Microsoft Technology Licensing, LLC

University of Illinois

Tao Cheng

Surajit J Chaudhuri

Kaushik Chakrabarti

Dong Xin

Vivek Ravindranath Narasayya

Kris Kuppuswamy Ganjam

Yeye He

Stelios Paparizos

Chi Wang

Zhimin Chen

Xifeng Yan

Kevin Chen-Chuan Chang

Hady W Lauw

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

A targeted disambiguation system is described herein which determines true mentions of a list of named entities in a collection of documents. The list of named entities is homogenous in the sense that the entities pertain to the same subject matter domain. The system determines the true mentions by leveraging the homogeneity in the list, and, more specifically by applying a context similarity hypothesis, a co-mention hypothesis, and an interdependency hypothesis. In one implementation, the system executes its analysis using a graph-based model. The system can operate without the existence of additional information regarding the entities in the list; nevertheless, if such information is available, the system can integrate it into its analysis.

Targeted disambiguation of named entities

A plurality of description phrases associated with a first domain may be determined, based on an analysis of a first plurality of documents to determine co-occurrences of the description phrases with one or more name labels associated with the first domain. An entity associated with the first domain may be obtained. An analysis of a second plurality of documents may be initiated to identify co-occurrences of mentions of the obtained entity and one or more of the plurality of description phrases, and contexts associated with each of the co-occurrences of the mentions and description phrases, in each one of the second plurality of documents. A description tag association between the obtained entity and one of the description phrases may be determined, based on an analysis of the identified contexts.

Tagging entities with descriptive phrases

An approach is described for generating synonyms to supplement at least one information item, such as, in one case, a set of related items. The approach can involve an expansion phase, a clean-up phase, and a reduction phase. In the expansion phase, the approach identifies, for each related item, a set of initial synonym candidates. In the clean-up phase, the approach removes noise from the set of initial synonym candidates (if such noise exists), to provide a set of filtered synonym candidate items. In the reduction phase, the approach ranks and applies a threshold (or thresholds) to the set of filtered synonym candidate items, to generate, for each information item, a set of selected synonyms. The approach uses query log data at various points in its operation. The selected synonyms can be used to improve the effectiveness of user searches.

Generating synonyms based on query log data

A similarity analysis framework is described herein which leverages two or more similarity analysis functions to generate synonyms for an entity reference string r. The functions are selected such that the synonyms that are generated by the framework satisfy a core set of synonym-related properties. The functions operate by leveraging query log data. One similarity analysis function takes into consideration the strength of similarity between a particular candidate string sand an entity reference string reven in the presence of sparse query log data, while another function takes into account the classes of sand r. The framework also provides indexing mechanisms that expedite its computations. The framework also provides a reduction module for converting long entity reference strings into shorter strings, where each shorter string (if found) contains a subset of the terms in its longer counterpart.

Robust discovery of entity synonyms using query logs

A set expansion system is described herein that uses general-purpose web data to expand a set of seed entities. The system includes a simple yet effective quality metric to measure the expanded set, and includes two iterative thresholding processes to rank candidate entities. The system models web data sources and integrates relevance and coherence measurements to evaluate potential set candidates using an iterative process. The system uses general-purpose web data that is not specific to the given seeds. The system defines quality of the result set as the sum of two component scores: the relevance of a set of entities that measures their similarity with the given seeds, and the coherence of the set of entities produced which is how closely the entities in the set are related to each other. Based on this quality measure, the system develops a class of iterative set expansion processes.

Iterative set expansion using samples

A system has a processor coupled to access a document database that indexes keywords and instances of entities having entity types in a plurality of documents. The processor is programmed to receive an input query including one or more keywords and one or more entity types, and search the database for documents having the keywords and entities with the entity types of the input query. The processor is programmed for aggregating a respective score for each of a plurality of entity tuples across the plurality of documents. The aggregated scores are normalized. Each respective normalized score provides a ranking of a respective entity tuple, relative to other entity tuples, as an answer to the input query. The processor has an interface to a storage or display device or network for outputting a list including a subset of the entity tuples having the highest normalized scores among the plurality of entity tuples.

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Redmond, WA, United States of America

Tao Cheng