Microsoft Technology Licensing, LLC

Abhinai Srivastava

Tarek Najm

Arungunram Chandrasekaran Surendran

Viswanath Vadlamani

Munirathnam Srikanth

Phani Vaddadi

Ying Li

Rajeev Prasad

Lee Wang

Benyu Zhang

Teresa Mah

Jeremy Tantrum

Heng Zhang

Mahbubul Alam Ali

John Midgley

Search result re-ranking is provided by employing a concept graph from a metabase. When a search query is received, a query context of the search query is analyzed to identify dominant concepts for the search query. The dominant concepts are expanded by identifying the dominant concepts within a concept graph and identifying additional concepts having a strong relationship with the dominant concepts within the concept graph. A set of search results for the search query is analyzed to determine strength of relationship of each search result to the expanded concepts. The search results are re-ranked based on the strength of relationship of each search result to the expanded concepts and the strength of relationship of each expanded concept to the dominant concepts.

Re-ranking search results based on lexical and ontological concepts

Systems, methods, and computer-storage media for generating lateral concepts are provided. The system includes a search engine to receive user queries, a storage to store content and its associated categories, and a lateral concept generator. The lateral concept generator is connected to both the search engine and storage. The lateral concept generator selects lateral concepts from categories associated with the content based on similarity scores for the stored content.

Generating and presenting lateral concepts

Electronic documents are analyzed to identify assertions, which are inverted to generate questions that may be answered by the assertions. A document or a corpus of electronic documents may be analyzed to identify entities and relationships among entities within the text of the document(s). Assertions are identified based on the entities and relationships among the entities. Each assertion represents a fact about an entity, and a group of assertions represents a summary of the document or document corpus. The assertions are inverted to generate questions that may be answered by the assertions. The questions may be further analyzed to identify relevant concepts and topics and to cluster the questions around the concepts and topics. A combined graph may also be generated that facilitates traversal among topics, concepts, questions, assertions, document summaries, and documents.

Ontological categorization of question concepts from document summaries

A table of contents is generated for search results in response to a search query by identifying semantic concepts as topics for inclusion within the table of contents. When a search query is received, a search is performed to identify search results. The search results are compared to an ontology of topics to identify relevant topics. Additionally, search results are compared to an ontology of partial topics to identify relevant partial topics, which are named. Independent key-phrases are further generated from search results and identified as key-phrase topics. The identified topics, named partial topics, and key-phrase topics are ranked, and topics are selected for inclusion within a table of contents. A search results page is returned in response to the search query that includes search results and the generated table of contents.

Semantic table of contents for search results

Advertisements are selected for presentation on search result pages and web pages based on phrases generated from lateral concepts and topics identified for the search result pages and web pages. A search query or an indication of a web page is received for which advertisements are to be provided. Lateral concepts and topics are identified based on the search query or content of the web page. The lateral concepts and topics are used as phrases for selecting advertisements from an advertisement inventory. Selected advertisements are provided for presentation on a search results page in response to a search query or on a web page initially identified.

Semantic advertising selection from lateral concepts and topics

A method and system to generate variants, including misspells from query log context usage are provided. Usage context obtained from the query logs is utilized to facilitate similarity determination. A Similarity Graph generation process generates a Similarity Graph, which is transformed to provide variants having varying edit distances. The transformed Similarity Graph is loaded into a hash table and provides query corrections in a search engine or related terms when bidding on keyword in an advertising system.

Generating misspells from query log context usage

Usage context obtained from search query logs is leveraged to facilitate in discovery and/or similarity determination of key search phrases. A key phrase extraction process extracts key phrases from raw search query logs and breaks individual queries into a vector of the key phrases. A Similarity Graph generation process then generates a Similarity Graph from the output of the key phrase extraction process. Information relating to the similarity levels between two key phrases can be employed to restrict a search space for tasks such as, for example, online keyword auctions and the like. Thus, instances can be employed to find frequent misspellings of a given keyword, keyword/acronym pairs, key phrases with similar intention, and/or keywords which are semantically related and the like.

Context-based key phrase discovery and similarity measurement utilizing search engine query logs

A method and system are provided for identifying groups in large-scale networks. The large-scale networks include a collection of nodes and edges that may represent relationships between entities or individuals. The large-scale network is split into a number of fractions satisfying an edge threshold. In turn, the nodes in each fraction are merged to generate one or more clusters based on a specified similarity metric. The large-scale network is recursively split and clustered until distinct groups are identified.

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Abhinai Srivastava