Microsoft Technology Licensing, LLC

Other

Zhimin Chen

Vivek Ravindranath Narasayya

Surajit J Chaudhuri

Kris Kuppuswamy Ganjam

Kaushik Chakrabarti

James P Finnigan

Kanstantsyn Zoryn

Rakesh Agrawal

Prakash Sikchi

Ariel Fuxman

Stelios Paparizos

Zhongyuan Wang

Lawrence William Colagiovanni

Juliana Freire De Lima E Silva

Hoa Nguyen

Philip Alan Bernstein

Yeye He

Venkatesh Ganti

Arvind Arasu

Chi Wang

Jay Scott Fuller

Shriraghav Kaushik

Tao Cheng

Gunjan Jha

David Talby

Jiewen Huang

Eduardo Laureano

Renfei Luo

Tsheko Mutungu

James Finnigan

Vivek R Narasayya

The present invention extends to methods, systems, and computer program products for computing features of structured data. Aspects of the invention include computing features of table components (e.g., of rows, columns, cells, etc.). Computed features can be used for ranking the table components. When aggregated, features for different components of a table can be used for ranking the table (e.g., a web table).

Computing features of structured data

The present invention extends to methods, systems, and computer program products for understanding tables for search. Aspects of the invention include identifying a subject tuple (e.g., a subject column) for a table, detecting a tuple header (e.g., a column header) using other tables, and detecting a tuple header (e.g., a column header) using a knowledge base. Implementations can be utilized in a structured data search system (SDSS) that indexes structured information, such as, tables in a relational database or html tables extracted from web pages. The SDSS allows users to search over the structured information (tables) using different mechanisms including keyword search and data finding data.

Understanding tables for search

The present invention extends to methods, systems, and computer program products for refining structured data indexes. Aspects of the invention include associating structured data, such as, for example, tables, with additional content. Additional content can include content outside the &#x3c;table&#x3e; and &#x3c;/table&#x3e; tags of a web table. Indexes for structured data (e.g., table indexes) can be refined based on the additional content to improve the relevance of providing parts of the structured data (e.g., parts of the table) in search results.

Refining structured data indexes

Various technologies that facilitate performance of a data finding data (DFD) search are described herein. A user specifies entities, for example, by entering the entities into a query field, selecting the entities from a computer-executable application, or the like. The user further specifies an attribute of the entities that is of interest. A query is constructed based upon the entities and the attribute, and a search for tables is performed based upon the entities and the attribute. Values of the attribute for the selected entities are identified in a table, and the values of the attribute are returned.

Retrieval of attribute values based upon identified entities

Concept expansion using tables, such as web tables, can return entities belonging to a concept based on an input of the concept and at least one seed entity that belongs to the concept. A concept expansion frontend can receive the concept and seed entity and provide them to a concept expansion framework. The concept expansion framework can expand the coverage of entities for concepts, including tail concepts, using tables by leveraging rich content signals corresponding to concept names. Such content signals can include content matching the concept that appear in captions, early headings, page titles, surrounding text, anchor text, and queries for which the page has been clicked. The concept expansion framework can use the structured entities in tables to infer exclusive tables. Such inference differs from previous label propagation methods and involves modeling a table-entity relationship. The table-entity relationship reduces semantic drift without using a reference ontology.

Concept expansion using tables

A data service system is described herein which processes raw data assets from at least one network-accessible system (such as a search system), to produce processed data assets. Enterprise applications can then leverage the processed data assets to perform various environment-specific tasks. In one implementation, the data service system can generate any of: synonym resources for use by an enterprise application in providing synonyms for specified terms associated with entities; augmentation resources for use by an enterprise application in providing supplemental information for specified seed information; and spelling-correction resources for use by an enterprise application in providing spelling information for specified terms, and so on.

