With the goal of harvesting all information about a given entity, in this paper, we try to harvest all matching documents for a given query submitted on a search engine. The objective is to retrieve all information about for instance "Michael Jackson", "Islamic State", or "FC Barcelona" from indexed data in search engines, or hidden data behind web forms, using a minimum number of queries. Policies of web search engines usually do not allow accessing all of the matching query search results for a given query. They limit the number of returned documents and the number of user requests. These limitations are also applied in deep web sources, for instance in social networks like Twitter. In this work, we propose a new approach which automatically collects information related to a given query from a search engine, given the search engine's limitations. The approach minimizes the number of queries that need to be sent by analysing the retrieved results and combining this analysed information with information from a large external corpus. The new approach outperforms existing approaches when tested on Google, measuring the total number of unique documents found per query.

With the increasing amount of data in deep web sources (hidden from general search engines behind web forms), accessing this data has gained more attention. In the algorithms applied for this purpose, it is the knowledge of a data source size that enables the algorithms to make accurate decisions in stopping the crawling or sampling processes which can be so costly in some cases [14]. This tendency to know the sizes of data sources is increased by the competition among businesses on the Web in which the data coverage is critical. In the context of quality assessment of search engines [7], search engine selection in the federated search engines, and in the resource/collection selection in the distributed search field [19], this information is also helpful. In addition, it can give an insight over some useful statistics for public sectors like governments. In any of these mentioned scenarios, in the case of facing a non-cooperative collection which does not publish its information, the size has to be estimated [17]. In this paper, the suggested approaches for this purpose in the literature are categorized and reviewed. The most recent approaches are implemented and compared in a real environment. Finally, four methods based on the modification of the available techniques are introduced and evaluated. In one of the modifications, the estimations from other approaches could be improved ranging from 35 to 65 percent.

With the information explosion on the internet, finding precise answers efficiently is a prevalent requirement by many users. Today, search engines answer keyword queries with a ranked list of documents. Users might not be always willing to read the top ranked documents in order to satisfy their information need. It would save lots of time and efforts if the the answer to a query can be provided directly, instead of a link to a document which might contain the answer. To realize this functionality, users must be able to define their information needs precisely, e.g., by using structured queries, and, on the other hand, the system must be able to extract information from unstructured text documents to answer these queries.To this end, we introduce a system which supports structured queries over unstructured text documents, aiming at finding structured answers to the users' information need. Our goal is to extract answers from unstructured natural text, by applying various efficient techniques that allow fast query processing over text documents from the web or other heterogeneous sources.A key feature of our approach is that it does not require any upfront integration efforts such as the definition of a common data model or ontology.

The research reported in this thesis has been carried out under the auspices of SIKS,