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https://github.com/mitre/quaerite

Search relevance evaluation toolkit
https://github.com/mitre/quaerite

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Search relevance evaluation toolkit

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README 

          

NOTE: Active development of this project has moved to https://github.com/tballison/quaerite.  The namespaces in the new repo and on maven central have been converted from 'org.mitre' to 'org.tallison'. This repository is no longer actively maintained.

===========================



Welcome to _Quaerite_

=================================================



Background and Goals

--------------------



This project includes tools to help evaluate relevance

ranking.  This code has been tested with Solr 4.x, 7.x and 8.x,

and ES 6.x and 7.x.



This project is not intended to compete with existing relevance

evaluation tools, such as [Splainer](http://splainer.io/),

[Quepid](https://quepid.com/), [Rated Ranking Evaluator](https://github.com/SeaseLtd/rated-ranking-evaluator/wiki/Maven-Plugin),

or [Luigi's Box](https://www.luigisbox.com/).

Rather, this project was developed for use cases not currently 

covered by open source software packages. The author encourages 

collaboration among these projects.



**NOTE: This project is under construction and is quite dynamic.  

There will be breaking changes before the first major release.**



While the name of this project may change in the future, we selected

_quaerite_ -- Latin imperative "seek", root of English "query" -- to

allude not only to the challenges of creating queries, but also

to the challenges of tuning search engines.  One may spend

a not insignificant amount of time tuning countless parameters.

In the end, we hope that _invenietis_ with slightly less effort

than without this project. For the pronunciation, see

[this link](https://forvo.com/word/quaerite_et_invenietis/).



Similarities and Differences between the Genetic Algorithm (GA) in _Quaerite_ and Learning to Rank

------------------------------------------ 

In the research literature, the application of a GA or Genetic Programming (GP) is one method for learning to rank (see, e.g. 

[Andrew Trotman](https://www.academia.edu/282562/Learning_to_Rank) on GP).



However, for integrators and developers who work in the Lucene ecosystem, "Learning to Rank" (LTR) connotes

a specific methodology/module initially added to Apache Solr by [Bloomberg](https://www.techatbloomberg.com/blog/bloomberg-integrated-learning-rank-apache-solr/) 

and then offered as a plugin for ElasticSearch by 

[Doug Turnbull and colleagues at OpenSource Connections, Wikimedia Foundation and Snagajob Engineering](https://elasticsearch-learning-to-rank.readthedocs.io/en/latest/).

In the following, I use LTR to refer to this Lucene-ecosystem-specific module and methodology.  



In _no_ way do I see this implementation of GA as a competitor to LTR; rather, it is another tool that 

might help complement LTR and/or other tuning methodologies.



### Similarities

* All of the basic requirements for quality search must first be met -- analyzer chains must be well designed for the data, 

    the underlying data in the index should be accurate, well organized and well curated 

* There must be sufficient, high quality, accurate and representative ground truth judgments for training and testing

* Machine learning can only do so much -- further tuning and/or adding new methods of enrichment may be required



### Differences

* In practice, LTR is designed to perform more costly calculations as a re-ranking step...that is, after the search 

engine has returned the best `n` documents, LTR is typically applied to carry out more costly calculations on 

this smaller subset of documents to re-rank the results based on the models built offline.  The goal of 

this implementation of GA (and the other tools in _Quaerite_) is to help tune the parameters used in the initial 

search system's ranking, _NOT_ as part of a secondary reranking.

* Bloomberg, OpenSource Connections, Wikimedia and Snagajob have spent quite a bit of time and effort 

developing and integrating these modules to make them easy to use.  This toolkit has been developed with 

far fewer resources for use initially by one relevance engineer...there are areas for improvement.



Current Status

----------------

As of this writing, `Quaerite` allows for experimentation with the following parameters:

`bf`, `bq`, `qf`, `pf`, `pf2`, `pf3`, `ps`, `ps2`, `ps3`, `q.op` (and `mm`), `solr url` 

(so that you can run experiments against 

different cores and/or different versions of Solr),

`customHandler` (so that you can compare different customized handlers), `tie`.

For ES, specifically, parameters include: `boost`, `fuzziness` and `multi_match_type` 

(e.g. `best_fields`, `most_fields`, `cross_fields` and `phrase`).



Getting Started

---------------

See the ```quaerite-examples``` module and its [README](https://github.com/mitre/quaerite/blob/master/quaerite-examples/README.md).



Releases

--------------

* [1.0.0-ALPHA](https://github.com/mitre/quaerite/releases) March 22, 2019



Road Map

----------

High priorities

* Add other features (e.g. ```bq```, ```bf```) as needed

* See the issues on [the issue tracker](https://github.com/mitre/quaerite/issues)



Planned Releases

* 1.0.0-beta1 May, 2019



Related Open Source Projects

---------------------

* https://quepid.com/

* https://github.com/SeaseLtd/rated-ranking-evaluator

* http://splainer.io/

* https://github.com/DiceTechJobs/RelevancyTuning



License (see also LICENSE.txt)

------------------------------



Copyright (c) 2019, The MITRE Corporation. All rights reserved.



Approved for Public Release; Distribution Unlimited. Case Number 18-3138-7.



Licensed to the Apache Software Foundation (ASF) under one or more contributor license agreements.  See the NOTICE file distributed with this work for additional information regarding copyright ownership.  The ASF licenses this file to You under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License.  You may obtain a copy of the License at