Patent Publication Number: US-8122042-B2

Title: Method and system for determining a relevant content identifier for a search

Description:
BACKGROUND 
     An internet or web search engine is a tool designed to search for information on the World Wide Web. Search engine/system providers aim to have a high degree in accuracy in delivering desired search result desired by a user for a given set of search terms. Approaches for ensuring delivery of accurate and desired search results have largely relied on finding search terms within a web page and/or a history of delivering a particular web page to a user with the same search terms. 
     SUMMARY 
     Embodiments of a method, a system and a computer-readable medium for a direct answer for search are disclosed. In an embodiment, a search query is received over a network, one or more answer entities are determined from one or more answer candidate snippets, with an answer candidate snippet having at least a portion of content available over the network for an answer candidate, a content identifier is determined for an answer candidate, a title is tokenized for the content identifier, a comparison is performed between a vector of tokens for the title and a vector of the one or more answer entities, an indicator of the relevance is adjusted for the content identifier in accordance with the comparison, and at least one answer candidate snippet is sent for a response to the search query. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The invention is described by way of example(s) with reference to the accompanying drawings, wherein: 
         FIG. 1  depicts an exemplary system for implementing an intelligent search system. 
         FIG. 2  depicts an exemplary system for implementing an intelligent search system. 
         FIG. 3  depicts an exemplary flowchart for an implementation of an intelligent search system. 
         FIG. 4  depicts an exemplary flowchart for an implementation of determining confidence in an answer. 
         FIG. 5  depicts an exemplary flowchart for an implementation of determining confidence in an answer. 
         FIG. 6A  depicts an exemplary flowchart for an implementation of determining confidence in an answer. 
         FIG. 6B  depicts an exemplary flowchart for an implementation of determining confidence in an answer. 
         FIG. 7  depicts an exemplary flowchart for an implementation of determining confidence in an answer. 
         FIG. 8  depicts an exemplary flowchart for an implementation of determining a relevant content identifier. 
         FIG. 9  depicts an exemplary flowchart for an implementation of determining a relevant content identifier. 
         FIG. 10A  depicts an exemplary flowchart for an implementation of determining a relevant content identifier. 
         FIG. 10B  depicts an exemplary flowchart for an implementation of determining a relevant content identifier. 
         FIG. 11  depicts an exemplary Interface in accordance with an embodiment of an intelligent search system. 
         FIG. 12  depicts an exemplary Snippet in accordance with an embodiment of an intelligent search system. 
         FIG. 13  depicts an exemplary block diagram for an architecture of a computer system. 
     
    
    
     DETAILED DESCRIPTION 
     Embodiments of the present invention are directed toward intelligent search systems, methods, and computer readable mediums. Intelligent search systems may attempt to provide more relevant search results in response to a search query. In one or more embodiments, the intelligent search system may provide a direct answer, such as a text statement with an answer, in response to an explicit or implicit question determined from a search query. As used herein, a search system, method, and/or a computer readable medium with instructions to provide an answer in response to a search query may be referred to as an implementation of “direct answer from search” (DAFS). The answer to a search query may be presented instead or in addition to search results. Search results may contain a content identifier for the answer. A content identifier is a reference or link to content that is relevant to may be relevant to the search query. The content identifier may be a Uniform Resource Locator (URL). For example, a search query that contains “When did Columbus discover America?” may return a text answer “Columbus discovered America in 1492.” Similarly, the same question may be implied with a search query that contains terms “America, Columbus, discover, year” and the same answer of “Columbus discovered America in 1492” may be returned. The content identifier(s) that identified the answer may be presented with the answer. 
     One or more embodiments of the present invention are directed toward determining confidence in an answer that may be presented in response to a search query with an intelligent search system. Confidence may be a score or a percentage that indicates a level of certainty that an answer candidate is the answer desired with a given search query. 
     One or more embodiments of the present invention are directed toward determining a relevant content identifier to present in response to a search query for an intelligent search system. 
       FIG. 1  depicts an exemplary system for implementing an intelligent search. A network  100  is a group of two or more connected computer systems to enable the exchange of data. The network  100  may employ the use of any number of protocols, standards, or formats to enable communication between networked computer systems. An internet is a group of two or more connected computer networks or portions of networks that enable communication between computer systems on accessible networks. As such, a computer system that is available to communicate with another computer system over a network may communicate over a local area network (LAN), a wide area network (WAN), world wide web, extranet, intranet, internet, or any other type of network, and the term “network” may be used interchangeably throughout. The network  100  may allow for communication between the elements of  FIG. 1  including, but not limited to, Website A  102 , Website B  104 , Website C  106 , Client Computer System  108 , Web Crawler  110 , Context Data  112 , Indexer  114 , Search Database  116 , Reference Site Index Data  124 , Famous People  125 , Local Data  128 , Search Engine  118 , Interface  120 , Intelligent Search System  122 , and Server Computer System  130 . 
     As depicted in  FIG. 1 , Website A  102  may have data or content available over the network. Data and content available over the network may be in the form of text, hypertext markup language (HTML) pages, extensible markup language (XML), pages generated by scripts or application servers, and any other type of data available over the network. A website may have one or more web pages and the web pages may have a common root URL. Web page is used broadly throughout to include a static markup language file (e.g. HTML page), a generated page, a portion of a web page, or any portion of a page or data that is available over a network. Although a web server is not depicted, a client may request that a web server provide access to Website A  102 , and the web server may provide access to the data that allows for presentation of a webpage of Website A  102 . For example, a webpage of Website A  102  may be data, such as a HTML page, that may transmitted to a client at a Client Computer System  108  in response to a request, such as an Hypertext Transfer Protocol (HTTP) request, received at a web server. 
