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Tools for encoding Wikipedia articles as vectors.

The wiki2USE method, which is implemented in this project, makes it possible to quantify the conceptual similarity of any familiar stimuli (such as well-known people, places, or things) without having to collect massive datasets of similarity ratings from participants.

Conceptual similarity estimates can then be used to inform psychology and cognitive neuroscience experiments by providing information about general prior knowledge that participants may have about stimuli before beginning an experiment. Snapshots of Wikipedia at different times can be used to reflect changing public knowledge.

schematic showing how Wikipedia entries are used to estimate conceptual similarity of two famous people, Julia Roberts and Barack Obama
Schematic illustrating the wiki2USE method. First, each item of interest is matched to a corresponding Wikipedia article. Next, the text for each article is extracted and encoded using the universal sentence encoder to create a 512-dimensional vector that reflects the meaning of the information contained in the Wikipedia article. Finally, the vectors for different items are compared to estimate their conceptual similarity. Item similarity can be visualized using multidimensional scaling, which places items such that their relative distance reflects their relative dissimlarity. Model similarity correlates with human ratings of conceptual similarity.


To get the latest stable version:

pip install wikivector

To get the development version:

pip install git+git://

Exporting Wikipedia text

First, run WikiExtractor on a Wikipedia dump. This will generate a directory with many subdirectories and text files within each subdirectory. Next, build a header file with a list of all articles in the extracted text data:

wiki_header wiki_dir

where wiki_dir is the path to the output from WikiExtractor. This will create a CSV file called header.csv with the title of each article and the file in which it can be found.

To extract specific articles, write a CSV file with two columns: "item" and "title". The "title" for each item must exactly match an article title in the Wikipedia dump. We refer to this file as the map_file.

If you are working with an older Wikipedia dump, it can be difficult to find the correct titles for article pages, as page titles may have changed between the archive and the current online version of Wikipedia. To help identify mismatches between the map file and the Wikipedia dump, you can run:

wiki_check_map header_file map_file

to display any items whose article is not found in the header file. You can then use the Bash utility grep to search the header file for correct titles for each missing item.

When your map file is ready, extract the text for each item:

export_articles header_file map_file output_dir

where map_file is the CSV file with your items, and output_dir is where you want to save text files with each item's article. Check the output carefully to ensure that you have the correct text for each item and that XML tags have been stripped out.

Universal Sentence Encoder

Once articles have been exported, you can calculate a vector embedding for each item using the Universal Sentence Encoder.

embed_articles map_file text_dir h5_file

This reads a map file specifying an item pool (only the "item" field is used) and outputs vectors in an hdf5 file. To read the vectors, in Python:

from wikivector import vector
vectors, items = vector.load_vectors(h5_file)


If you use wikivector, please cite the following paper:

Morton, NW*, Zippi, EL*, Noh, S, Preston, AR. 2021. Semantic knowledge of famous people and places is represented in hippocampus and distinct cortical networks. Journal of Neuroscience. 41(12) 2762-2779. doi:10.1523/JNEUROSCI.2034-19.2021. *authors contributed equally


Tools for encoding Wikipedia articles as vectors.







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