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Linked Data Tutorial
Linked Data is a set of design principles for sharing machine-readable interlinked data on the Web. It is one of the main components of Semantic Web, a domain which seeks to create links between datasets that are understandable to both humans and machines. For this purpose, Linked Data implements a number of standards in order to determine the guidelines for merging and integrating hypertext datasets of different formats and sources. These principles are coined by Tim Berners-Lee as early as in 2006 which is also the inventor of the World Wide Web.
The Uniform Resource Identifier (URI) is a single global identification system used for giving unique names to anything – from digital content available on the Web to real-world objects and abstract concepts.
As the HTTP protocol provides a simple mechanism for retrieving resources, when things can be identified by URIs in conjunction with this protocol, they become easier to find.
To be able to use URIs efficiently, we should use RDF or SPARQL for querying. RDF is a graph-based data representation format while SPARQL is the query language for manipulating or fetching data registered in RDF format.
Links to other URIs makes data interconnected and enables the user to access sets of related data by maximizing the reuse and interlinking among existing data.
Linked Data introduces a variety of enhancements to Web including:
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Scalability: Linked Data is a design that ensures a graceful scalability as more datasets are introduced to Web. The decentralized nature of Linked Data provides an easy interface for the integration of new data into the existing data space without requiring significant changes to existing systems.
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Interconnectedness: Linked Data provides linking between various datasets and creates a wide network of interlinked data. This brings an easier access between related data that belongs to different resources and formats.
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Transparency: Linked Data is most of the time intertwined with Open Data by creating Linked Open Data (LOD) which supports both interlinked data across Web and free & open usability of resources amongst different users and hosts. This transparency leads to collaboration and innovation by allowing researchers and developers to build upon existing datasets.
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Flexibility: Linked Data supports representation of different formats of data which allows for the integration of data from various fields.
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Semantic Enrichment: Linked Data incorporates semantic technologies such as RDF (Resource Description Framework) and OWL (Web Ontology Language), which enable the representation of rich semantic metadata alongside the data itself.
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DBpedia: a dataset containing extracted data from Wikipedia; it contains about 3.4 million concepts described by 1 billion , including abstracts in 11 different languages
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GeoNames: provides RDF descriptions of more than 7,500,000 geographical features worldwide
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Wikidata: a collaboratively-created linked dataset that acts as central storage for the structured data of its sibling projects
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Global Research Identifier Database (GRID): an international database of 89,506 institutions engaged in academic research, with 14,401 relationships. GRID models two types of relationships: a parent-child relationship that defines a subordinate association, and a related relationship that describes other associations
Linked Data, despite its potential, faces several challenges that can impact its adoption and effectiveness. Here are some of the common challenges and proposed solutions:
- Challenge: Ensuring high data quality and consistency across different datasets can be difficult due to varying standards and practices.
- Solution: Implementing comprehensive data governance policies and using automated validation tools to check data quality and consistency.
- Challenge: Protecting sensitive information while promoting data linking and sharing.
- Solution: Adopting robust encryption methods, access control mechanisms, and anonymization techniques to safeguard data privacy and security.
- Challenge: Managing and querying vast amounts of linked data efficiently.
- Solution: Developing more efficient storage systems, indexing techniques, and query optimization methods to enhance scalability.
- Challenge: Ensuring interoperability among diverse data sources and linked data applications.
- Solution: Promoting the use of common standards and best practices for linked data publication and consumption.
The future of Linked Data is promising, with several emerging trends and areas of research that could significantly enhance its capabilities and applications:
- Integration with AI and Machine Learning: Leveraging linked data for knowledge representation in AI models to improve machine learning outcomes.
- Decentralized Web: Using linked data principles to build a more decentralized web where users have control over their data.
- Semantic Web Technologies: Continued advancement in semantic web technologies to create more intelligent and adaptable linked data applications.
- Data Provenance: Developing methods to track the origin and history of linked data to ensure its reliability and trustworthiness.
- Knowledge Graphs: Enhancing knowledge graphs with linked data to improve information discovery and decision-making processes.
- Natural Language Processing (NLP): Integrating linked data with NLP to enrich text analysis and understanding.
The ongoing development in these areas promises to address current challenges and open new opportunities for linked data, making it an even more valuable asset in the web's evolution.
This section provides definitions for key terms associated with Linked Data. Understanding these terms is essential for navigating the concepts and discussions surrounding Linked Data.
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Linked Data: A method of publishing structured data so that it can be interlinked and become more useful. It extends the Web to facilitate data sharing and reuse across various applications.
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RDF (Resource Description Framework): A standard model for data interchange on the Web. RDF extends the linking structure of the Web to use URIs to name the relationship between things as well as the two ends of the link (usually referred to as a triple).
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SPARQL (SPARQL Protocol and RDF Query Language): An RDF query language—that is, a semantic query language for databases—able to retrieve and manipulate data stored in Resource Description Framework (RDF) format.
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OWL (Web Ontology Language): A family of knowledge representation languages for authoring ontologies. Ontologies are a formal way to describe taxonomies and classification networks, essentially defining the structure of knowledge for various domains.
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URI (Uniform Resource Identifier): A string of characters used to identify a resource on the Internet. URIs enable users to deal with resources without needing to know how they are implemented at any moment.
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Semantic Web: An extension of the World Wide Web through standards set by the World Wide Web Consortium (W3C). The goal of the Semantic Web is to make Internet data machine-readable.
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Knowledge Graph: A knowledge base that uses a graph-structured data model or topology to integrate data. Knowledge graphs often serve as the backbone of linked data and semantic web applications.
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N-Triples: A format for storing and transmitting data, part of the RDF Specification. It is a line-based, plain text format for encoding RDF graphs.
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SKOS (Simple Knowledge Organization System): A common data model for sharing and linking knowledge organization systems via the Web.
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RDFa (Resource Description Framework in Attributes): A W3C Recommendation that adds a set of attribute-level extensions to HTML, XHTML, and various XML-based document types for embedding rich metadata within Web documents.
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