     A Crawler  110  is a software program or script that locates and retrieves content available on the network. The Crawler  110  may follow links to websites and webpages available on the network to ensure that content for one or more websites is captured. Website A  102 , Website B  104 , and Website C  106  may be crawled by a Crawler  110 . The Crawler  110  may be configured to indiscriminately crawl the web or may be given priorities for collecting content available on the network. The Crawler  110  may collect a copy of the content, changes to content since the data was last captured, information on content available, and/or check to ensure the website is operational. Optionally, a copy of content collected by the Crawler  110  may be stored in a Collected Data Store  112 . The Collected Data Store  112  may be any type of database. A database is any collection of data that may be stored and queried for retrieval of the stored data. 
     After data has been collected, an Indexer  114  may index or categorize the data. The Indexer  114  may be software or a script that indexes and/or categorizes the data collected by the Crawler  110 . The indexed data optionally may be stored in a Search Database  116 . A Search Engine  120  may query a Search Database  118  to retrieve indexed data upon request. 
     A Client Computer System  108  may have client software, such as a web browser, that may send a request through an Interface  120  to Intelligent Search System  122  and/or the Search Engine  118 . The Intelligence Search System  122  and Search Engine  118  are depicted as being part of a Server Computer System  130  and the Server Computer System  130  receives requests from a client over the network or with a direct connection to the Server Computer System  130 . Although depicted as a Client-Server model, those skilled in the art will recognize that other models are available for implementation of an intelligent search system. In one or more embodiments, the Intelligent Search System  122  may send queries through the Search Engine  118  to the Search Database  116 . Optionally, the Search Engine  118  may query Reference Site Indexed Data  124 , Famous People Data  126 , Local Data  128  or any other collected data. The Reference Site Indexed Data  124 , Famous People Data  126 , and Local Data  128  may be fed to the system by a third-party or similarly collected by the Crawler  110 . 
     The Client Computer System  108  may receive a response from the Search Engine  118  and/or Intelligent Search System  122 . As discussed above, the Intelligent Search System  122  may respond with a text answer to a search query in addition to search results. The Intelligent Search System  122  may transmit data for display of a webpage on the Client Computer System  108  with the search results and the text answer. 
     Although the elements of the search system are depicted as being part of one server, those skilled in the art will recognize that the elements of the search system may be on multiple computer systems. 
       FIG. 2  depicts an exemplary system for implementing an intelligent search system. A Client Computer System  200  interacts with a DAFS System  202 , an intelligent search system, over the Network  100 . As will be appreciated by those skilled in the art, the functional elements of the DAFS System  202  as illustrated in  FIG. 2  are provided as an example, and various implementations of the system may have subsets of the functional elements and/or elements may be distributed among various computer systems (as indicated by the dashed line). In an embodiment, the DAFS System  202  may be a server with instructions executing on a computer system and servicing requests received from a client executing on the Client Computer System  200  over a Network  100 . In another embodiment, the DAFS System  202  may be stored and executed as a stand-alone application on a computer system. 
     A Client Computer System  200  may interact with the DAFS System  202  using an Interface  204 . The Interface  204  may be a webpage, an application programming interface (API) or any other type of interface that allows for communicating a request to the DAFS System  202 . Embodiments may view the Interface  204  as a front end for the DAFS System  202  and the back end may consist of the elements of the DAFS System  202  that handle responding to the search query. In one or more embodiments, the DAFS system  202  may be a web service which provides an API that can be accessed locally and over a network, such as the internet, and allows for execution of instructions on a remote system hosting the requested service. The Client Computer System  200  uses the Interface  204  to request a search query and input Search Terms  206  for the search query. For example, the Client Computer System  200  may use a client, such as a browser, to request the web page Interface  204  for the DAFS System  202  and input a request for a search query with Search Terms  206  and submit the request to the DAFS System  202 . Embodiments may provide a web page interface with a form text input box that allows for the input of text search terms. Other embodiments may restrict search terms permitted to be inputted, such as with radio buttons or display of any other limitation on the selection of search terms. 
     As indicated above, the Search Terms  206  may explicitly or implicitly indicate a question and the DAFS System  202  may provide an answer for the question indicated by the Search Terms  206 . The Question Expansion Module  208  may compare Search Terms  206  in a search query to a question pattern in order to determine the type of question that the query may represent. In one or more embodiments, a question pattern may have one or more question templates or grammars that enable identification of a type of question indicated by the Search Terms  206 . In a preferred embodiment, a pattern (e.g. question pattern, answer pattern) is a regular expression that can be used to match a question in Search Terms or an answer within text. For example, the Question Expansion Module  208  may parse the search query by tokenizing (e.g. identifying specific words in the Search Terms) the Search Terms  206  and using a grammar to identify a question or set of questions that the Search Terms  206  represent. By way of further example, each a question pattern may represent one or more questions, such as “when was x born” or “what is the birthdate of x” and the question pattern would allow for the identification of the similar questions. Questions that may be categorized together for question patterns may be determined manually and/or dynamically determined from prior interactions with the DAFS System  202 . A tokenizer or lexer may identify tokens “when, was, born” and recognize “Madonna” as a name token, and a grammar may be used to identify a particular order of the Search Terms  206  that indicate a question, such as “when was x born.” In some embodiments, a comparison between the tokens identified and a set of tokens identified with a question pattern may indicate the question posed by the Search Terms  206 . 
     Optionally, the Question Analysis Module  210  may identify a type or classification of answer that is expected from a question. The classification of the answer expected may be determined from a question pattern itself. For example, interrogative words within a question pattern and/or Search Terms may be used to classify an answer for the Search Terms  206  (e.g. “Who” may indicate a person is expected as an answer, “When” may indicate a date is expected as an answer, “Where” may indicate that a location is expected as an answer). Similarly, adjectives may indicate an answer. By way of example, the adjectives after “how” may be used to classify the category of the numeric value (e.g. “much” may indicate a price or other amount is expected as an answer, “tall” and “high” may indicate a height is expected as answer, “large” and “big” may indicate a size is expected as answer). The nouns in a search query may indicate an answer (e.g. “time” after the word “what” may indicate a time is expected as an answer). Occupational words may be used to identify the type of answer expected (e.g. “president” may indicate a president is expected as an answer). Statistics may also be used to determine the answer expected from a set of Search Terms  206 . A generic machine learning system, such as dbacl, may be used to determine the answer expected from a set of Search Terms  206 . Those skilled in the art will recognize that there may be a variety of ways to identify an answer expected from a set of Search Terms  206 . 
