Published Sep 3, 2019

Episode 116: The Semantic Web with Jim Hendler

Discover the transformative power of AI in semantic web development with expert Jim Hendler, as he explores its evolution, ethical challenges, and the foundational technologies like RDF and OWL that enable seamless data integration and interconnected web applications.
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  • RDF & OWL

    explains the foundational elements of the semantic web, focusing on RDF and OWL. RDF, or Resource Description Framework, serves as a data format that allows databases to link and integrate by providing a standard for data description. OWL, or Web Ontology Language, is used to define terms within data, enabling the merging of databases by understanding the meaning behind data entries 1. This semantic understanding is crucial for creating a web where data can work together seamlessly, requiring machine-to-machine integration 2.

    The concept is, right now the information on the web is primarily described for human use. But if I'm using data or something like that, it's very hard to describe that for human use, because what I need to do is correlate it, aggregate it, collate it, query it, all things that sort of happen back behind the sort of somewhere in the infrastructure.

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    These technologies form the backbone of the semantic web, allowing for a more interconnected and meaningful web experience.

       

    SPARQL

    SPARQL, a query language for the semantic web, extends the capabilities of SQL by allowing queries on graph patterns. highlights how SPARQL, along with RDF and OWL, forms a comprehensive toolset for querying and linking data across the web 3. This approach enables web applications to directly query data, simplifying processes that traditionally required scraping data from web presentations 4.

    You need a standard way to query that sort of stuff. The language sparkle s p a r q lh is sort of the semantic web extension of SQL that lets you put in those graph patterns.

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    By utilizing SPARQL, developers can efficiently manage and interact with complex data sets, paving the way for more dynamic and responsive web applications.

       

    Inference Engines

    Inference engines play a pivotal role in the semantic web by enabling logical reasoning over data. compares these engines to Prolog, noting their ability to derive new information from existing data through defined relationships 5. These engines utilize RDF and OWL to standardize data descriptions, allowing for accurate inferences without the ambiguity often found in natural language 2.

    So essentially you start from agreement, we just know two terms mean the same thing. Then we can say given something is using that term, and is related by a certain relation to another term, maybe I know something about it.

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    This capability is essential for creating intelligent systems that can understand and manipulate data in a meaningful way, enhancing the overall functionality of the semantic web.

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