In this project, we use skip-gram model to embed Wikipedia Concepts and Entities. The English version of Wikipedia contains more than five million pages, which suggest its capability to cover many English Entities, Phrases, and Concepts. Each Wikipedia page is considered as a concept.

WikipediaParser folder contains the code to extract and prepare the Wikipedia dump.

Please find in the following, link the pre-traind Concept, Word and Entitie vectors (as a result of this project):

• The Wikipedia ID to Title map file (This file maps the Wikipedia ID of a page to its title): https://web.cs.dal.ca/~sherkat/Files/ID_title_map.zip (210MB)

• Traind Concepts, Entities and Words: Concepts and entities are presented by their Wikipedia ID.

• ConVec https://web.cs.dal.ca/~sherkat/Files/WikipediaClean5Negative300Skip10.zip (3.3GB)

• ConVec Fine Tuned: https://dalu-my.sharepoint.com/personal/eh379022_dal_ca/_layouts/15/guestaccess.aspx?docid=0602754033d8a4e65aa8c841aa2efc491&authkey=AZGkUWbSwjiO4SrsPub0LBI (6.86GB)

• ConVec Heuristic: https://dalu-my.sharepoint.com/personal/eh379022_dal_ca/_layouts/15/guestaccess.aspx?docid=0b42ef5ba2d3247ccab2c10d5c1691a47&authkey=AYZLBIfjQLta9aAmSaRzymY (3.75GB)

• ConVec Only Anchors https://dalu-my.sharepoint.com/personal/eh379022_dal_ca/_layouts/15/guestaccess.aspx?docid=0592fd0392e1f4ec89d84a512e6482d03&authkey=AVpBqVspTRnF0Ra6xedMVIk (3.57GB)

Please cite to the following paper if you used the code, datasets and vector embedings:

@misc{NLDBSherkat2017,
   Author = {Ehsan Sherkat, Evangelos Milios},
   Title  = {Vector Embedding of Wikipedia Concepts and Entities},
   Year   = {2017},
   link   = {https://arxiv.org/abs/1702.03470}
   Url    = {https://doi.org/10.1007/978-3-319-59569-6_50}
   doi    = {10.1007/978-3-319-59569-6_50}
   booktitle= {Natural Language Processing and Information Systems, NLDB 2017}
}