     The Rewrite Generation Module  212  may be used to generate a variety of queries for Search Terms  206  inputted with the original search query. Embodiments may rely on question patterns to determine similar queries for a set of Search Terms  206 . For example, continuing with the above example, Search Terms of “When was Madonna born” may allow for the generation of a query with Search Terms of “What is the birth date of Madonna.” 
     One or more queries may then be submitted by the Information Retrieval Module  214 . Queries may be submitted to one or more databases or data stores to retrieve query results. The query results may indicate answer information for the one or more queries determined by the Rewrite Generation Module  212 . For example, one or more queries may be sent to databases for Indexed Data  216 , Reference Site Indexed Data  218 , and Behavioral Data  220 . Indexed Data  216  may be data that has been retrieved by crawling the internet and/or provided by a third party and categorized for information retrieval. In one or more embodiments, reference site data that is indexed, illustrated with Reference Site Indexed Data  218 , may be queried. Examples of reference data that may be indexed and search are Wikipedia® and Merriam-Webster Online Dictionary copyright©. Behavioral Data  220  may also be information on behavior or use of the system by users. The behavioral data may include, but is not limited to, interactions by a user within a session with the system, interactions by multiple users with the system, interactions with the system that indicate popularity of queries, answers, links, and data, and user click popularity for URLs. An example of behavioral data is provided in U.S. Pat. No. 7,181,447, entitled “Methods and Systems for Conceptually Organizing and Presenting Information,” filed May 24, 2004, hereby incorporated by reference. 
     Snippets  222  may be determined from the query results returned to the Information Module  214 . Snippets  222  may be any information about data or content available over the network. A snippet may include a portion of a piece of content available over the network. For example, a Snippet  222  may include, but is not limited to, a title for a web page, a description of the web page, a content identifier (e.g. a URL) to locate the web page, and content available on the web page. An Answer Identification Module  224  may be used to identify the answer within one or more Snippet(s)  222 . An answer pattern may be used to identify the answer within a snippet. The answer pattern may include, but is not limited to, a lexer, a parser, a grammar, a script, or any combination thereof. 
     An Answer Clustering Module  226  may cluster the Snippet(s)  222  into groups of Snippet(s)  222  that may identify similar or nearly the same answer. A cluster of Snippet(s)  222  may be viewed as a set of Snippet(s)  222  for an answer candidate and the set or a portion of the set of Snippet(s)  222  may be presented with the answer candidate in response to the originally submitted search query. An answer candidate is a potential answer for the originally search submitted query. The answer candidates may be ranked and one or more answers may be selected from the answer candidates for presentation in response to the originally submitted search query. 
     An Answer Relevance Module  228  may be used to determine the best URL within the answer candidates. A Confidence Module  230  may be used to determine the confidence in the answers to be presented to the user. The Confidence Module  230  may rank and/or rerank the answer candidates for presentation. A Presentation Module  232  allows for presentation of the answer(s) returned by the DAFS System  202 . The Presentation Module  232  may generated and/or enable the transmission of data that allows for presentation of a web page on a client executing on the Client Computer System  200 . The Presentation Module  232  may enable a thin client, a thick client, and/or a stand-alone application to execute on a Client Computer System  200 . In one or more embodiments, the Presentation Module  232  may allow streaming of data for presentation of the answers to the client, such that all data for presentation of the answer to the user does not have to be downloaded to the Client Computer System  200  to enable display of answers. 
       FIG. 3  depicts an exemplary flowchart for an implementation of an intelligent search system. Initially, a query may be received by a DAFS system  202  using an interface ( 300 ). The query may be a set of Search Terms  206  that is submitted in a request from a Client Computer System  200  to the DAFS system  202 . For example, a client, such as a browser, may be executing on the Client Computer System  200  and allow for sending an HTTP request using a web page interface. For clarity&#39;s sake, the query submitted initially to begin a search may be referred to as the “original search query” throughout. 
     Next, one or more question patterns may be retrieved using the Search Terms  206  ( 302 ). The one or more question patterns may then be compared to the original search query ( 304 ). The Search Terms  206  may serve as a guide for which question patterns may be retrieved. For example, Search Terms  206  of “Madonna, birth, of” may indicate that a question pattern should be retrieved with questions: “when is x&#39;s birthday,” “what is the birthdate of x,” and “when was x born.” Continuing with the example, the Search Terms  206  “birth of Madonna” may also indicate that a question pattern with questions: “when did x give birth,” and “when was x&#39;s child born.” 
     A determination is made whether the original search query is supported by the DAFS System  202  ( 306 ). The comparison of the original search query to the retrieved question patterns may indicate if the original search query may be supported by the DAFS System  202 . If the DAFS System  202  does not support providing an answer for the original search query, then a Search Database  116  may be queried ( 308 ). 
     By way of example, querying a Search Database  116  may include a search with the original search query against an indexed data may be performed with the search terms in the original search query. Although depicted as a Search Database  116 , those skilled in the art will recognize that any number of queries and databases may be used to return search results for the original search query. Next, a determination is made as to whether search results were returned by searching with the original search query ( 312 ). If search results were not returned from querying the Search Database  116 , then the search ends. Optionally, the Presentation Module  232  may indicate that the search was unsuccessful. Alternatively, the search results may be sent to the Presentation Module  232  for presentation on the Client Computer System  200 . 
     Continuing with  FIG. 3 , if the DAFS System  202  does support the original search query ( 306 ), then the Search Database may optionally be queried in parallel with performance of the DAFS intelligent search ( 316 ). As described in detail above, the Search Database may be queried with the original search query ( 316 ). Depending on whether the query result ( 318 ) contains search results ( 320 ), the search results may be presented with the Presentation Module ( 322 ). If search results are not returned, the query search of the Search Database  116  may end. Those skilled in the art will recognize that the search with the DAFS System  202  may still be executing even though the query of the Search Database  116  executing in parallel has ended. Although depicted as two processes that execute in parallel, those skilled in the art will recognize that the searches may be performed in any order preferred. 
