Searching for information about people in the web is one of the most common activities of internet users. Around 30 percent of search engine queries include person names. However, retrieving information about people from web search engines can become difficult when a person has nicknames or name aliases. Identification of entities on the web is difficult for two fundamental reasons: first, different entities can share the same name (i.e., lexical ambiguity); second, a single entity can be designated by multiple names (i.e., referential ambiguity). For example, the lexical ambiguity consider the name Jim Clark. Aside from the two most popular namesakes, the formula-one racing champion and the founder of Netscape, at least 10 different people are listed among the top 100 results returned by Google for the name. On the other hand, referential ambiguity occurs because people use different names to refer to the same entity on the web. An individual is typically referred by numerous name aliases on the web. Accurate identification of aliases of a given person name is useful in various web related tasks such as information retrieval, sentiment analysis, personal name disambiguation, and relation extraction. We propose a method to extract aliases of a given personal name from the web. Given a personal name, the proposed method first extracts a set of candidate aliases. Second, we rank the extracted candidates according to the likelihood of a candidate being a correct alias of the given name. We propose a novel, automatically extracted lexical pattern-based approach to efficiently extract a large set of candidate aliases from snippets retrieved from a web search engine. We define numerous ranking scores to evaluate candidate aliases using two approaches: word co-occurrences model and page counts on the web. To construct a robust alias detection system, we integrate the different ranking scores into a single ranking function using ranking support vector machines. The proposed method outperforms numerous baselines and previously proposed name alias extraction methods, achieving a statistically significant mean reciprocal rank (MRR) of 0.67. Experiments carried out using location names and English personal names suggest the possibility of extending the proposed method to extract aliases for different types of named entities, and for different languages. Moreover, the aliases extracted using the proposed method are successfully utilized in an information retrieval task and improve recall by 30 percent in a relation detection task.