Kanserini

Kanserini is an open-source search engine toolkit developed by the Information Retrieval and Data Mining (IRDM) group at the University of Waterloo. It is designed to facilitate research and development in information retrieval and related fields. The toolkit provides a comprehensive set of tools and resources for building and evaluating search engines, including indexing, retrieval, and evaluation components.

Kanserini is built on top of the Apache Lucene library, which is a widely-used open-source search library. It extends Lucene's capabilities by providing additional features and tools tailored for research purposes. These features include support for various indexing and retrieval models, such as BM25, language modeling, and neural ranking models.

One of the key strengths of Kanserini is its support for reproducible research. The toolkit includes scripts and documentation to help researchers reproduce experiments and compare results with existing studies. This is achieved by providing access to standard datasets, evaluation metrics, and experimental setups.

Kanserini also includes tools for evaluating search engines, such as the trec_eval tool, which is used to compute standard information retrieval metrics like Mean Average Precision (MAP) and Normalized Discounted Cumulative Gain (NDCG). These tools help researchers assess the performance of their search engines and compare them with state-of-the-art systems.

The toolkit is actively maintained and updated by the IRDM group, with contributions from the research community. It is released under an open-source license, allowing researchers and developers to use, modify, and distribute the software freely. Kanserini is widely used in academia and industry for research and development in information retrieval and related fields.