Microsoft Technology Licensing, LLC

Alejandro Gutierrez Munoz

Igor Rondel

Jon Whisler

Michael Golebiewski

Sandeep Aparajit

Adam Levi

Shahab Moradi

A computing system generates a taxonomic tree for a domain in an unsupervised manner (e.g., without human intervention). Hierarchical structures of documents of the domain are collected from a document index. A category for each node of each of the hierarchical structures is extracted. The extracted categories are embedded as multidimensional category vectors in a multidimensional vector space. The multidimensional category vectors are grouped into multiple groups. The multidimensional category vectors of a first group satisfy a similarity condition for the first group better than the multidimensional category vectors of a second group. Each group of the multidimensional category vectors constitutes a category cluster. Each category cluster includes multidimensional category vectors for extracted categories from different hierarchical levels of the hierarchical structures. The taxonomic tree is generated with each category cluster inserted as a category node of the taxonomic tree.

Taxonomic tree generation

Techniques for enabling a search engine to automatically classify the content type of Web documents. In an exemplary embodiment, Web documents may be classified as adult or non-adult, based on whether a document contains adult content. In an aspect, Web documents are mined offline to determine the presence of 'adult hubs' to which adult documents are connected. The presence of such adult hubs is a strong indicator that linking Web documents may themselves contain adult content. Computational techniques for quantifying the connection between a candidate document and adult hubs are disclosed. The techniques may be utilized in an Internet search engine platform designed to accept user search queries and deliver highly relevant results.

Search engine classification

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Parkland, FL, United States of America

Alejandro Gutierrez Munoz