Microsoft Technology Licensing, LLC

Micorsoft Corpration

Microsoft Corporation

Ji-Rong Wen

Wei-Ying Ma

Zaiqing Nie

Ruihua Song

Shuming Shi

Jun Zhu

Xiaojiang Liu

Yong Cao

Hang Cui

Hong-Jiang Zhang

Yi-Min Wang

Zhiwei Li

Qing Yu

Gang Luo

Ruochi Zhang

Haifeng Liu

Deng Cai

Yunxiao Ma

Bo Zhang

Xing Xie

Zheng Zhang

Ying-Qing Xu

Fei Xing

Chun Liang Yuan

Shipeng Yu

Jian-Yun Nie

Zhicheng Dou

Mingjie Zhu

Shihui Zheng

Hongjun Lu

Guomao Xin

Jianfeng Gao

Hang Li

Lie Lu

Yu Chen

Ying Li

Mingjing Li

Hsiao-Wuen Hon

Liu Yang

Matthew Robert Scott

Lee Wang

Hua Yang

Qi Yao

Kai Wu

Cong Li

Nan Sun

Eric Anthony Reel

Daniel Takacs

Tong Yao

Junyan Chen

Chunyu Yang

Jihwan Song

Zhengdong Lu

Jiyu Zhang

Jonathan R M Hart

Wen-Jun Zhou

Honghua Dai

Lingzhi Zhao

Min Wan

Shuyi Zeng

Gengxin Miao

Xiaofei He

Ming-Jing Li

Hang Deptof Computer Science Cui

The automated social networking graph mining and visualization technique described herein mines social connections and allows creation of a social networking graph from general (not necessarily social-application specific) Web pages. The technique uses the distances between a person's/entity's name and related people's/entities names on one or more Web pages to determine connections between people/entities and the strengths of the connections. In one embodiment, the technique lays out these connections, and then clusters them, in a 2-D layout of a social networking graph that represents the Web connection strengths among the related people's or entities' names, by using a force-directed model.

Automated networking graph mining and visualization

When a user-submitted query is received, a set of candidate queries is identified. For each of the candidate queries, features are extracted that, for each candidate query, reflect a measure of effectiveness of the candidate query. The candidate queries are rank ordered based on the measure of effectiveness, and one or more of the top-ranked candidate queries are presented as suggested alternatives to the user-submitted query.

Adaptive query suggestion

Automated social networking graph mining and visualization

Techniques are described for automatically mining query dimensions from web pages resulting from execution of a search query. Lists of items such as words, terms, or phrases are extracted from the web pages based on the recognition of free text, metadata tag, or repeated region patterns within the web page text. Extracted item lists are weighted according to document matching and/or inverse document frequency, and item lists are clustered based on shared or similar items within the lists to generate query dimensions. The generated query dimensions, and the items within each query dimension, are ranked according to quality, and high-quality query dimensions are provided for display alongside top search results.

Extracting query dimensions from search results

Interactive synchronization of Web data and spreadsheets is usable to build data wrappers based on any type of data found in a document. Such data wrappers can be used to interact with source documents, crawl a network for additional data, map data from across domains, and/or synchronize data from dynamic Web documents.

Interactive synchronization of web data and spreadsheets

Methods and systems for Web-scale entity relationship extraction are usable to build large-scale entity relationship graphs from any data corpora stored on a computer-readable medium or accessible through a network. Such entity relationship graphs may be used to navigate previously undiscoverable relationships among entities within data corpora. Additionally, the entity relationship extraction may be configured to utilize discriminative models to jointly model correlated data found within the selected corpora.

Displaying search results with edges/entity relationships in regions/quadrants on a display device

Described is a technology for understanding entities of a webpage, e.g., to label the entities on the webpage. An iterative and bidirectional framework processes a webpage, including a text understanding component (e.g., extended Semi-CRF model) that provides text segmentation features to a structure understanding component (e.g., extended HCRF model). The structure understanding component uses the text segmentation features and visual layout features of the webpage to identify a structure (e.g., labeled block). The text understanding component in turn uses the labeled block to further understand the text. The process continues iteratively until a similarity criterion is met, at which time the entities may be labeled. Also described is the use of multiple mentions of a set of text in the webpage to help in labeling an entity.

