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DBpedia Spotlight
Shedding light on the web of documents

It is a tool for automatically annotating mentions of DBpedia resources in text, providing a solution for linking unstructured information sources to the Linked Open Data cloud through DBpedia.

How Does the Tool Work?

With a four step approach, DBpedia Spotlight performs named entity extraction, including entity detection and name resolution. It can also be used for named entity recognition , amongst other information extraction tasks.

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Enrich your content and take advantage of Linked Open Data Cloud

Empower the user experience reusing, interlinking and making semantic queries among high-quality open datasets, extract meaning from unstructured data.

SPOTTING

Identification of surface forms substrings of the original input that may be entity mentions

CANDIDATE SELECTION

Selecting a set of surface forms from step 1 along with the DBpedia resources that are candidate meanings for those surface forms

DISAMBIGUATION

Deciding on the most likely candidate resource for each selected surface form

FILTERING

Adjusting the annotations to task-specific requirements according to user-provided configuration

Documentation

Click to the links below to learn more about DBpedia Spotlight. In our wiki, you can find all information you need to use and integrate DBpedia Spotlight in your projects.

Contribute to improve DBpedia Spotlight

If you find/discover any problem, bug or a possible improvement to DBpedia Spotlight please, make it publish through the DBpedia forum, don't forget to sign up and be part of the DBpedia community.

Acknowledgements

This work has been partially funded by

Publications

Improving Efficiency and Accuracy. in Multilingual Entity Extraction

There has recently been an increased interest in named entity recognition and disambiguation systems at major conferences such as WWW, SIGIR, ACL, KDD, etc. However, most work has focused on algorithms and evaluations, leaving little space for implementation details. In this paper, we discuss some implementation and data processing challenges we encountered while developing a new multilingual version of DBpedia Spotlight that is faster, more accurate and easier to configure [...]

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