AGDISTIS: Agnostic Disambiguation of Named Entities Using Linked Open Data

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AGDISTIS is an Open Source Named Entity Disambiguation Framework able to link entities against every Linked Data Knowledge Base.

Source Code Demo Issues

Logo AGDISTIS The ongoing transition from the current Web of unstructured data to the Data Web yet requires scalable and accurate approaches for the extraction of structured data in RDF (Resource Description Framework). One of the key steps towards extracting RDF from natural-language corpora is the disambiguation of named entities.

AGDISTIS combines the HITS algorithm with label expansion strategies and string similarity measures. Based on this combination, it can efficiently detect the correct URIs for a given set of named entities within an input text. Furthermore, AGDISTIS is agnostic of the underlying knowledge base.

AGDISTIS has been evaluated on different datasets against state-of-the-art named entity disambiguation frameworks.

Moreover, FOX is a modern Named Entity Recognition Framework which uses AGDISTIS for the Named Entity Linking Part. Additionally, FOX is used by DEER to extract named entities from resource descriptions.

For more information please visit our project site at Github.

The new web services are available here:

  • en http://akswnc9.informatik.uni-leipzig.de:8113/AGDISTIS
  • de http://akswnc9.informatik.uni-leipzig.de:8114/AGDISTIS
  • es http://akswnc9.informatik.uni-leipzig.de:8115/AGDISTIS
  • fr http://akswnc9.informatik.uni-leipzig.de:8116/AGDISTIS
  • it http://akswnc9.informatik.uni-leipzig.de:8117/AGDISTIS
  • ja http://akswnc9.informatik.uni-leipzig.de:8118/AGDISTIS
  • nl http://akswnc9.informatik.uni-leipzig.de:8119/AGDISTIS


AGDISTIS - Graph-Based Disambiguation of Named Entities using Linked Data (Best Research Paper) presented by Axel-Cyrille Ngonga Ngomo

NEWS

17.11.2017 Diego Moussallem will present the extension of AGDISTIS, dubbed MAG, at K-CAP https://k-cap2017.org/

01.05.2017 New release of AGDISTIS to support multilingual applications. https://github.com/AKSW/AGDISTIS/releases/tag/v1.0-mag

01.12.2015 AGDISTIS is used in the projects DIESEL (scalable NED/L in text) and QAMEL (resource-efficient linking in queries)

09.02.2015 - Since our last measurement in September 2014 AGDISTIS language versions were used as follows:

  • 88306 English AGDISTIS (before 63664 calls)
  • 473 German AGDISTIS (before 301 calls)
  • 258 Chinese AGDISTIS (before 168 calls)

This steady growth is amazing! Thank you all!

23.10.2014 - Award AGDISTIS We won the Best Research Paper Award at ISWC 2014. Read more here: http://blog.aksw.org/2014/aksw-successful-at-iswc2014/

Here is the video from the closing ceremony.

21.10.2014 - We present the AGDISTIS demo at Stand 79 at ISWC 2014. Visit us! Discuss with us!

22.09.2014 - Try out our new demo of AGDISTIS: http://agdistis.aksw.org/demo * Updated to DBpedia 2014 * Faster CSS and JS

16.09.2014 - Short news: the English version of AGDISTIS has been called 63664 times since deployment in October 2013. Sofar the German and the Chinese endpoint of AGDISTIS have been called 301 respectively 168 times since July 2014.

15.09.2014 - New version released!

AGDISTIS is now more efficient, faster and especially easier to configure.

12.09.2014 - We are currently improving maintainability of AGDISTIS by doing a light version of it. For all experiments as described in "AGDISTIS - Graph-Based Disambiguation of Named Entities using Linked Data by Ricardo Usbeck, Axel-Cyrille Ngonga Ngomo, Sören Auer, Daniel Gerber und Andreas Both in International Semantic Web Conference" have a look at commit #80 or release v0.0.1

Publications

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News

DL-Learner 1.4 (Supervised Structured Machine Learning Framework) Released ( 2019-09-24T22:41:46+02:00 by Simon Bin)

2019-09-24T22:41:46+02:00 by Simon Bin

Dear all, The Smart Data Analytics group [1] and the E.T.-db-MOLE sub-group located at the InfAI Leipzig [2] is happy to announce DL-Learner 1.4. DL-Learner is a framework containing algorithms for supervised machine learning in RDF and OWL. Read more about "DL-Learner 1.4 (Supervised Structured Machine Learning Framework) Released"

DBpedia Day @ SEMANTiCS 2019 ( 2019-08-01T10:35:05+02:00 by Sandra Bartsch)

2019-08-01T10:35:05+02:00 by Sandra Bartsch

 We are happy to announce that SEMANTiCS 2019 will host the 14th DBpedia Community Meeting at the last day of the conference on September 12, 2019. Read more about "DBpedia Day @ SEMANTiCS 2019"

LDK conference @ University of Leipzig ( 2019-03-22T09:21:41+01:00 by Julia Holze)

2019-03-22T09:21:41+01:00 by Julia Holze

With the advent of digital technologies, an ever-increasing amount of language data is now available across various application areas and industry sectors, thus making language data more and more valuable. Read more about "LDK conference @ University of Leipzig"

13th DBpedia community meeting in Leipzig ( 2019-02-22T12:22:07+01:00 by Julia Holze)

2019-02-22T12:22:07+01:00 by Julia Holze

We are happy to invite you to join the 13th edition of the DBpedia Community Meeting, which will be held in Leipzig. Read more about "13th DBpedia community meeting in Leipzig"

SANSA 0.5 (Semantic Analytics Stack) Released ( 2018-12-13T09:25:34+01:00 by Prof. Dr. Jens Lehmann)

2018-12-13T09:25:34+01:00 by Prof. Dr. Jens Lehmann

We are happy to announce SANSA 0.5 – the fifth release of the Scalable Semantic Analytics Stack. Read more about "SANSA 0.5 (Semantic Analytics Stack) Released"