Entities (such as people, organizations, or products) are meaningful units for organizing information and can provide direct answers to many search queries. Nordlys is a toolkit for entity-oriented and semantic search.
Nordlys currently supports four entity-oriented tasks, which are core components in semantic search:
- Entity cataloging
- Entity retrieval
- Entity linking in queries
- Target type identification
- General-purpose information retrieval and machine learning components at its core
- Implementations of various methods for the above entity-oriented search tasks (with more on their way)
- Based on the DBpedia knowledge base (extendible to other knowledge bases)
- Can used as a black box through a RESTful API
- Can be reached via a graphical web user interface
- Can be deployed on a local server and used as a Python package or as a command line tool
- Highly modular and well documented code, based on a 3-tier architecture
- Open source project that is actively being developed
Nordlys is not (yet) a mature production-level system, but rather a research prototype (as can be seen from the current version number, which is v0.2). We welcome contributions on all levels (pull requests, suggestions for improvements, feature requests, etc.).
Nordlys was presented as the following conference article:
F. Hasibi, K. Balog, D. Garigliotti and S. Zhang. Nordlys: A Toolkit for Entity-Oriented and Semantic Search. In: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '17). ACM. Tokyo, Japan. August 2017. DOI: 10.1145/3077136.3084149
You can get the author version of the article here.
If you use the resources presented in this repository, please cite:
@inproceedings{Hasibi:2017:NTE,
author = {Hasibi, Faegheh and Balog, Krisztian and Garigliotti, Dar\'{\i}o and Zhang, Shuo},
title = {Nordlys: A Toolkit for Entity-Oriented and Semantic Search},
booktitle = {Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval},
series = {SIGIR '17},
year = {2017},
pages = {1289--1292},
doi = {10.1145/3077136.3084149},
publisher = {ACM},
}
If possible, please also include the http://nordlys.cc/ URL in your paper.
Faegheh Hasibi, Krisztian Balog, Dario Garigliotti, Shuo Zhang.