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Readme.md

License Documentation Status SWH

entity-fishing

entity-fishing performs the following tasks:

  • entity recognition and disambiguation against Wikidata in a raw text or partially-annotated text segment, entity-fishing

  • entity recognition and disambiguation against Wikidata at document level, in particular for a PDF with layout positioning and structure-aware annotations, entity-fishing

  • search query disambiguation (the short text mode) - below disambiguation of the search query "concrete pump sensor" in the service test console, Search query disambiguation

  • weighted term vector disambiguation (a term being a phrase), Search query disambiguation

  • interactive disambiguation in text editing mode (experimental).
    Editor with real time disambiguation

Documentation

Presentation of entity-fishing at WikiDataCon 2017 for some design, implementation descriptions, and some evaluations.

The documentation of entity-fishing is available here.

Current version

entity-fishing is a work-in-progress! Latest release version is 0.0.4.

This version supports English, French, German, Italian and Spanish, with an in-house Named Entity Recognizer for English and French. For this version, the knowledge base includes around 87 million entities and 1.1 billion statements from Wikidata.

Runtime: on local machine (Intel Haswel i7-4790K CPU 4.00GHz - 8 cores - 16GB - SSD)

  • 800 pubmed abstracts (172 787 tokens) processed in 126s with 1 client (1371 tokens/s)

  • 4800 pubmed abstracts (1 036 722 tokens) processed in 216s with 6 concurrent clients (4800 tokens/s)

  • 136 PDF (3443 pages, 1 422 943 tokens) processed in 1284s with 1 client (2.6 pages/s, 1108.2 tokens/s)

  • 816 PDF (20658 pages, 8 537 658 tokens) processed in 2094s with 6 concurrent clients (9.86 pages/s, 4077 tokens/s)

Accuracy: f-score for disambiguation only between 76.5 and 89.1 on standard datasets (ACE2004, AIDA-CONLL-testb, AQUAINT, MSNBC) - to be improved in the next versions.

The knowledge base contains more than 1.5 billion objects, not far from 15 millions word and entity embeddings, however entity-fishing will work with 3-4 GB RAM memory after a 15 second start-up for the server - but please use SSD!

How to cite

If you want to cite this work, please refer to the present GitHub project, together with the Software Heritage project-level permanent identifier. For example, with BibTeX:

@misc{entity-fishing,
    title = {entity-fishing},
    howpublished = {\url{https://github.com/kermitt2/entity-fishing}},
    publisher = {GitHub},
    year = {2016--2020},
    archivePrefix = {swh},
    eprint = {1:dir:cb0ba3379413db12b0018b7c3af8d0d2d864139c}
}

License and contact

Distributed under Apache 2.0 license. The dependencies used in the project are either themselves also distributed under Apache 2.0 license or distributed under a compatible license.

Main author and contact: Patrice Lopez (patrice.lopez@science-miner.com)

entity-fishing is developed and maintained by SCIENCE-MINER (since 2015, first Open Source public version in 2016), with contributions of Inria Paris (2017-2018).

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