Swoogle, the Semantic web search engine, is a research project carried out by the ubiquity research group in the Computer Science and Electrical Engineering Department at the University of Maryland. It’s an engine tailored towards finding documents on the semantic web.

Swoogle employed a system of crawlers to discover RDF documents and HTML documents with embedded RDF content.

The Swoogle engine performs crawling of semantic documents like most web search engines and the search is available as web service too. The engine is primarily written in Java with the PHP used for the front-end and MySQL for database. Swoogle is capable of searching over 10,000 ontologies and indexes more that 1.3 million web documents. It also computes the importance of a Semantic Web document. The techniques used for indexing are the more google-type page ranking and also mining the documents for inter-relationships that are the basis for the semantic web.

The Swoogle Architecture

Swoogle’s architecture can be broken into four major components:

This architecture is data centric and extensible. different components work on different tasks independently.

Advantages of Swoogle

Limitations of Swoogle


Swoogle is an implemented system that discovers, analyzes and indexes knowledge encoded in semantic web documents on the Web. Swoogle reasons about these documents and their constituent parts (e.g., terms and triples) and records meaningful metadata about them. Swoogle provides webscale semantic web data access service, which helps human users and software systems to find relevant documents, terms and triples, via its search and navigation services. Swoogle also provides a customizable algorithm inspired by Google’s PageRank algorithm but adapted to the semantics and use patterns found in semantic web documents.

B.Sc in Software Engineering (Honors)