Semantic advertising selection from lateral concepts and topics

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.

1 . One or more computer-readable media storing computer-useable instructions that, when used by one or more computing devices, cause the one or more computing devices to perform a method comprising:
 receiving a search query or an indication of a web page;   identifying one or more lateral concepts based on content identified as being relevant to the search query or content of the web page, wherein each lateral concept is identified as a candidate phrase for advertisement selection purposes;   identifying one or more topics based on the search query or the content of the web page, wherein each topic is identified as a candidate phrase for advertisement selection purposes;   selecting one or more phrases from the identified candidate phrases;   querying an advertisement inventory using the one or more selected phrases to select one or more advertisements; and   providing the one or more advertisements for presentation to a user.   
     
     
         2 . The one or more computer-readable media of  claim 1 , wherein identifying one or more lateral concepts comprises:
 obtaining a first set of content from storage that corresponds to the user query or content of the web page;   identifying a plurality of categories associated with the obtained first set of content; and   selecting a subset of the plurality of identified categories as lateral concepts.   
     
     
         3 . The one or more computer-readable media of  claim 1 , wherein identifying one or more lateral concepts comprises:
 calculating similarity between content in storage and the user query or the content of the web page;   creating a collection of content having a predetermined number of content similar to the user query or the content of the web page;   identifying a plurality of categories that correspond to content in the collection of content; and   selecting several identified categories as lateral concepts.   
     
     
         4 . The one or more computer-readable media of  claim 1 , wherein a search query is received and wherein identifying one or more topics comprises:
 determining if an ontology mapping exists for the search query;   if an ontology mapping exists for the search query, retrieving a first set of topics based on the ontology mapping and adding the first set of topics to a list of topics;   performing a search using the search query to obtain a plurality of search results, each search result corresponding with a document snippet;   receiving at least a portion of the document snippets as a document set for further analysis;   comparing each document snippet in the document set to an ontology of topics;   for each document snippet in which positive topic identification is determined, assigning the document snippet to a corresponding topic and removing (320) the document snippet from the document set;   adding at least one topic identified from the ontology of topics to the list of topics;   comparing each document snippet remaining in the document set to an ontology of partial topics;   for each document snippet in which positive partial topic identification is determined, assigning the document snippet to a corresponding partial topic and removing the document snippet from the document set;   naming at least one partial topic having one or more assigned document snippets;   adding at least one named partial topic to the list of topics;   computing independent key-phrases from document snippets remaining in the document set;   assigning documents to independent key-phrases;   identifying at least one key-phrase topic; and   adding the at least one key-phrase topic to the list of topics.   
     
     
         5 . The one or more computer-readable media of  claim 4 , wherein naming a partial topic comprises:
 identifying occurrences of a partial topic identifier word for the partial topic within one or more document snippets assigned to the partial topic;   extracting words and/or phrases occurring around identified occurrences of the partial topic identifier word within the one or more document snippets;   counting frequency of each extracted word and/or phrase;   selecting a most frequently used word or phrase; and   naming the partial topic using the partial topic identifier and the most frequently used word or phrase.   
     
     
         6 . The one or more computer-readable media of  claim 5 , wherein counting frequency of each extracted word and/or phrase comprises tracking position of each extracted word and/or phrase relative to the partial topic identifier word, and wherein naming the partial topic comprises sequencing the partial topic identifier word and the most frequently used word or phrase based on position information for the most frequently used word or phrase. 
     
     
         7 . The one or more computer-readable media of  claim 4 , wherein computing independent key-phrases from document snippets remaining in the document set comprises:
 generating candidate key-phrases from the document snippets remaining the document set;   evaluating candidate key-phrases for independence;   merging mutually dependent candidate key-phrases; and   identifying a most frequent candidate key-phrase for each group of merged mutually dependent key-phrases.   
     
