Web Similarity in Sets of Search Terms using Database Queries

TL;DR

This paper introduces the normalized web distance (NWD), a semantic similarity measure for sets of search terms based on web data, enabling classification and learning across multiple databases.

Contribution

It develops the theory of NWD for sets of terms, demonstrating its application in classification tasks and its advantages over pairwise measures like NGD.

Findings

NWD effectively classifies search terms using web data.

NWD reveals new correlations in health hazard data.

NWD outperforms pairwise NGD in extracting set-level information.

Abstract

Normalized web distance (NWD) is a similarity or normalized semantic distance based on the World Wide Web or another large electronic database, for instance Wikipedia, and a search engine that returns reliable aggregate page counts. For sets of search terms the NWD gives a common similarity (common semantics) on a scale from 0 (identical) to 1 (completely different). The NWD approximates the similarity of members of a set according to all (upper semi)computable properties. We develop the theory and give applications of classifying using Amazon, Wikipedia, and the NCBI website from the National Institutes of Health. The last gives new correlations between health hazards. A restriction of the NWD to a set of two yields the earlier normalized google distance (NGD) but no combination of the NGD's of pairs in a set can extract the information the NWD extracts from the set. The NWD enables a new contextual (different databases) learning approachbased on Kolmogorov complexity theory that incorporates knowledge from these databases.