SIREn - Semi-Structured Information Retrieval Engine
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Latest commit 725fac7 Jul 22, 2014 @rendel rendel Added project status

SIREn: Open-Source Semi-Structured Information Retrieval Engine


This academic project is not maintained anymore. For updates and commercial support, please refer to the new project web site.


SIREn is a Lucene/Solr extension for efficient schemaless semi-structured full-text search. SIREn is not a complete application by itself, but rather a code library and API that can easily be used to create a full-featured semi-structured search engine.

Efficient, large scale handling of semi-structured data is an increasingly important issue in information search scenarios on the web as well as in the enterprise..

While Lucene has long offered these capabilities, its native capabilities are not intended for collections of schemaless semi-structured documents, e.g., collections where the schema varies across documents or collections with a complex schema and a complex nested structure. For this reason we have developed SIREn, a Lucene/Solr plugin to overcome these shortcomings and to efficiently index and query complex JSON documents with arbitrary schema.

For its features, SIREn can be seen as being halfway between Solr (of which it offers all the search features) and MongoDB (given it can index arbitrary JSON documents).


The SIREn project is composed of six modules:

  • siren-parent: This module provides the parent pom that defines configuration shared across all the other modules.

  • siren-core: This module provides the core functionality of SIREn such as the low-level indexing and search APIs.

  • siren-qparser: This module provides a number of query parsers to easily create complex queries through rich query languages.

  • siren-solr: This module provides plugins for Solr to integrate the core functionality and the query languages of SIREn into the Solr API.

  • siren-demo: This module provides a demonstration of the functionality of SIREn.


If you are using SIREn for your scientific work, please cite the following article as follow:

Renaud Delbru, Stephane Campinas, Giovanni Tummarello, Searching web data: An entity retrieval and high-performance indexing model, In Web Semantics: Science, Services and Agents on the World Wide Web, ISSN 1570-8268, 10.1016/j.websem.2011.04.004.



Please join the SIREn-User mailing list by subscribing at SIREn-User.


The SIREn project is based upon works supported by:

  • the European FP7 Okkam (GA 215032) and LOD2 (257943) projects,
  • the SFI funded project Lion2 under Grant No. SFI/08/CE/I1380,
  • the Irish Research Council for Science, Engineering and Technology.

Copyright 2014, National University of Ireland, Galway