by Angela Guess
Writing for Smart Data Collective, Zygimantas Jacikevicius recently explained, “The internet is a global computer network that is interlinked through URLs or a Uniform Resource Locator to access information stored on a different computer. Often a user does not know the exact URL directed to the information he/she wants. To find this information we use search engines such as Google or Bing where we search indexes to find relevant information. These rely on extracted keywords and other metrics such links with other sites and visitor numbers. Although this is a great breakthrough in technology, computers and search engines still struggle to understand the subtleties around meaning and context that often determine whether something is relevant or not. Machine learning and semantics are being developed to solve this problem of machines understanding context, meaning, relationships and other semantically challenging concepts.”
He goes on, “A cornerstone of the semantic web is its use of newer graph-based approaches and technologies – such as the RDF and SPARQL W3C initiatives. Given the internet is a giant web of connected data this model works well compared to traditional relational techniques where it has been necessary to structure data in ways less geared to showing complex relationships such as hierarchies. RDF or Resource Description Framework is a way of describing data in a way that it can be queried using the SPARQL language. Using a RDF framework is beneficial as instead of just relying on keywords search engines and browsers can also read the RDF files and understand the concepts references and the context of a web page – for example whether it is about a book, product, company or many other things a user might be searching for. The Resource Description Framework works in a simple way of dividing information into triples: subject – predicate – object.”
photo credit: Flickr
