumers. Common ontologies help to arrive at a shared understanding of the intended meaning of data.
However, they don’t address one critically important issue: What does it mean for data to be complete and/or valid? Semantic knowledge graphs without a shared notion of completeness and validity quickly turn into a Big Ball of Data Mud.
The Shapes Constraint Language (SHACL), an upcoming W3C standard, promises to help solve this problem. By keeping semantics separate from validity, SHACL makes it possible to resolve a slew of data quality and data exchange issues.
Presented at the Lotico Berlin Semantic Web Meetup.
ShEx is a language for validating RDF data. It allows defining shapes that specify constraints on nodes and triples. ShEx expressions can be used to validate if RDF graphs conform to the defined shapes. The ShEx language is inspired by languages like RelaxNG and provides different serialization formats like ShExC, ShExJ, and ShExR. There are open-source implementations of ShEx validators in languages like JavaScript, Scala, Ruby, Python, and Java. ShEx provides a concise way to define RDF shapes and validate instance data against those shapes.
Although RDF is a corner stone of semantic web and knowledge graphs, it has not been embraced by everyday programmers and software architects who need to safely create and access well-structured data. There is a lack of common tools and methodologies that are available in more conventional settings to improve data quality by defining schemas that can later be validated. Two technologies have recently been proposed for RDF validation: Shape Expressions (ShEx) and Shapes Constraint Language (SHACL). In the talk, we will review the history and motivation of both technologies. We will also and enumerate some challenges and future work with regards to RDF validation.
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.
FedX - Optimization Techniques for Federated Query Processing on Linked Dataaschwarte
The final slides of our talk about FedX at the 10th International Semantic Web Conference in Bonn. For details about FedX see http://www.fluidops.com/fedx/
SPIN is a vocabulary that represents SPARQL queries and constraints as RDF triples. This allows SPARQL queries to be stored and shared on the semantic web. SPIN can be used to define SPARQL constraints, rules, functions and reusable query templates. Storing SPARQL queries as RDF triples provides benefits like referential integrity, managing namespaces centrally, and facilitating the easy sharing of queries on the semantic web.
SPARQL is a standardized query language for retrieving and manipulating data stored in RDF format. It was created by the RDF Data Access Working Group to provide querying of RDF stores. SPARQL supports four query forms: SELECT, CONSTRUCT, DESCRIBE, and ASK. It also defines a protocol for executing queries over HTTP. SPARQL has become a key technology for working with semantic data on the web.
A followup on our 2011 presentation on the new Linked Open Digital Library, discussing how we are creating a digital library centered around LInked Open Data. Include details on how we are creating a dataset of botanists and their publications that is to be shared as linked open data.
SPARQL1.1 Tutorial, given in UChile by Axel Polleres (DERI)net2-project
This document provides an introduction to SPARQL 1.1. It begins by explaining that SPARQL is a query language for the semantic web that allows users to query RDF data stores similarly to how SQL queries relational databases. It then describes SPARQL 1.0, the initial standard version, and the new features being added in SPARQL 1.1, including aggregate functions, subqueries, property paths and federated querying. The document concludes by discussing SPARQL implementations and the status of the 1.1 specification.
Federated SPARQL query processing over the Web of DataMuhammad Saleem
The document discusses approaches for federating SPARQL queries over the web of data. It describes SPARQL endpoint federation, linked data federation, and distributed hash tables approaches. It also discusses techniques for optimizing query federation, including query rewriting, source selection, join order selection, and join implementations. Source selection algorithms discussed include index-free using SPARQL ASK queries, index-only using data summaries, and hybrid approaches.
Compare and contrast RDF triple stores and NoSQL: are triples stores NoSQL or not?
Talk given 2011-09-08 tot he BigData/NoSQL meetup at Bristol University.
The document summarizes the key changes and additions in the RDF 1.1 specification, including:
1) Support for named graphs, also known as RDF datasets or quads, to represent multiple RDF graphs each with a unique name;
2) Additional datatypes like durations and date/time stamps;
3) JSON-LD as a new syntax for serializing RDF in JSON; and
4) Some controversial proposals like deprecating features and allowing literals as subjects that did not make the final specification.
SPARQL is a query language for retrieving and manipulating data stored in RDF format. It is a W3C recommendation similar to SQL for relational databases. SPARQL queries contain SELECT, FROM and WHERE clauses to identify result variables, specify the RDF dataset, and provide a basic graph pattern to match against the data. SPARQL can be used to query RDF knowledge bases and retrieve variable bindings or boolean results. Query results are returned in XML format according to the SPARQL Query Results specification.
