Search and search optimization using a pattern of a location identifier

Abstract

Systems and methods for search and search optimization using a pattern in a location identifier is disclosed. In one aspect, embodiments of the present disclosure include a method, which may be implemented on a system, of search and search optimization. The method includes, detecting a set of location identifiers that have a pattern that matches a specified pattern and identifying a set of search results as having content related to the semantic type. The specified pattern can be stored in a computer-readable storage medium and corresponds to a semantic type. The set of search results can include objects associated with the set of location identifiers having the specified pattern.

What is claimed is: 
     
         1 . A method of search, comprising:
 detecting a set of Universal Resource Identifiers (URIs) that have a pattern that matches a specified pattern that is not a URI;   wherein, the specified pattern corresponds to a semantic type;   identifying a set of search results as having content related to the semantic type from a set of type-determined web pages associated with the set of URIs having the specified pattern;   data mining content of each of the set of search results to further determine relevancy to the semantic type;   identifying a refined set of search results from the set of type-determined web pages based on the relevancy to the semantic type determine via the data mining;   wherein, the set of URIs are identified from links of user-added content in a social network.   
     
     
         2 . The method of  claim 1 , further comprising associating each of the set of type-determined web-pages with an ontology defining the semantic type. 
     
     
         3 . The method of  claim 1 , further comprising,
 extracting additional semantic data from HTML or XHTML content of each of the set of type-determined web pages; and   generating a refined set of search results from the set of search results using the additional semantic data.   
     
     
         4 . The method of  claim 3 , wherein, the additional semantic data is extracted using GRDDL (Gleaning Resource Descriptions from Dialects of Languages). 
     
     
         5 . The method of  claim 1 , further comprising, ranking each of the set of type-determined web pages based on the relevancy determined from the data mining. 
     
     
         6 . The method of  claim 1 , further comprising, identifying multiple patterns corresponding to the semantic type based on identification of multiple webpages as having content of the semantic type by the end user or additional end users. 
     
     
         7 . The method of  claim 1 , wherein, the pattern further corresponds to an attribute of the semantic type. 
     
     
         8 . The method of  claim 7 , wherein, each of the type-determined web pages includes content that is associated with the attribute of the semantic type. 
     
     
         9 . The method of  claim 1 , wherein, the pattern includes a domain name segment or a wildcard segment. 
     
     
         10 . The method of  claim 11 , wherein, the pattern further includes a semantic type segment. 
     
     
         11 . The method of  claim 11 , wherein, the pattern further includes an attributes segment. 
     
     
         12 . A method performed by a search engine, the method comprising:
 detecting a set of Universal Resource Identifiers (URIs) that match a specified pattern that is not a URI;   wherein, the specified pattern corresponds to a semantic type;   wherein, the specified pattern includes a domain name segment or a wildcard segment, a semantic type segment, and an attributes segment,
 wherein, the semantic type is associated with multiple attributes that are user-defined; 
   identifying a set of search results as having content related to the semantic type from a set of type-determined web pages associated with the set of URIs having the specified pattern;   data mining the content of each of the set of type-determined web pages to further determine relevancy to the semantic type;   identifying a refined set of search results from the set of type-determined web pages based on the relevancy to the semantic type determined via the data mining.   
     
     
         13 . The method of  claim 12 , further comprising, associating each of the set of type-determined web-pages with an ontology defining the semantic type. 
     
     
         14 . The method of  claim 12 , further comprising,
 extracting additional semantic data from HTML or XHTML content of each of the set of type-determined web pages; and   generating a refined set of search results from the set of search results using the additional semantic data.   
     
     
         15 . The method of  claim 14 , wherein, the additional semantic data is extracted using GRDDL (Gleaning Resource Descriptions from Dialects of Languages). 
     
     
         16 . The method of  claim 12 , further comprising ranking each of the set of type-determined web pages based on the relevancy determined from the data mining. 
     
     
         17 . The method of  claim 12 , wherein, the multiple attributes are rated by users via a user interface. 
     
     
         18 . The method of  claim 12 , further comprising, identifying multiple patterns corresponding to the semantic type based on identification of multiple webpages as having content of the semantic type by the end user or additional end users. 
     
     
         19 . The method of  claim 12 , wherein the semantic type is associated with additional attributes are automatically determined. 
     
     
         20 . The method of  claim 12 , wherein, some of the multiple attributes of the semantic type are determined by a predefined ontology. 
     
     
         21 . The method of  claim 12 , wherein, some of the multiple attributes of the semantic type are determined by a user-defined ontology. 
     
     
         22 . A system comprising:
 at least a processor and memory, cooperating to function as:
 a detecting unit configured to detect a set of Universal Resource Identifiers (URIs) that match a specified pattern that is not a URI; 
 wherein, the specified pattern corresponds to a semantic type; 
 wherein, the specified pattern includes a domain name segment or a wildcard segment, a semantic type segment, and an attributes segment, 
 wherein, the semantic type is associated with multiple attributes that are user-defined; 
 a first identifying unit configured to identify a set of search results as having content related to the semantic type from a set of type-determined web pages associated with the set of URIs having the specified pattern; 
 a mining unit configured to data mine the content of each of the set of type-determined web pages to further determine relevancy to the semantic type; 
 a second identifying unit configured to identify a refined set of search results from the set of type-determined web pages based on the relevancy to the semantic type determined via the data mining. 
   
     
     
         23 . The system of  claim 22 , wherein the at least one processor and memory cooperating to further function as an associating unit configured to associate each of the set of type-determined web-pages with an ontology defining the semantic type. 
     
     
         24 . The system of  claim 22 , wherein the at least one processor and memory cooperating to further function as a ranking unit configured to rank each of the set of type-determined web pages based on the relevancy determined from the data mining. 
     
     
         25 . The machine-readable storage medium of  claim 22 , wherein, some of the multiple attributes of the semantic type are determined by a user defined ontology.