     Continuing with  FIG. 3 , an entity may be determined from a Search Term  206  ( 324 ) and the entity may be added to a set of answer entities determined from Search Terms  206  ( 324 ). An entity may be a key term, topic, category, or keyword. Next, a determination is made as to whether there are more Search Terms  206  ( 326 ), and if there are more search terms, then entities are determined from the Search Terms  206  ( 324 ) until there are no more Search Terms  206  and/or enough entities have been determined for the DAFS System  202 . 
     Alternatively, when a search for entities has been performed for the Search Terms  206  ( 326 ), a determination is made as to whether there are similar or nearly similar queries for the original search query ( 328 ). The question patterns may indicate queries that are similar to the original search query. The original search query may be rewritten and added to a set of rewritten queries for the DAFS search ( 330 ). For example, an original search query with Search Terms  206  “Madonna, birth, of” may allow for rewrites of the original search query of “when is Madonna&#39;s birthday,” “when was Madonna born,” and “what is Madonna&#39;s birth date.” If there are more similar queries for a query as indicated by a query pattern ( 332 ), then the original search query may be rewritten and added to a set of rewritten queries ( 330 ). If all similar queries for a query pattern ( 332 ) have been added to the set of rewritten queries, then a determination is made as to whether there are more question patterns ( 334 ). If there are more question patterns, then similar queries are determined for the original search query ( 328 ), the original search query is rewritten ( 330 ) for each similar query ( 332 ) which may be determined by the query pattern. 
     Alternatively, if there are no more query patterns ( 334 ), then the original search query and the set of rewritten queries are sent as query requests to the database ( 336 ). The original search query and set of rewritten queries may be sent as a query request to a database by the Information Retrieval Module  214 . If the Information Retrieval Module does not return query results ( 338 ), then the DAFS search may end. The query results may be returned in the form of one or more Snippets  222 . Optionally, the Presentation Module  232  may indicate that no DAFS search results were found. Alternatively, if Snippet(s)  222  were returned by the Information Retrieval Module  214  ( 338 ), then the DAFS search may continue with  FIG. 3B . 
     Continuing to  FIG. 3B , answer patterns may be determined ( 340 ) to extract the answer from a Snippet  222  ( 342 ). In one or more embodiments, the query used to query the database for query results by the Information Retrieval Module  214  may be used to determine the answer patterns that can be used to locate an answer within the text included or accessible with a Snippet  222 . Embodiments may use a set of words to serve as an answer pattern and a search within the text of accessible with a given Snippet  222  may be performed to find the set of words within the text. For example, for a query that contains terms “when was x born”, the answer pattern may be “[x] was born on [Month] [day] [comma] [year]” (where x is a name, month is a month, day is a day possible within the month, comma is a “,” and year is a possible year). The answer pattern may be used to directly match a sentence within the text accessible with a Snippet  222  by parsing the text accessible with the Snippet  222 . Text that is accessible with the Snippet  222  may be included within the Snippet  222 , accessible by retrieving data with a content identifier for the Snippet  222  or any combination thereof. In one or more embodiments, the answer pattern may be used as a guide and the sentence fragment provided with the answer pattern may not need to be matched exactly. Next, a determination is made as to whether an answer candidate can be obtained with a Snippet  222  and answer pattern ( 344 ). An answer candidate is a potential answer for the original search query. If an answer candidate ( 344 ) cannot be determined from the Snippet  222  with the answer pattern ( 344 ), then a determination is made as to whether another answer pattern is available ( 352 ) and the process to find an answer within the Snippet  222  is repeated. If an answer candidate is determined from a Snippet  222  ( 344 ), then the Snippet may optionally be clustered or grouped with Snippet(s)  222  that had the same answer. To cluster the Snippet  222  with the answer candidate, a search is performed for the existence of the cluster or group with a similar answer ( 346 ). If a cluster does not exist ( 346 ), then an answer candidate cluster ( 348 ) is created with the Snippet  222  added to the new cluster, and a determination is made as to whether there are more answer patterns ( 352 ). Alternatively, the Snippet  222  may be added to the cluster for the answer candidate ( 350 ), and a determination is made as to whether there are more answer patterns ( 352 ). If there are more answer patterns, then the process is repeated until all answer patterns are exhausted. In other embodiments, the process for determining an answer from a Snippet  222  may end after an answer is found in a Snippet  222 . 
     Alternatively, if there are no more answer patterns ( 352 ), then a determination is made as to whether there are more Snippet(s)  222  ( 354 ). The process for finding an answer with answer patterns is then repeated for the next Snippet  222  ( 340 ). If the Snippets  222  needed for determining an answer have all been processed, then the answer candidates are ranked ( 356 ). As previously described, the Snippet(s)  222  that produces the same or nearly the same answer may have been clustered. Next, a determination is made as to the relevant content identifier for the answer candidates ( 358 ) and a determination is made as to the confidence in the answer ( 360 ) to be presented to the user. 
       FIG. 4  depicts an exemplary flowchart for an implementation of determining confidence in an answer. As indicated above, confidence in an answer may be represented with a score and/or a percentage that indicates a level of certainty that an answer candidate is the answer desired with a search query. The confidence for an answer candidate may indicate that the answer candidate should be presented in response to a search query. Confidence may be determined by comparing answer candidates to other answer candidates. Confidence may also be determined for a Snippet for an answer candidate as compared to other Snippets identified with a search query. Those skilled in the art will recognize that the level of granularity for confidence is an implementation detail. For example, confidence can be determined for an answer candidate using all Snippet(s)  222  for the answer candidate (e.g. all Snippets included in the cluster for the answer candidate) and/or a particular Snippet  222  (e.g. a representative Snippet for the cluster) for an answer candidate within a cluster. Similarly, confidence for an answer candidate may be determined for a particular Snippet  222  as compared to other Snippets within the cluster and/or confidence for an answer candidate may be determined for a particular Snippet  222  as compared with all Snippet(s) identified with a search query. 