This document contains slides from a presentation by Pedro Szekely on RDF and related Semantic Web topics. The slides cover Unicode, URLs, URIs, namespaces, XML, XML Schema, RDF graphs, RDF syntaxes including XML and Turtle formats, and comparisons between XML and RDF. Key topics include using URIs to identify resources on the web, representing information as subject-predicate-object triples in RDF graphs, combining vocabularies using namespaces, and leveraging XML tools while making RDF more human-readable.
An introduction to Semantic Web and Linked DataFabien Gandon
Here are the steps to answer this SPARQL query against the given RDF base:
1. The query asks for all ?name values where there is a triple with predicate "name" and another triple with the same subject and predicate "email".
2. In the base, _:b is the only resource that has both a "name" and "email" triple.
3. _:b has the name "Thomas".
Therefore, the only result of the query is ?name = "Thomas".
So the result of the SPARQL query is:
?name
"Thomas"
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.
Talk about Exploring the Semantic Web, and particularly Linked Data, and the Rhizomer approach. Presented August 14th 2012 at the SRI AIC Seminar Series, Menlo Park, CA
This document summarizes Rodrigo Dias Arruda Senra's 2012 doctoral thesis defense at the University of Campinas. The thesis studied how to organize digital information for sharing across heterogeneous systems and proposed three main contributions: 1) SciFrame, a conceptual framework for scientific digital data processing; 2) database descriptors to enable loose coupling between applications and database management systems; and 3) organographs, a method for explicitly organizing information based on tasks.
The document provides an introduction to Semantic Web and Linked Data. It discusses key concepts such as RDF, which represents data as subject-predicate-object triples that can be connected to form a graph. RDF has several syntaxes including XML, Turtle, and JSON. Properties in RDF triples can link to other resources or contain literal values. Types are identified with URIs and vocabularies are designed to be extensible. The goal of Linked Data is to publish structured data on the web and link it to other data to form a global data web.
This query will not return any results. The pattern specified in the WHERE clause contains two triples, but the second triple contains a syntax error - it is missing the property between ?x and ?ema. A valid property like email would need to be specified, such as:
SELECT ?name WHERE {
?x name ?name .
?x email ?email
}
This query will select and return the ?name of any resources ?x that have both a name and email property specified.
W3C Tutorial on Semantic Web and Linked Data at WWW 2013Fabien Gandon
The document provides an introduction to Semantic Web and Linked Data. It discusses key concepts such as RDF, which represents data as subject-predicate-object triples that can be connected to form a graph. RDF has several syntaxes including XML, Turtle, and JSON. Properties in RDF triples can link to other resources or contain literal values. Types are identified with URIs and vocabularies are extensible. The goal of Linked Data is to publish structured data on the web and link it to other data to form a global data web.
This document provides an overview of semantic web technologies including the Resource Description Framework (RDF), which is a standard model for data interchange on the web. It describes RDF concepts like triples, graphs, and syntaxes like RDF/XML and Turtle. It also covers ontologies, SPARQL for querying RDF data, linked data, and tools for working with RDF and semantic web technologies.
- 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.
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
SPARQL is a standard query language for retrieving and manipulating data stored in RDF format. It consists of three parts: a query language, a result format, and an access protocol. The query language uses graph patterns to match against RDF graphs. It supports keywords like SELECT, FROM, and WHERE to identify values to return, data sources, and triple patterns to match. SPARQL can be run over HTTP or SOAP and returns XML results. It provides a unified method for querying RDF data distributed across the web.
The document discusses several options for publishing data on the Semantic Web. It describes Linked Data as the preferred approach, which involves using URIs to identify things and including links between related data to improve discovery. It also outlines publishing metadata in HTML documents using standards like RDFa and Microdata, as well as exposing SPARQL endpoints and data feeds.
This document introduces SPARQL, the SPARQL query language used to retrieve and manipulate RDF data. It provides an example SPARQL query to return full names from a sample RDF graph. It then describes what a SPARQL Service Description is, which is a vocabulary for discovering and describing SPARQL services and endpoints. It outlines several properties and classes used in SPARQL Service Descriptions.
RDF and linked data standards allow for layering and linking of information on the web. There is a large and growing amount of RDF data available from sources like Wikipedia, Flickr, government data sets, and more. Standards like RDF, RDFS, OWL, SKOS, and SPARQL enable publishing, linking, querying and reusing this structured data on the web in a way that is machine-readable. Integrating RDF and linked data into systems like Drupal could provide benefits like improved searchability, cross-linking of content, and reuse of external taxonomies and metadata schemas.