Data services for enterprises leveraging search system data assets

The present invention extends to methods, systems, and computer program products for annotating structured data for search. Aspects of the invention include associating structured data, such as, for example, tables, with additional content to improve indexing of the structured data for search and/or provide improved search results for structured data. Web pages can include tables as well as other content. The other content in a web page, such as, for example, content outside the &#x3c;table&#x3e; and &#x3c;/table&#x3e; tags of a web table, can be useful in supporting searches for web tables. Content in one web page can also be useful in supporting searches for a table in another web page.

Annotating structured data for search

The present invention extends to methods, systems, and computer program products for ranking tables for keyword search. Aspects of the invention include generating lists of candidate tables for inclusion in a search query response, computing table hit matrices, retrieving content from fields of candidate tables having keyword hits, generating ranking features of tables, and computing ranking scores for tables. Aspects of the invention can be used to match keywords against column names, to match keywords against values in subject and non-subject columns, and to match keywords against table descriptions like page titles, table captions, cell values, nearest headings and surrounding text. Which keywords are matched against which fields can depend on the table and/or the query (referred to as 'late binding').

Ranking tables for keyword search

The present invention extends to methods, systems, and computer program products for understanding tables for search. Aspects of the invention include identifying a subject column for a table, detecting a column header using other tables, and detecting a column header using a knowledge base. Implementations can be utilized in a structured data search system (SDSS) that indexes structured information, such as, tables in a relational database or html tables extracted from web pages. The SDSS allows users to search over the structured information (tables) using different mechanisms including keyword search and data finding data.

A set expansion system is described herein that improves precision, recall, and performance of prior set expansion methods for large sets of data. The system maintains high precision and recall by 1) identifying the qualify of particular lists and applying that quality through a weight, 2) allowing for the specification or negative examples in a set of seeds to reduce the introduction of bad entities into the set, and 3) applying a cutoff to eliminate lists that include a low number of positive matches. The system may perform multiple passes to first generate a good candidate result set and then refine the set to find a set with highest quality. The system may also apply Map Reduce or other distributed processing techniques to allow calculation in parallel. Thus, the system efficiently expands large concept sets from a potentially small set of initial seeds from readily available web data.

High precision set expansion for large concepts

Methods and systems for automatically synthesizing product information from multiple data sources into an on-line catalog are disclosed, and in particular, for automatically synthesizing the product information based on attribute-value pairs. Information for a product may be obtained, via entity extraction, feed ingestion, and other mechanisms, from a plurality of structured and unstructured data sources having different taxonomies and schemas. Product information may additionally or alternatively be obtained or derived based on popularity data. The product information may be cleansed, segmented and normalized. The product information may be clustered so closest products, attribute names and attribute values are associated. A representative value for an attribute name may be determined, and the on-line catalog may be updated so that entries are comprehensive, meaningful and useful to a catalog user. Updates from at least 500 million different data sources may be scheduled to occur as frequently as several times daily.

Product synthesis from multiple sources

A non-linear transformation including a plurality of non-linear logical operations is masked to a second or higher order. The masking includes receiving a set of random bits, and machine-masking two or more of the plurality of non-linear logical operations with a same random bit from the set of random bits.

Masking with shared random bits

A plurality of items included in a catalog may be obtained, each item associated with an item category. Brand indicators may be obtained, each brand indicator associated with the item category. Brand indicators associated with each of the items may be determined, and the each item may be assigned to a partition group associated with the brand indicator that is associated with the each item. Correlated string tokens that are correlated, greater than a predetermined correlation threshold value, with the brand indicator associated with the partition group that is associated with the each one of the items, the correlated string tokens associated with the each one of the plurality of items, may be determined. A dictionary hierarchy may be generated based on the one or more correlated string tokens.

Dictionary for hierarchical attributes from catalog items

This patent application relates to foreign-key detection. One implementation obtains a set of data tables. This implementation automatically determines foreign-key relationships of columns from separate tables of the set.

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Redmond, WA, United States of America

Zhimin Chen