Microsoft Technology Licensing, LLC

Venkatesh Ganti

Surajit J Chaudhuri

Kaushik Shriraghav

Kaushik Chakrabarti

Sanjay Agrawal

Shriraghav Kaushik

Dong Xin

Arvind Arasu

Rajeev Motwani

Rohit Ananthakrishna

Xin Dong

Vivek Ravindranath Narasayya

Rahul Kapoor

Kris Kuppuswamy Ganjam

Bee-Chung Chen

Zhimin Chen

Luis Gravano

Yevgeny E Agichtein

Gunjan Jha

Anish Das Sarma

Yuri Siradeghyan

Bee Chung Chen

Surajt Chaudhuri

Vassilakis Theodore

Embodiments for identifying an entity synonym of an entity are described. A query log is stored in a database located on at least one computing device. A candidate generation module can select a candidate query in the query log that shares a click on a URL with the entity. A correlated tag module can generate a set of phrase-tag pairs for the entity and the candidate query and measure a mutual information value for each phrase-tag pair. A candidate filtering module can determine a click similarity value between the candidate query and the entity based on a set of URLs selected in the search engine results and a tag similarity value based on the mutual information values. A candidate query is selected as an entity synonym if the click similarity value and the tag similarity value are greater than predetermined thresholds respectively.

Identifying entity synonyms

A set of documents is filtered for entity extraction. A list of entity strings is received. A set of token sets that covers the entity strings in the list is determined. An inverted index generated on a first set of documents is queried using the set of token sets to determine a set of document identifiers for a subset of the documents in the first set. A second set of documents identified by the set of document identifiers is retrieved from the first set of documents. The second set of documents is filtered to include one or more documents of the second set that each includes a match with at least one entity string of the list of entity strings. Entity recognition may be performed on the filtered second set of documents.

Scalable lookup-driven entity extraction from indexed document collections

Identifying synonyms of entities using a collection of documents is disclosed herein. In some aspects, a document from a collection of documents may be analyzed to identify hit sequences that include one or more tokens (e.g., words, number, etc.). The hit sequences may then be used to generate discriminating token sets (DTS's) that are subsets of both the hit sequences and the entity names. The DTS's are matched with corresponding entity names, and then used to create DTS phrases by selecting adjacent text in the document that is proximate to the DTS. The DTS phrases may be analyzed to determine whether the corresponding DTS is synonyms of the entity name. In various aspects, the tokens of an associated entity name that are present in the DTS phrases are used to generate a score for the DTS. When the score at least reaches a threshold, the DTS may be designated as a synonym. A list of synonyms may be generated for each entity name.

Identifying synonyms of entities using a document collection

This patent application relates to taxonomy editing. One implementation involves a taxonomy editor configured to generate a visual representation of a taxonomy associated with a set of scientific papers. The taxonomy editor includes a properties module configured to identify properties relating to an individual node of the taxonomy and a statistics module configured to determine trends relating to the individual node. The taxonomy editor further includes a similarity module configured to evaluate keyword similarity relative to individual scientific papers associated with the individual node. The taxonomy editor also includes a suggestion module configured to utilize the properties, the trends and the keyword similarity to identify potential modifications to the taxonomy. The taxonomy editor is further configured to present at least some of the potential modifications, the properties, the trends, and the keyword similarity concurrently with the visual representation of the taxonomy.

Taxonomy editor

A deduplication algorithm that provides improved accuracy in data deduplication by using aggregate and/or groupwise constraints. Deduplication is accomplished using only as many of these constraints that are satisfied rather than be imposed inflexibly as hard constraints. Additionally, textual similarity between tuples is leveraged to restrict the search space. The algorithm begins with a coarse initial partition of data records and continues by raising the similarity threshold until the threshold splits a given partition. This sequence of splits defines a rich space of alternatives. Over this space, an algorithm finds a partition of the input that maximizes constraint satisfaction. In the context of groupwise aggregation constraints for deduplication all SQL (structured query language) aggregates are allowed, including summation.

