Microsoft Technology Licensing, LLC

Xiaoxin Yin

Yi-Min Wang

Sanaz Ahari

Brian W MacDonald

Sarthak D Shah

Nikhil Dandekar

Michael Maxwell Cameron

Farid Hosseini

Adam D Troy

Wenzhao Tan

Li-wei He

Emre Mehmet Kiciman

Iftah Gamzu

Joseph Yossi Azar

Dan Fain

Robert L Rounthwaite

Chun-Kai Wang

Chao Liu

Aleksey Sinyagin

Jinliang Fan

Galen Andrew

Fang Liu

Junhua Wang

Vijay Nair

Ryan Frederick Stewart

Daniel C Fain

Tiffany Kumi Dohzen

Yi-Chin Tu

Computer-readable media and computerized methods are provided for identifying terms associated with one or more queries, including aspects directed to manipulating a list of terms associated with a query class by extracting terms, identifying equivalences or relevant terms, and condensing or expanding the list of terms. In embodiments, synonymous terms can be identified. An updated list of terms can be written to a storage location in association with a query or query class.

Identifying terms associated with queries

Technologies pertaining to fact lookup queries are described herein. A relational database is automatically built by extracting attribute identities and attribute values from a one dimensional table, wherein the one dimensional table does not include an entity identity that corresponds to the attribute identity and the attribute value. The entity identity is inferred, and the attribute value is indexed in a relational database by the entity identity and the attribute identity. When a query is issued by a user that includes the entity identity and the attribute identity, the corresponding attribute value in the relational database is returned to the user.

Automated database generation for answering fact lookup queries

A crowdsourcing data structuring system and method for capturing unstructured data from the Web and adding structure by placing the data in a document that is accessible by others in a cloud computing environment. Using crowdsourcing, the unstructured data is annotated, amended, and verified to add structure to the unstructured data. An anchor and update module convert the data to a pointer that links the document to the data at an information source and stores the pointer in the document rather than the data itself. The data displayed in the document is updated whenever the information source is updated. A contribution module allows users to add data to the document, a validation module allows users to determine the validity of the data linked to in the document, and an expert ranking module allows users to rank the expert or contributor of the data in the document.

Structuring unstructured web data using crowdsourcing

Techniques and systems are disclosed providing improved ranking of results to an online search-based query. One or more user types are identified for a search-based query, and may correspond to a number of user relevant results, and which user results are selected. A user profile can be determined for the respective user types for the search-based query, which may identify a proportion of the users that belong to that type, and how many results are relevant to that type. A set of relevant results can be identified for the respective user types for the search-based query, based on a number of results used by the user type. An improved ranking of the results can be determined for the search-based query, from the one or more sets of relevant results, based on user profiles and a desired user satisfaction metric for a desired number of users.

Ranking results of multiple intent queries

A system described herein includes a receiver component that receives a dataset that is stored in a computer-readable medium of a computing device, wherein the dataset includes a plurality of queries issued by users to a search engine and a plurality of search results selected by the users upon issuing the plurality of queries. A distribution determiner component determines click distributions over the search results selected by the users with respect to the plurality of queries. A labeler component labels at least two queries in the plurality of queries as being substantially similar to one another based at least in part upon the click distributions over the search results selected by the users with respect to the plurality of queries.

Determining a similarity measure between queries

Computer-readable media and computerized methods for automatically organizing search results according to task groups are provided. The methods involve aggregating a gallery of entities (e.g., search queries that share a common categorization) into a query class and assigning a dictionary (e.g., list of terms that are drawn from various sources) to the query class. The task groups are identified from the list of terms within the dictionary. The process of identification includes analyzing patterns of user search behavior to select terms from the list of terms, which reflect popular user search intents, and ranking the selected terms based on predetermined parameters to produce an ordering. Based on the ordering, a set of the selected terms that are highest ranked are declared the task groups. The task groups are employed to arrange the search results on a UI display and to provide a consistent and intuitive format for refining a search.

Identifying task groups for organizing search results

Systems and methodologies for improved query classification and processing are provided herein. As described herein, a query prediction model can be constructed from a set of training data (e.g., diagnostic data obtained from an automatic diagnostic system and/or other suitable data) using a machine learning-based technique. Subsequently upon receiving a query, a set of features corresponding to the query, such as the length and/or frequency of the query, unigram probabilities of respective words and/or groups of words in the query, presence of pre-designated words or phrases in the query, or the like, can be generated. The generated features can then be analyzed in combination with the query prediction model to classify the query by predicting whether the query is aimed at a head Uniform Resource Locator (URL) or a tail URL. Based on this prediction, an appropriate index or combination of indexes can be assigned to answer the query.

System for finding queries aiming at tail URLs

Techniques and systems are disclosed providing improved ranking of results to an online search-based query. One or more user types are identified for a search-based query, which may correspond to a number of user relevant results and which ones are selected. A user profile can be determined for the respective user types for the search-based query, which may identify a proportion of the users that belong to that type, and how many results are relevant to that type. A set of relevant results can be identified for the respective user types for the query, based on a number of results used by the user type. An improved ranking of the results can be determined for the query, from the one or more sets of relevant results, based on the user profiles and a desired user satisfaction metric for a desired number of users.

Search relevance failures are diagnosed automatically. Users presented with unsatisfactory search results can report their dissatisfaction through various mechanisms. Dissatisfaction reports can trigger automatic investigation into the root cause of such dissatisfaction. Based on the identified root cause, a search engine can be modified to resolve the issue creating dissatisfaction thereby improving search engine quality.

Automatic diagnosis of search relevance failures

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Bothell, WA, United States of America

Xiaoxin Yin