     Initially, a DAFS Computer System  202  may receive a set of answer candidate Snippet(s) for a search query ( 400 ). As indicated above, a Snippet  222  may be any information about data or content available over the network. For example, a Snippet  222  may include, but is not limited to, a title for a web page, a description of the web page, a content identifier (e.g. a URL) to locate the web page, and content available on the web page. The content available on the web page may be a sentence, phrase, and/or entity (e.g. keyword) provided on the web page. The set of answer candidate Snippet(s)  222  may be a cluster of Snippet(s)  222  identified for a particular answer candidate. The set of answer candidate Snippet(s)  222  for a search query may include Snippet(s)  222  returned with the original search query as well as rewritten queries. In other embodiments, the set of answer candidate Snippet(s)  222  may include a cluster for determined for a single query. 
     The Confidence Module  230  may receive the set of answer candidate Snippets  222  from any module within the DAFS system  228 . Confidence may be performed after clustering and/or directly after identification of Snippets  222  from the Information Retrieval Module  214 . Confidence determination may be a process that is executed in parallel with other processes within the DAFS system  202 . 
     Continuing with  FIG. 4 , one or more answer entities within an answer candidate Snippet  222  are determined ( 402 ). An answer entity is a keyword and/or phrase that may be found within the Snippet  222 .  FIG. 12  provides an example of a Snippet in accordance with an implementation of a DAFS System. Answer entities in  FIG. 12  may be “Uncle Tom&#39;s Cabin” from the title, “American History” from the description, “slayery” and “slayery_toms_cabin” from the URL, “1852” and “Harriet Beecher Stowe,” “wrote,” and “Uncle Tom&#39;s Cabin” from the content, etc. The answer entity may be an original Search Term  206 , a topic, a category, a word, or phrase that is within the answer candidate Snippet  222 . For example, in  FIG. 12 , the Search Terms  206  “who wrote Uncle Tom&#39;s Cabin” may be found in the content, title, and URL of the Snippet of  FIG. 12 . The answer candidate Snippet  222  may be a Snippet  222  from the cluster of Snippets (e.g. a Snippet that represents the cluster). Alternatively, the answer entities may be determined for one or more Snippets for a cluster depending on the granularity desired for determining the confidence for an answer. For the remainder of the description of confidence, one answer candidate Snippet  222  will be described for confidence although those skilled in the art could use any number of Snippets  222  to determine the one or more answer entities for Snippets  222 . 
     Next, a frequency of the determined one or more answer entities found within the set of answer candidate Snippet(s) for the search query is determined ( 404 ). Continuing with the example of  FIG. 12 , a frequency that answer entities (e.g. “Uncle Tom&#39;s Cabin,” “1852,” “Harriet Beecher Stowe,” etc.) in a set of answer candidate Snippets for a search query may be determined. 
     A confidence score for the answer candidate in accordance with the frequency of the one or more entities found with the set of answer candidate Snippets may be adjusted ( 406 ). For example, the Snippet of  FIG. 12  may be in a cluster for answer candidate “Harriet Beecher Stowe.” The answer entities in the Snippet of  FIG. 12  may have been present in 80% of a set of answer candidate Snippet(s). The score for answer candidate “Harriet Beecher Stowe” can be adjusted accordingly such that the answer candidate may be presented as an answer. 
       FIG. 5  depicts an exemplary flowchart for an implementation of determining confidence in an answer. Initially, a set of answer candidate Snippet(s)  222  are received for a search query ( 500 ). As indicated above, the set of answer candidate Snippet(s) may be a cluster for an answer candidate and the search query may include the original search query and a set of rewritten search queries. Next, one or more answer entities are determined with an answer candidate Snippet ( 502 ). 
     A query request may be sent to a behavioral database ( 504 ). The behavioral database may have interactions with the system by users or by users with a search system. The query request to the behavioral database may be for one or more related search queries to the search query. In a preferred embodiment, the behavioral database may group queries that return a URL in search results and the URL is selected by users for the group of queries. For example, if a first query “who wrote Uncle Tom&#39;s Cabin” and a second query “Uncle Tom&#39;s author” both contain a search result with the URL from  FIG. 12 , and users of a search system selected the URL from  FIG. 12  with both the first and second query, then the two queries may be grouped for the URL. In one or more embodiments, the first and second queries may be stored in a table of a database for the URL. Those skilled in the art will recognize that related queries may be determined by other methods. Related queries may be determined by a first query and a second query being submitted by a user within a session and/or by multiple users and/or multiple sessions. 
     Next, a query result may be received from the behavioral database ( 506 ). The query result may have one or more related queries for the search query. One or more search term entities may be determined from the query result with the one or more related search queries ( 508 ). Continuing with the same example, the search term entities “wrote,” “Uncle Tom&#39;s Cabin,” “Uncle Tom&#39;s,” and “author” may be determined from the two queries. The search term entities may be entities (e.g. keywords) found within the two queries. 
     Next, a frequency of the one or more answer entities found within the one or more related search queries may be determined ( 510 ). The entities found within an answer candidate Snippet may be compared to the entities found within the related search queries (e.g. search term entities). 
     Next, the confidence score may be adjusted in accordance with the frequency of the one or more answer entities found with the one or more search term entities ( 512 ). For example, the confidence score may have an initial score of 35/100 and with a frequency support of 20%, the confidence score may be increased. In one or more embodiments, the confidence score may increase for each occurrence of an answer entity for an answer candidate that is similar to a search term entity. 
       FIG. 6A  depicts an exemplary flowchart for an implementation of determining confidence in an answer. Initially, answer entities from an answer candidate Snippet  222  are determined ( 600 ). The answer entities may be determined from an answer candidate Snippet  222  using an answer pattern. As indicated above, any number of Snippet(s)  222  may be selected to represent the answer candidate. For simplicities sake, one answer candidate Snippet  222  will be described in regards to  FIG. 6A-B . 