Leveraging constraints for deduplication

Architecture for finding related entities for web search queries. An extraction component takes a document as input and outputs all the mentions (or occurrences) of named entities such as names of people, organizations, locations, and products in the document, as well as entity metadata. An indexing component takes a document identifier (docID) and the set of mentions of named entities and, stores and indexes the information for retrieval. A document-based search component takes a keyword query and returns the docIDs of the top documents matching with the query. A retrieval component takes a docID as input, accesses the information stored by the indexing component and returns the set of mentions of named entities in the document. This information is then passed to an entity scoring and thresholding component that computes an aggregate score of each entity and selects the entities to return to the user.

Finding related entity results for search queries

Example-driven creation of record matching queries. The disclosed architecture employs techniques that exploit the availability of positive (or matching) and negative (non-matching) examples to search through this space and suggest an initial record matching query. The record matching task is modeled as that of designing an operator tree obtained by composing a few primitive operators. This ensures that record matching programs be executable efficiently and scalably over large input relations. The architecture joins records across multiple (e.g., two) relations (e.g., R and S). The architecture exploits the monotonicity property of similarity functions for record matching in the relations, in that, any pair of matching records have a higher similarity value than non-matching record pairs on at least one similarity function.

Example-driven design of efficient record matching queries

The described implementations relate to data analysis, such as membership checking. One technique identifies candidate matches between document sub-strings and database members utilizing signatures. The technique further verifies that the candidate matches are true matches.

Membership checking of digital text

Entities, such as people, places and things, are labeled based on information collected across a possibly large number of documents. One or more documents are scanned to recognize the entities, and features are extracted from the context in which those entities occur in the documents. Observed entity-feature pairs are stored either in an in-memory store or an external store. A store manager optimizes use of the limited amount of space for an in-memory store by determining which store to put an entity-feature pair in, and when to evict features from the in-memory store to make room for new pairs. Feature that may be observed in an entity's context may take forms such as specific word sequences or membership in a particular list.

Leveraging cross-document context to label entity

A machine implemented system and method that efficiently facilitates and effectuates exact similarity joins between collections of sets. The system and method obtains a collection of sets and a threshold value from an interface, and based at least in part on an identifiable similarity, such as an overlap or intersection, between the collection of sets the analysis component generates and outputs a candidate pair that at least equals or exceeds the threshold value.

Efficient exact set similarity joins

The subject disclosure pertains to a class of object finder queries that return the best target objects that match a set of given keywords. Mechanisms are provided that facilitate identification of target objects related to search objects that match a set of query keywords. Scoring mechanisms/functions are also disclosed that compute relevance scores of target objects. Further, efficient early termination techniques are provided to compute the top K target objects based on a scoring function.

Efficient evaluation of object finder queries

Architecture that provides a data profile computation technique which employs key profile computation and data pattern profile computation. Key profile computation in a data table includes both exact keys as well as approximate keys, and is based on key strengths. A key strength of 100% is an exact key, and any other percentage in an approximate key. The key strength is estimated based on the number of table rows that have duplicated attribute values. Only column sets that exceed a threshold value are returned. Pattern profiling identifies a small set of regular expression patterns which best describe the patterns within a given set of attribute values. Pattern profiling includes three phases: a first phases for determining token regular expressions, a second phase for determining candidate regular expressions, and a third phase for identifying the best regular expressions of the candidates that match the attribute values.

Key profile computation and data pattern profile computation

The invention concerns a detection of duplicate tuples in a database. Previous domain independent detection of duplicated tuples relied on standard similarity functions (e.g., edit distance, cosine metric) between multi-attribute tuples. However, such prior art approaches result in large numbers of false positives if they are used to identify domain-specific abbreviations and conventions. In accordance with the invention a process for duplicate detection is implemented based on interpreting records from multiple dimensional tables in a data warehouse, which are associated with hierarchies specified through key&#x2014;foreign key relationships in a snowflake schema. The invention exploits the extra knowledge available from the table hierarchy to develop a high quality, scalable duplicate detection process.

Detecting duplicate records in databases

The subject disclosure pertains to a powerful and flexible framework for record matching. The framework facilitates design of a record matching query or package composed of a set of well-defined primitive operators (e.g., relational, data cleaning . . . ), which can ultimately be executed to match records. To assist design of such packages, a learning technique based on examples is provided. More specifically, a set of matching and non-matching record pairs can be input and employed to facilitate automatic package generation. A generated package can subsequently be transformed manually and/or automatically into a semantically equivalent form optimized for execution.

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Redmond, WA, United States of America

Venkatesh Ganti