Disambiguating the Appearances of People by an Automatic Discovery Model of Personal Name

The most common activities of internet users are searching for information. Retrieving information about people from web search engines can become difficult when a person has nicknames or name aliases or in different names. Thus there is an important issue of knowing the exact name of the user in case of information reclamation, outlook analysis, personal name disambiguation, and relation extraction. Identifying aliases of a name are important in information retrieval. Automatically extracted lexical pattern-based approach is used to efficiently extract a large set of candidate aliases from snippets retrieved from a web search engine. The proposed method comprises two main components: pattern extraction, and pseudonym extraction and ranking. Using a seed list of name-alias pairs, first extract lexical patterns that are frequently used to convey information related to alias on the web. The extracted patterns are then used to find candidate pseudonyms or aliases for a given name. They define various ranking scores using the hyperlink structure on the web and page counts retrieved from a search engine to identify the correct aliases among the extracted candidates. Reduction in generation of infrequent candidates will improve the speed of mining process to many folds making the algorithm highly efficient.