     Next, a determination is made as to whether to factor in prior search results ( 602 ). If prior search results are to be factored in to a confidence score ( 602 ), then the frequency of answer entities within all Snippet(s)  222  returned from a search query may be determined ( 604 ). The Snippet(s)  222  may include, but are not limited to, Snippet(s)  222  from the original search query and Snippet(s)  222  from query rewrites of the original search query. The confidence score for the answer candidate may be adjusted in accordance with the frequency of answer entities ( 606 ). In one or more embodiments, the confidence score may increase for each occurrence of an answer entity for an answer candidate in a Snippet  222  returned from the search query. Next, a determination is made as to whether there are more answer candidates ( 608 ). If there are more answer candidates ( 606 ), then the process for determining a confidence score for an answer candidate by factoring in prior search results will repeat ( 604 ). 
     Alternatively, if there are no more answer candidates ( 608 ), then a determination is made as to whether to factor in behavioral data ( 610 ). If behavioral data is to be factored in to the confidence score for the answer candidate ( 610 ), then the process described in  FIG. 5  may be performed. Next, a determination is made as to whether there are more answer candidates ( 612 ). If there are more answer candidates ( 612 ), then the process for determining a confidence score for an answer candidate by factoring in behavioral data may repeat by continuing with  FIG. 5 . 
     Alternatively if behavioral data is not to be factored in to the confidence score ( 610 ) and/or there are no more answer candidates for factoring in behavioral data ( 612 ), then a determination is made as to whether to factor in an external database ( 614 ). If a determination is made to factor in an external database ( 614 ), then the external database may be queried ( 616 ), continued on  FIG. 6B . The external database may receive a query with the original search query. The external database may be a third-party that is a reference site, such as Wikipedia® and Merriam-Webster Online Dictionary copyright©. The data may be from an external database but reference site data may be indexed and the indexed reference site data may be used for querying to get terms (e.g. entities) returned with the original search query. 
     Next, the query results from querying the external database are received ( 618 ). The entities within the external database query results may then be compared to the answer candidate answer entities ( 620 ). The confidence score may be adjusted for the answer candidate in accordance with the frequency of answer entities found within the query results from the external database ( 622 ). If there are more answer candidates ( 624 ), then the process for determining a confidence score for an answer candidate by factoring in an external database may repeat ( 612 ). 
     Alternatively if an external database data is not to be factored in to the confidence score ( 616 ) and/or there are no more answer candidates for factoring in behavioral data ( 624 ), then a determination is made as to whether more confidence is needed in an answer ( 626 ). If more confidence in the answer is unnecessary ( 626 ), then the answer candidates are ranked in accordance with their confidence score ( 628 ). One or more answers for the answer candidates may be provided to the Presentation Module  232  for presentation on a Client Computer System  200 . Alternatively, if more confidence in the answer is desired ( 626 ), then further processing of confidence continues with  FIG. 7 . 
       FIG. 7  depicts an exemplary flowchart for an implementation of determining confidence in an answer. A determination is made as to whether to factor in pattern matching ( 700 ). If pattern matching is factored in to the confidence ( 700 ), then a comparison between an answer candidate Snippet  222  to a pattern is performed ( 702 ). The pattern may be an answer pattern and the comparison may indicate how close the answer candidate Snippet  222  is to the answer pattern. The confidence score for the answer candidate may be adjusted in accordance with the comparison ( 704 ). If there are more patterns for comparison ( 706 ), then the process for comparison to a pattern repeats ( 702 ). Alternatively, if there are no more patterns for comparison ( 706 ) and there are more Snippet(s)  222  ( 708 ), then the process may repeat with more answer candidate Snippet(s)  222  ( 702 ). If there are no more Snippet(s) ( 708 ), then a determination is made as to whether to factor in answer candidate size ( 710 ). If answer candidate size is factored in to the confidence for an answer candidate ( 710 ), then the confidence score is adjusted in accordance with the size of the cluster ( 712 ). 
     Alternatively, if the answer candidate size is not factored ( 710 ) or the confidence score has already been adjusted for size ( 712 ), then a determination is made as to whether to factor in machine learning ( 714 ). If machine learning is to be factored in to the confidence ( 714 ), then machine learning may be accounted for in the confidence score for an answer candidate ( 716 ). Machine learning may be performed to track a user&#39;s interactions with the system and prior recorded behavior by one or more users with the original search query may influence the confidence in the answer candidate confidence score. 
     Next, the answer candidates are ranked in accordance with their confidence score ( 718 ). One or more answers for the answer candidates may be provided to the Presentation Module  232  for presentation on a Client Computer System  200 . 
     In one or more embodiments, the age and/or date of the URL associated with the answer candidate Snippet  222  may factor in to the answer candidate Snippet  222  presented with the Presentation Module  232 . 
       FIG. 8  depicts an exemplary flowchart for an implementation of determining a relevant content identifier. An implementation of the DAFS system  202  may be used to determine a relevant content identifier. A relevant content identifier may be a content identifier that is relevant to the search being performed. Initially, one or more answer entities from one or more answer candidate Snippet(s)  222  are determined ( 800 ). For example, answer entities “Uncle Tom&#39;s Cabin,” “Harriet Beecher Stowe,” etc. may be found in the Snippet depicted in  FIG. 12 . Patterns may be used to determine the answer entities within an answer candidate Snippet  222 . Next, a content identifier is determined for an answer candidate ( 802 ). The Snippet  222  may provide or identify a content identifier, such as a URL, for the answer candidate. 
     Next, a title for the content identifier may be tokenized ( 804 ). A title for the content identifier may be designated in a source file for a web page that can be located with the content identifier. A source file is text file that is used for display of a web page. The source file may be written in a markup language and/or generated. The title may be tokenized using a lexer. The lexer may identify the words, numbers, symbols, and phrases within the title and assign a token for each in order to identify the words, numbers, symbols, and phrases. Regular expressions may be used to identify the tokens. 