     
         8 . The one or more computer-readable media of  claim 1 , wherein selecting one or more phrases from the candidate phrases comprises:
 ranking each candidate phrase based on an estimate of an extent to which each candidate phrase will produce advertising revenue; and   selecting the one or more phrases based on ranking;   
     
     
         9 . The one or more computer-readable media of  claim 1 , wherein querying the advertisement inventory using the one or more selected phrases to select one or more advertisements comprises performing an auction process to select the one or more advertisements based on relevance of each advertisement to the one or more phrases and based on monetization factors associated with each advertisement. 
     
     
         10 . The one or more computer-readable media of  claim 1 , wherein a search query is received, and wherein providing the one or more advertisements for presentation to the user comprises providing the one or more advertisements for presentation on a search results page including search results in response to the search query. 
     
     
         11 . The one or more computer-readable media of  claim 10 , wherein the search results page includes the one or more lateral concepts allowing the user to access content associated with the one or more lateral concepts. 
     
     
         12 . The one or more computer-readable media of  claim 11 , wherein the search results page includes the one or more topics in a table of contents allowing the user to select a topic from the one or more topics to view content associated with the selected topic. 
     
     
         13 . The one or more computer-readable media of  claim 1 , wherein an indication of a web page is received, and wherein providing the one or more advertisements for presentation to the user comprises providing the one or more advertisements for presentation on the web page. 
     
     
         14 . A computer system including one or more processors and one or more computer-readable media configured to select and deliver advertisements, the computer system including:
 a phrase generator to generate candidate phrases based on a search query or identified web page, wherein the phrase generator includes a lateral concept generator and a semantic topic engine, wherein the lateral concept generator is configured to select lateral concepts from categories associated with content in storage based on similarity scores for the stored content, wherein the semantic topic engine is configured to identify topics by analyzing the search query or web page with an ontology of topics and with an ontology of partial topics and by generating key-phrase topics, and wherein the lateral concepts and topics are identified as candidate phrases;   a phrase selection component configured to select one or more phrases from the candidate phrases; and   an advertising delivery system including an advertisement selection component and an advertisement delivery engine, wherein the advertisement selection component is configured to query an advertisement inventory using the one or more phrases to select one or more advertisements, and wherein the advertisement delivery engine is configured to deliver the one or more advertisements for presentation to a user.   
     
     
         15 . The computer system of  claim 14 , wherein the advertising delivery system delivers an advertisement for presentation on a search results page that includes search results in response to the search query, and wherein the search results page includes lateral concepts and a table of contents listing the topics. 
     
     
         16 . A computer-implemented executed by a search engine, the computer-implemented method comprising:
 receiving a search query or an indication of a web page;   identifying one or more lateral concepts based on content identified as being relevant to the search query or content of the web page, wherein each lateral concept is identified as a candidate phrase for advertisement selection purposes;   identifying one or more topics based on the search query or the content of the web page, wherein each topic is identified as a candidate phrase for advertisement selection purposes;   selecting one or more phrases from the identified candidate phrases;   querying an advertisement inventory using the one or more selected phrases to select one or more advertisements; and   providing the one or more advertisements for presentation to a user.   
     
     
         17 . The method of  claim 16 , wherein selecting one or more phrases from the candidate phrases comprises:
 ranking each candidate phrase based on an estimate of an extent to which each candidate phrase will produce advertising revenue; and   selecting the one or more phrases based on ranking;   
     
     
         18 . The method of  claim 16 , wherein querying the advertisement inventory using the one or more selected phrases to select one or more advertisements comprises performing an auction process to select the one or more advertisements based on relevance of each advertisement to the one or more phrases and based on monetization factors associated with each advertisement. 
     
     
         19 . The method of  claim 16 , wherein a search query is received, and wherein providing the one or more advertisements for presentation to the user comprises providing the one or more advertisements for presentation on a search results page including search results in response to the search query. 
     
     
         20 . The method of  claim 19 , wherein the search results page includes the one or more lateral concepts allowing the user to access content associated with the one or more lateral concepts and the search results page includes the one or more topics in a table of contents allowing the user to select a topic from the one or more topics to view content associated with the selected topic.