oke-challenge (https://github.com/anuzzolese/oke-challenge) Task 2: Class Induction and entity typing for Vocabulary and Knowledge Base enrichment.

• (i) identify the type(s) of the given entity as they are expressed in the given definition,
• (ii) create a owl:Class statement for defining each of them as a new class in the target knowledge base,
• (iii) create a rdf:type statement between the given entity and the new created classes, and
• (iv) align the identified types, if a correct alignment is available, to a set of given types.

J. Gao and S. Mazumdar. Exploiting linked open data to uncover entity type. In Proceedings of the OKE Challenge 2015 co-located with the 12th Extended Semantic Web Conference (ESWC 2015), 2015.

Prerequisite to run:

1. install python 3+
   

2. install libraries:
   

   $ sudo pip3 install flask $ sudo pip3 install -U nltk $ sudo pip3 install -U numpy scipy scikit-learn $ sudo pip3 install -U distance

   ===Download NLTK data===== Run the Python interpreter and type the commands: import nltk nltk.download()

3. need the access to DBpedia

1. run test example
   

• /oke-extractor/oke/oak/$ /usr/bin/python3.4 okeConceptRecogniser.py 

2. run with webservice
   start webservice
   

   • /oke-extractor/oke/oak/$/usr/bin/python3.4 conceptRecognitionWS.py

   
   This will start a web service via localhost:5000 with a RESTful API "http://localhost:5000/extract". API accepts N3 formatted NIF data input and output enriched N3 formatted NIF data. oke.oak.TestOKEConceptRecogniserWS.py gives an example how to test the API.

   Example test via cURL:

   curl -i -X POST http://localhost:5000/extract -H "Content-Type: text/xml" --data-binary "@path-to-file\task2_test.ttl"

Official results is published in https://github.com/anuzzolese/oke-challenge. Annotator related to this work is OAK@Sheffield