The exponential growth of digital information available in companies and on the web creates the need for search tools that can respond to the most sophisticated information needs. Many user tasks would be simplified if search engines would support typed search, and return entities instead of just web documents. For example, an executive who tries to solve a problem needs to find people in the company who are knowledgeable about a certain topic.
In the first part of the book, we propose a model for expert finding based on the well-consolidated vector space model for information retrieval and investigate its effectiveness. In the second part of the book, we investigate different methods based on semantic web and natural language processing techniques for ranking entities of different types both in Wikipedia and, generally, on the web.
In the third part of this publication, we study the problem of entity retrieval for news applications and the importance of the news trail history (i.e., past related articles) to determine the relevant entities in current articles. We also study opinion evolution about entities: we propose a method for automatically extracting the public opinion about political candidates from the blogosphere.