I am not sure about you might heard about swoogle or not. On recent years swoogle gains more popularity among its community.

What is swoogle?

What the Wikipedia says?

“Swoogle provided services to human users through a browser interface and to software agents via RESTful web services. Several techniques were used to rank query results inspired by the PageRank algorithm developed at Google but adapted to the semantics and use patterns found in semantic web documents.”

Introduction

“what is the best way to index, digest and cache such systems?”, and “is it possible to create a meaningful rank measure that uses link semantics?”.

The system consists of a database that stores metadata about the SWDs, two distinct web crawlers that locate new and modified SWDs, components that compute useful document metadata, components to compute semantic relationships among the SWDs, an N-Gram based indexing and retrieval engine, a simple user interface for querying the system, and agent/web service APIs to provide useful services.

We describe an algorithm, Ontology Rank, inspired by the Page Rank algorithm , that is used to rank hits returned by the retrieval engine. This algorithm takes advantage of the fact that the graph formed by SWDs has a richer set of relations. In other word, the edges in this graph have explicit semantics. Some are defined or derivable from the RDF and OWL languages (e.g., imports, usesTerm, version, extends, etc.) and others by common ontologies (e.g., FOAF’s knows 1 ). We will also present some preliminary results summarizing the characteristics of the portion of the semantic web that our system has crawled and analyzed.

Following are broad uses of Swoogle:

  1. Finding instance data
  2. Studying the structure of the semantic web

Swoogle Architecture

Swoogle’s architecture has four major components

  1. SWD discovery -The potential SWDs throughout the Web and keep up-to date information about SWDs.
  2. Metadata creation - The metadata creation component caches a snapshot of a SWD and generates objective metadata about SWDs in both syntax level and semantic level.
  3. Data analysis -The data analysis component uses the cached SWDs and the created metadata to derive analytical reports, such as classification of SWO and SWDB, rank of SWDs, and the IR index of SWDs.
  4. Interface -The interface component focuses on providing data service to the Semantic Web community.

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

Reference: https://books.google.lk/books?id=-CcmoI2b2Z8C&pg=PA147&lpg=PA147&dq=swoogle&source=bl&ots=Y2AFOd4IPc&sig=ACfU3U3hU_nA9BMDBTDfdymBvjkL1ndGBg&hl=en&sa=X&ved=2ahUKEwjqg8nz_vHtAhX-yzgGHe_kCEkQ6AEwBnoECAcQAg#v=onepage&q=swoogle&f=false