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.
NEW LAUNCH! How to build graph applications with SPARQL and Gremlin using Ama...Amazon Web Services
In this session, we will demonstrate how you can easily start using graph databases to solve your business problems. We will demonstrate setting up a Neptune instance, loading the dataset and using Gremlin and SPARQL via Java to build a application. We will also cover scaling, availability and administrative aspects of the Neptune service.
Introduction to Apache Flink - Fast and reliable big data processingTill Rohrmann
This presentation introduces Apache Flink, a massively parallel data processing engine which currently undergoes the incubation process at the Apache Software Foundation. Flink's programming primitives are presented and it is shown how easily a distributed PageRank algorithm can be implemented with Flink. Intriguing features such as dedicated memory management, Hadoop compatibility, streaming and automatic optimisation make it an unique system in the world of Big Data processing.
Unlocking value with event-driven architecture by Confluentconfluent
Sfrutta il potere dello streaming di dati in tempo reale e dei microservizi basati su eventi per il futuro di Sky con Confluent e Kafka®.
In questo tech talk esploreremo le potenzialità di Confluent e Apache Kafka® per rivoluzionare l'architettura aziendale e sbloccare nuove opportunità di business. Ne approfondiremo i concetti chiave, guidandoti nella creazione di applicazioni scalabili, resilienti e fruibili in tempo reale per lo streaming di dati.
Scoprirai come costruire microservizi basati su eventi con Confluent, sfruttando i vantaggi di un'architettura moderna e reattiva.
Il talk presenterà inoltre casi d'uso reali di Confluent e Kafka®, dimostrando come queste tecnologie possano ottimizzare i processi aziendali e generare valore concreto.
InflectraCON 360: Risk-Based Testing for Mission Critical SystemsInflectra
Mission-critical systems demanded unwavering reliability. But in the face of tight deadlines and limited resources, how did Quality Engineering teams ensure comprehensive testing without sacrificing speed?
In this webinar, experts Adam Sandman and Ben Johnson-Ward delved into the world of risk-based testing, sharing strategies honed from their experience with high-stakes software. Attendees discovered how to prioritize testing efforts, targeting areas most likely to harbor critical bugs and prevent catastrophic failures.
Key takeaways:
• Strategic prioritization: The webinar explored how to identify and focus on the most critical components of complex systems.
• Streamlined testing: Experts shared insights on optimizing test generation to maximize coverage while minimizing wasted effort.
• Continuous improvement: The session covered how to integrate risk assessment throughout the development lifecycle for ongoing quality assurance.
• Real-world insights: Attendees gained valuable lessons from industries where software failures had life-or-death consequences.
Whether participants are developing medical devices, financial software, or any other mission-critical application, this webinar equipped them with the knowledge and tools to build quality into every step of their process.
A Step-by-Step Guide to Selecting the Right Automated Software Testing Tools.pdfkalichargn70th171
Software testing is highly essential in the software development lifecycle. Selecting the appropriate testing tool is pivotal for effective test automation and project success. As technology advances, the demands of the software market escalate, pushing industry players to deliver high-quality products swiftly through agile methodologies.
Overview: How To Fill Timesheet In TaskSprint?
Ever feel like time is running fast and slipping through your fingers? Yes, we have all experienced it. You put your nose to the grindstone for a project and deal with tasks and deadlines as if they were easy hurdles. But when it is time to complete a timesheet, you find yourself at sea about the amount of time each project consumes. But fear not, fellow soldier, in the battle against time! TaskSprint, your reliable sidekick in project management, offers an in-built timesheet feature to make tracking your hours seem like a walk in the park.
This is a detailed guide that will lead you in such a way that you will become familiar with how to fill the timesheet. We'll show you how to navigate the interface, easily add entries, and ensure your project manager understands your valuable work hours.
So, ditch the guesswork and embrace precise time tracking. Get ready to transform your timesheet woes into a streamlined, efficient process. Let's dive in and learn how to fill timesheets.
Get to know Autonomous 2.0, the latest innovation from Applitools, in this sneak peek session showcasing how our AI-powered testing solutions revolutionize how you create, debug, and manage test scripts. See more and sign up for a free trial at https://applitools.info/ml6
Trawex provides Amadeus Travel API is a collection of APIs that allow travel organizations to access Amadeus' various travel-related products and content. This includes the ability to search for flights, book flights, get hotel information, and view destination content. With the Amadeus API, travel agents and agencies can serve travelers globally, help them find the best deals, and manage their travel businesses, reducing costs and increasing revenues.