Webpage entity extraction through joint understanding of page structures and sentences

Techniques for displaying a relationship graph are described herein. In one example, a search term may be used to obtain a plurality of documents from a network, such as the Internet. A plurality of entities, and relationships between at least some of those entities, may be extracted from the documents. In an example user interface, representations of a plurality of entities may be displayed, such as by shapes (e.g., circles) labeled to identify people or organizations. Edges (e.g., lines) may be used to connect different representations of entities and to thereby indicate a relationship between the connected entities. In a particular example, input from movement of a cursor over an edge may result in display of a description of a relationship between the connected entities. In a further particular example, size of each entity may be related to a number of connections each has with others.

Web-scale entity relationship extraction

A topic modeling architecture is used to discover high-quality semantic classes from a large collection of raw semantic classes (RASCs) for use in generating responses to queries. A specific semantic class is identified from a collection of RASCs, and a preprocessing operation is conducted to remove one or more items with a semantic class frequency less than a predetermined threshold. A topic model is then applied to the specific semantic class for each of the items that remain in the specific semantic class after the preprocessing operation. A postprocessing operation is then conducted on the items of the specific semantic class to merge and sort the results of the topic model and generate final semantic classes for use by a search engine to respond to a query.

Employing topic models for semantic class mining

A method and system for determining relevance of a document having text and images to a text string is provided. A scoring system identifies image text associated with an image of the document. The scoring system calculates an image score indicating relevance of the image text to the text string. The image score may be used in many applications, such as searching, summary generation, and document classification, image search, and image classification.

Scoring relevance of a document based on image text

A 'Name Disambiguator' provides various techniques for implementing an interactive framework for resolving or disambiguating entity names (associated with objects such as publications) for entity searches where two or more same or similar names may refer to different entities. More specifically, the Name Disambiguator uses a combination of user input and automatic models to address the disambiguation problem. In various embodiments, the Name Disambiguator uses a two part process, including: 1) a global SVM trained from large sets of documents or objects in a simulated interactive mode, and 2) further personalization of local SVM models (associated with individual names or groups of names such as, for example, a group of coauthors) derived from the global SVM model. The result of this process is that large sets of documents or objects are rapidly and accurately condensed or clustered into ordered sets by that are organized by entity names.

Interactive framework for name disambiguation

Web-scale entity relationship extraction that extracts pattern(s) based on an extracted tuple

A method and system for identifying the importance of information areas of a display page. An importance system identifies information areas or blocks of a web page. A block of a web page represents an area of the web page that appears to relate to a similar topic. The importance system provides the characteristics or features of a block to an importance function that generates an indication of the importance of that block to its web page. The importance system 'learns' the importance function by generating a model based on the features of blocks and the user-specified importance of those blocks. To learn the importance function, the importance system asks users to provide an indication of the importance of blocks of web pages in a collection of web pages.

Method and system for calculating importance of a block within a display page

This document describes tools for adjusting anchor text weight to provide more relevant search engine results. Specifically, these tools take advantage of a site-relationship model to consider relationships not only between an anchor text source site and a destination page but also relationships between multiple anchor text source sites to improve web searches. Consideration of these relationships aids in determining a new an anchor text weight, which in turn results in more relevant search results.

Using anchor text with hyperlink structures for web searches

Described is a summarizing a web entity (e.g., a person, place, product or so forth) based upon the entity's appearance in web documents (e.g., on the order of hundreds of millions or billions of webpages). Webpages are separated into blocks, which are then processed according to various features to filter the number of blocks to further process, and rank the most relevant blocks with respect to the entity that remain. A redundancy removal mechanism removes redundant blocks, leaving a set of remaining blocks that are used to provide a summary of information that is relevant to the entity.

Web-scale entity summarization

Growing community of inventors

Beijing, China

Ji-Rong Wen