Search (3 results, page 1 of 1)

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Abstract

The degree of structure of Web content is the determining factor for the types of functionality that search engines can provide. The more well structured the Web content is, the easier it is for search engines to understand Web content and provide advanced functionality, such as faceted filtering or the aggregation of content from multiple Web sites, based on this understanding. Today, most Web sites are generated from structured data that is stored in relational databases. Thus, it does not require too much extra effort for Web sites to publish this structured data directly on the Web in addition to HTML pages, and thus help search engines to understand Web content and provide improved functionality. An early approach to realize this idea and help search engines to understand Web content is Microformats, a technique for markingup structured data about specific types on entities-such as tags, blog posts, people, or reviews-within HTML pages. As Microformats are focused on a few entity types, the World Wide Web Consortium (W3C) started in 2004 to standardize RDFa as an alternative, more generic language for embedding any type of data into HTML pages. Today, major search engines such as Google, Yahoo, and Bing extract Microformat and RDFa data describing products, reviews, persons, events, and recipes from Web pages and use the extracted data to improve the user's search experience. The search engines have started to aggregate structured data from different Web sites and augment their search results with these aggregated information units in the form of rich snippets which combine, for instance, data This chapter gives an overview of the topology of the Web of Data that has been created by publishing data on the Web using the microformats RDFa, Microdata and Linked Data publishing techniques.

Source

Semantic search over the Web. Eds.: R. De Virgilio, et al

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Abstract

Six years ago in this magazine, Tim Berners-Lee, James Hendler and Ora Lassila unveiled a nascent vision of the Semantic Web: a highly interconnected network of data that could be easily accessed and understood by any desktop or handheld machine. They painted a future of intelligent software agents that would head out on the World Wide Web and automatically book flights and hotels for our trips, update our medical records and give us a single, customized answer to a particular question without our having to search for information or pore through results. They also presented the young technologies that would make this vision come true: a common language for representing data that could be understood by all kinds of software agents; ontologies--sets of statements--that translate information from disparate databases into common terms; and rules that allow software agents to reason about the information described in those terms. The data format, ontologies and reasoning software would operate like one big application on the World Wide Web, analyzing all the raw data stored in online databases as well as all the data about the text, images, video and communications the Web contained. Like the Web itself, the Semantic Web would grow in a grassroots fashion, only this time aided by working groups within the World Wide Web Consortium, which helps to advance the global medium. Since then skeptics have said the Semantic Web would be too difficult for people to understand or exploit. Not so. The enabling technologies have come of age. A vibrant community of early adopters has agreed on standards that have steadily made the Semantic Web practical to use. Large companies have major projects under way that will greatly improve the efficiencies of in-house operations and of scientific research. Other firms are using the Semantic Web to enhance business-to-business interactions and to build the hidden data-processing structures, or back ends, behind new consumer services. And like an iceberg, the tip of this large body of work is emerging in direct consumer applications, too.

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Date

5. 7.2015 22:02:31