Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Systems, methods, and computer-storage media for generating and providing knowledge content to users utilizing a web architecture that integrates information across data silos through a common, flexible data storage schema, such as a star or snowflake schema, are provided. Data from a content graph, a user activity graph, a social graph, and temporal data as it relates to each of the content graph, the social graph and the user activity graph, is stored in a knowledge content database utilizing the star schema. In this way, data from each of these formerly disparate sources may be accessed from a common, extensible application platform utilizing ontologies and pivot table functionality, thus providing smarter, more comprehensive knowledge in response to received user queries.

Abstract: 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.

Abstract: Systems, methods, and computer-storage media for generating and providing knowledge content to users utilizing a web architecture that integrates information across data silos through a common, flexible data storage schema, such as a star or snowflake schema, are provided. Data from a content graph, a user activity graph, a social graph, and temporal data as it relates to each of the content graph, the social graph and the user activity graph, is stored in a knowledge content database utilizing the star schema. In this way, data from each of these formerly disparate sources may be accessed from a common, extensible application platform utilizing ontologies and pivot table functionality, thus providing smarter, more comprehensive knowledge in response to received user queries.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Graphical user interfaces, methods, and computer-storage media for navigating relationships among entities are provided. The method is executed by a computer system that generates a graphical user interface having entities associated with a query. The query is issued to a search engine that searches multiple sources to locate results. The computer system extracts the entities from the results. In turn, a graph is added to the graphical user interface to group the entities. The graph links the query and the entities and provides a visual overview of the results for the query.

Abstract: 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.

Abstract: Systems, methods, and computer-storage media for identifying dominant concepts are provided. The system includes a search engine connected to various sources, an entity extraction component, a metabase, and a ranking component. The search engine receives a contextual query and provides results in response to the contextual query. The entity extraction component parses the results and identifies entities included in the results. The metabase provides a distance between the entities included in the results and the query terms included in the contextual query. The ranking component ranks the entities based on the provided distance and selects dominant concepts within the results based on the ranks assigned to entities.

Abstract: Advertisements are selected for presentation on a search results page based on the presence or absence of user activity within the search results page. When a search results page is provided to an end user, the search results page is tracked for user activity. If user activity is detected, a user context is identified and new advertisements are selected for presentation on the search results page based on the user context. In some embodiments, the search results page provides search results in clusters, and the user context corresponds with a search result cluster selected by an end user. In some embodiments, dominant concepts in the user context are identified and used to select advertisements. Alternatively, if user activity is not detected within a predetermined period of time, new advertisements are selected based on the absence of user activity within the search results page.

Abstract: Systems, methods, and computer-storage media for presenting answers are provided. The system includes a search engine to receive user queries and contexts, a query understanding component to provide results, and an answer generator to present answers to the user queries. The answers may include a link to a browser that provides a graph, table, or cluster for the results, where nodes of the graph are associated with a confidence level.

Abstract: 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.

Abstract: 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.

Abstract: 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.