     Next, a comparison may be performed between a vector of tokens for the title and a vector of the one or more answer entities ( 806 ). The comparison between the tokens and the answer entities may indicate how many tokens and entities are similar or nearly the same. An indicator for the relevance of the content identifier may be adjusted in accordance with the comparison ( 808 ). 
       FIG. 9  depicts an exemplary flowchart for an implementation of determining a relevant content identifier. An implementation of the DAFS system  202  may be used to determine a relevant content identifier. A relevant content identifier may be a content identifier that is relevant to the search being performed. Initially, one or more answer entities from one or more answer candidate Snippet(s)  222  are determined ( 900 ). Patterns may be used to determine the answer entities within an answer candidate Snippet  222 . Next, a content identifier is determined for an answer candidate ( 902 ). The Snippet  222  may provide or identify a content identifier, such as a URL, for the answer candidate. 
     Next, a content identifier for the answer candidate may be tokenized ( 904 ). A lexer, patterns, and/or regular expressions may be used to tokenize the content identifier for the answer candidate. A comparison may be performed between a vector of tokens for the content identifier and a vector of the one or more answer entities ( 906 ). An indicator of the relevance for the content identifier may be adjusted in accordance with the comparison ( 908 ). 
       FIG. 10A  depicts an exemplary flowchart for an implementation of determining a relevant content identifier. Initially, answer entities may be determined from one or more answer candidate Snippet(s)  222  ( 1000 ). One or more content identifiers within an answer candidate cluster may be determined ( 1002 ). A set of content identifiers and/or all content identifiers for an answer candidate may be selected for a determination of the most relevant content identifier for the answer candidate. Those skilled in the art will recognize that any set of content identifiers may be selected for determining a relevant content identifier out of the set. 
     Next, a determination is made as to whether to factor in the title of content identifiers in a determination of a relevant content identifier ( 1004 ). If the title for the content identifier is factored in to determining content identifier relevance ( 1004 ), then the title of the content identifier may be parsed ( 1006 ). The parser may include a lexer, patterns, regular expressions, scripts, or any other process for determining the tokens within a title for the content identifier. A vector of tokens may be created for the title of the content identifier ( 1010 ). Nonessential words may be may be removed from the vector of tokens ( 1012 ). Tokens that do not convey a keyword, topic, or category may be removed. For example, tokens for “a,” “the,” and “and” may be removed because the tokens may not convey a keyword that would help with determining relevance of a content identifier. 
     Next, word level stemming may be performed ( 1014 ). Word level stemming may involve normalizing verbs to remove a tense. For example, “running” may become “run” for determining the relevance of a content identifier. Word level stemming may include eliminating an indication of a plural noun and/or possession. For example, “nuclei” may become “nucleus” for determining the relevance of a content identifier. 
     A vector of answer entities may be created and compared with a vector of tokens ( 1016 ) and a score may be adjusted in accordance with the comparison ( 1018 ). The score may be increased for each overlap between the vector of answer entities and vector of tokens. Next, a determination is made as to whether there are more content identifiers for determining a relevant content identifier ( 1020 ) and if there are more content identifiers, then the process repeats ( 1006 ). 
     Alternatively, if there are no more content identifiers ( 1020 ) or the title was not factored in to the relevance of a content identifier ( 1004 ), then a determination is made as to whether to factor in the content identifier ( 1022 ). 
     If the content identifier itself (e.g. a URL) is to be factored in to the relevance of content identifiers ( 1022 ), then the content identifier is parsed ( 1024 ). For example, a URL, such as “www.ask.com/a/b/c” may be parsed into “www,” “ask,” “com,” “a,” “b,” and “c.” A vector of tokens may be created for the title of the content identifier ( 1026 ). Nonessential words may be may be removed from the vector of tokens ( 1028 ). Tokens that do not convey a keyword, topic, or category may be removed. For example, “www” and “com” may be removed from the vector of tokens from the content identifier. Next, word level stemming may be performed ( 1030 ). A vector of answer entities may be created and compared with a vector of tokens ( 1032 ) and a score may be adjusted in accordance with the comparison ( 1034 ). The score may be increased for each overlap between the vector of answer entities and vector of tokens. Next, a determination is made as to whether there are more content identifiers for determining a relevant content identifier ( 1036 ) and if there are more content identifiers, then the process repeats ( 1024 ). 
     Alternatively,  FIG. 10B  depicts a continuation of an exemplary flowchart for an implementation of determining a relevant content identifier. Initially, a determination is made as to whether to factor in the popularity of a content identifier ( 1038 ). If the popularity of the content identifier is factored in ( 1038 ), then the behavioral database for popularity of a content identifier is queried ( 1040 ). An indication of the popularity of the content identifier may be received from the database ( 1040 ). The indication of the popularity of the content identifier may provide an indication of the popularity of the web page associated with the content identifier and the popularity of a main website and/or a common root for a content identifier. For example, the indication of popularity for a content identifier may be a click count for an article within cnn.com and a click count for “www.cnn.com.” A click count is a number that indicates popularity for a webpage or website. Continuing with the example, click count for the main website for a web page may be an aggregate of the click counts for all webpages or content available at “cnn.com.” In one or more embodiments, the click count is an estimation of how many times the page is accessed. The score of the relevance of the content identifier may be adjusted in accordance with the popularity of the content identifier ( 1042 ). Next, a determination is made as to whether there are more content identifiers for determining the relevance of a content identifier ( 1044 ). If there are more content identifiers ( 1044 ), then the process repeats ( 1040 ). 
     Alternatively, if the popularity of a content identifier is not factored in ( 1038 ), then a determination is made as to whether to factor in references to the content identifier ( 1046 ). References to the content identifier may include web pages that link to the content identifier or reference the content identified by the content identifier. If the popularity of references the content identifier is factored in ( 1046 ), then the behavioral database for references to a content identifier is queried ( 1048 ). An indication of the popularity of the content identifier may be received from the database ( 1048 ). The score of the relevance of the content identifier may be adjusted in accordance with the popularity of the content identifier ( 1050 ). Next, a determination is made as to whether there are more content identifiers for determining the relevance of a content identifier ( 1052 ). If there are more content identifiers ( 1052 ), then the process repeats ( 1048 ). 