For more details, Pls visit our website:
https://www.trawex.com/amadeus-travel-api.php
PathSpotter: Exploring Tested Paths to Discover Missing Tests (FSE 2024)andrehoraa
When creating test cases, ideally, developers should test both the expected and unexpected behaviors of the program to catch more bugs and avoid regressions. However, the literature has provided evidence that developers are more likely to test expected behaviors than unexpected ones. In this paper, we propose PathSpotter, a tool to automatically identify tested paths and support the detection of missing tests. Based on PathSpotter, we provide an approach to guide us in detecting missing tests. To evaluate it, we submitted pull requests with test improvements to open-source projects. As a result, 6 out of 8 pull requests were accepted and merged in relevant systems, including CPython, Pylint, and Jupyter Client. These pull requests created/updated 32 tests and added 80 novel assertions covering untested cases. This indicates that our test improvement solution is well received by open-source projects.
5. Graphs
Reference data
Life Sciences Ontologies
Vocabularies
Sharable data
Wikipedia Info boxes (DBpedia)
Analytics and Unstructured data
Fraud analysis
Social Graphs
6. Use Case for Graphs
Looking for patterns
➢ Analytics
● Social networks and recommendation engines
● Data center infrastructure management
➢ Knowledge Graphs
● Happenings: people, places, events
● Customer databases / products catalogues
8. RDF
➢ A graph is a set of links
Link: a triple : subject - predicate - object
predicate (or property) is the link name : an IRI
➢ IRIs (=URIs)
literals (strings, numbers, …)
blank nodes
9. prefix : <http://example/myData/>
prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
prefix foaf: <http://xmlns.com/foaf/0.1/>
# foaf:name is a short form of <http://xmlns.com/foaf/0.1/name>
:alice rdf:type foaf:Person ;
foaf:name "Alice Smith" ;
foaf:knows :bob .
:alice
foaf:knows
"Alice Smith"
foaf:name
foaf:Person
rdf:type
:bob
16. Apache Jena
TLP: April 2012
➢ Involvement in standards
➢ RDF 1.1, SPARQL 1.1
➢ RDF database
➢ SPARQL server
Other RDF@ASF:
➢ Any23, Marmotta, Clerezza, Stanbol, Rya
17. Property Graph Data Model
A property graph is a set of vertices and edges with
respective properties (i.e. key / values):
➢ each vertex or edge has a unique identifier
➢ each vertex has a set of outgoing edges and a set of incoming edges
➢ edges are directed: each edge has a start vertex and an end vertex
➢ each edge has a label which denotes the type of relationship
➢ vertexes and edges can have a properties (i.e. key / value pairs)
Directed multigraph with properties
attached to vertexes and edges
18. Property Graph: Example
id = 1 id = 2
name = “Alice”
surname = “Smith”
age = 32
email = alice@example.com
...
name = “Bob”
surname = “Brown”
age = 45
email = bob@example.com
...
since = 01/01/1970
...
id = 3
knows
19. Property Graphs : Access
➢ Tinkerpop Gremlin
DSL for various languages
g.V().as('person').out('knows').as('friend')
.select().by{it.value('name').length()}
➢ Cypher
MATCH (you:Person {name:"You"})
FOREACH (name in ["Johan","Rajesh","Anna","Julia","Andrew"] |
CREATE (you)-[:FRIEND]->(:Person {name:name}))
➢ Connect : API
22. Layering
Using Property Graphs tech for RDF
Using RDF tech for Property Graphs
Doable but why?
Can’t use the tools of one without
understanding the other.
23. What to take from RDF
URIs as data types
Data Exchange
Data modelling
Emphasis on data formats for exchange
Relational Algebra engines
25. What to take from PG
Separate links and values
Short names for attributes
Engines for Graph Algorithms
26. Some Conclusions
➢ Data Graphs are (still) new to many people
➢ RDF emphasizes information modelling
→ Knowledge graphs e.g SNOMED
→ SQL-like query
➢ Property Graph emphasizes data syntax
→ Data capture
→ Graph analytic algorithms
➢ Naive layering of data models leads dissatisfaction
→ Can only mix toolsets by knowing it’s layered
➢ Could share technology
→ Storage, data access, query algebra
29. The Answer
Building one on top of the other is possible
… but why do it?
Really hard to use! Worse of both worlds.
Semantic Web has some useful features
Apply to property graphs