     Alternatively, if there are no more content identifiers ( 1052 ) or references to content identifiers are not factored ( 1046 ), then the content identifiers are ranked in accordance with the relevance score for the content identifier ( 1054 ). The most relevant content identifier for an answer candidate may be presented with the answer with the Presentation Module  232 . 
       FIG. 11  depicts an exemplary Interface in accordance with an embodiment of an intelligent search system. The Interface  204  depicted in  FIG. 11  provides a text input box  1100  for input of a search query. The search results are displayed with an answer  1102  and a relevant content identifier  1104  for the answer. 
       FIG. 12  depicts an exemplary Snippet in accordance with an embodiment of an intelligent search system. The Snippet  222  provides a title, a description, a content identifier (e.g. URL) and a portion of content for the content identifier. 
       FIG. 13  depicts an exemplary block diagram for a system architecture of a computer system. The execution of instructions to practice the invention may be performed by any number of computer systems  1300  as depicted in  FIG. 13 . As used herein, the term computer system is broadly used to describe any computing device that can store and independently run one or more programs, applications, scripts, or software processes. Implementations of the present invention may have a single computer system  1300  or any number of computer systems  1300 . 
     Computer systems  1300  may communicate with other computer systems/devices with any number of Communication Interface(s)  1302 . The Communication Interface  1302  may provide the ability to transmit and receive signals, such as electrical, electromagnetic or optical signals, that include data streams representing various types of information (e.g. messages, communications, instructions, and data). The Communication Interface  1302  may provide an implementation for a communication protocol, such as a network protocol. Instructions may be executed by a Processor  1308  upon receipt and/or stored in Storage  1304  accessible to the Computer System  1300 . 
     Storage  1304  may be accessed by the Computer System  1300  with a Storage Interface  1306 . The Computer System  1300  may use the Storage Interface  1306  to communicate with the Storage  1304 . The Storage Interface  1306  may include a bus coupled to the storage and able to transmit and receive signals. Storage  1304  may include random access memory (RAM) or other dynamic storage devices, for storing dynamic data and instructions executed by the Processor  1308 . Any number of Processor(s)  1308  may be used to execute instructions for the Computer System  1300 . Storage may include, but is not limited to, read only memory (ROM), magnetic disks, flash drives, usb drives, and optical disks. In one or more embodiments, a Computer System  1300  may be connected to a Display  1310  for displaying information to a user. 
     “Computer usable medium” or “Computer-readable medium” refers to any medium that provides information and/or may be used by a Processor  1308 . Medium may include volatile and non-volatile storage mediums. 
     Various embodiments of the present invention may be implemented with the aid of computer-implemented processes or methods (e.g. programs or routines) that may be rendered in any computer language including, without limitation, C#, C/C++, Fortran, COBOL, PASCAL, Ruby, Python, assembly language, markup languages (e.g., HTML, SGML, XML, VoXML), and the like, as well as object-oriented environments such as the Common Object Request Broker Architecture (CORBA), Java™ and the like. In general, however, all of the aforementioned terms as used herein are meant to encompass any series of logical steps performed in a sequence to accomplish a given purpose. 
     In view of the above, it should be appreciated that some portions of this detailed description are presented in terms of algorithms and symbolic representations of operations on data within a computer memory. These algorithmic descriptions and representations are the means used by those skilled in the computer science arts to most effectively convey the substance of their work to others skilled in the art. An algorithm is here, and generally, conceived to be a self-consistent sequence of steps leading to a desired result. The steps are those requiring physical manipulations of physical quantities. Usually, though not necessarily, these quantities take the form of electrical or magnetic signals capable of being stored, transferred, combined, compared and otherwise manipulated. It has proven convenient at times, principally for reasons of common usage, to refer to these signals as bits, values, elements, symbols, characters, terms, numbers or the like. It should be borne in mind, however, that all of these and similar terms are to be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities. Unless specifically stated otherwise, it will be appreciated that throughout the description of the present invention, use of terms such as “processing”, “computing”, “calculating”, “determining”, “displaying” or the like, refer to the action and processes of a computer system, or similar electronic computing device, that manipulates and transforms data represented as physical (electronic) quantities within the computer system&#39;s registers and memories into other data similarly represented as physical quantities within the computer system memories or registers or other such information storage, transmission or display devices. 
     The present invention can be implemented with an apparatus to perform the operations described herein. This apparatus may be specially constructed for the required purposes, or it may comprise a general-purpose computer, selectively activated or reconfigured by an executing computer program stored in the computer. Such a computer program may be stored in a computer-readable storage medium, such as, but not limited to, any type of disk including floppy disks, optical disks, CD-ROMs, and magnetic-optical disks, read-only memories (ROMs), random access memories (RAMs), EPROMs, EEPROMs, magnetic or optical cards, or any type of media suitable for storing electronic instructions, and each coupled to a computer system bus. 
     Various general-purpose systems may be used with programs in accordance with the teachings herein, or it may prove convenient to construct more specialized apparatus to perform the required method. For example, any of the methods according to the present invention can be implemented in hard-wired circuitry, by programming a general-purpose processor or by any combination of hardware and software. One of ordinary skill in the art will immediately appreciate that the invention can be practiced with computer system configurations other than those described below, including hand-held devices, multiprocessor systems, microprocessor-based or programmable consumer electronics, DSP devices, network PCs, minicomputers, mainframe computers, and the like. The invention can also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. 
     While certain exemplary embodiments have been described and shown in the accompanying drawings, it is to be understood that such embodiments are merely illustrative and not restrictive of the current invention, and that this invention is not restricted to the specific constructions and arrangements shown and described since modifications may occur to those ordinarily skilled in the art. The previous detailed description is of a small number of embodiments for implementing the invention and is not intended to be limiting in scope. One of skill in this art will immediately envisage the methods and variations used to implement this invention in other areas than those described in detail. The following claims set forth a number of the embodiments of the invention disclosed with greater particularity.