Search engines of today are focusing on efficiently and accurately generating search results.Yet, there is much to be explored in the way people interact with the applications and relate to the content. Individuals are commonly unique, with complex preferences, motives and expectations. Not only is it important to be sensitive to these differences, but to accommodate the extremes. Enhancing a search engine does not only rely on technological development, but to explore potential user experiences in broader perspectives - which not only gratifies the needs for information, but supports a diversity of journeys. The aim of the project is to develop an alternate search engine with different functionality based on new values that reflects contemporary needs. The result, Exposeek, is an experiential prototype supporting exploratory browsing based on principles of distributed infrastructure, transparent computation and serendipitous information. Suggestive queries, legible algorithms and augmented results provide additional insights and present an alternative way to seek and peruse the Web. / Search Engines, Interaction Design

Search engines have forever changed the way people access and discover knowledge, allowing information about almost any subject to be quickly and easily retrieved within seconds. As increasingly more material becomes available electronically the influence of search engines on our lives will continue to grow. This presents the problem of how to find what information is contained in each search engine, what bias a search engine may have, and how to select the best search engine for a particular information need. This research introduces a new method, search engine content analysis, in order to solve the above problem. Search engine content analysis is a new development of traditional information retrieval field called collection selection, which deals with general information repositories. Current research in collection selection relies on full access to the collection or estimations of the size of the collections. Also collection descriptions are often represented as term occurrence statistics. An automatic ontology learning method is developed for the search engine content analysis, which trains an ontology with world knowledge of hundreds of different subjects in a multilevel taxonomy. This ontology is then mined to find important classification rules, and these rules are used to perform an extensive analysis of the content of the largest general purpose Internet search engines in use today. Instead of representing collections as a set of terms, which commonly occurs in collection selection, they are represented as a set of subjects, leading to a more robust representation of information and a decrease of synonymy. The ontology based method was compared with ReDDE (Relevant Document Distribution Estimation method for resource selection) using the standard R-value metric, with encouraging results. ReDDE is the current state of the art collection selection method which relies on collection size estimation. The method was also used to analyse the content of the most popular search engines in use today, including Google and Yahoo. In addition several specialist search engines such as Pubmed and the U.S. Department of Agriculture were analysed. In conclusion, this research shows that the ontology based method mitigates the need for collection size estimation.

Thesis (MTech (Business Information Systems))--Cape Peninsula University of Technology, 2005. / Includes bibliographical references (leaves: 59-66). Also available online.

Thesis (Ph.D.) -- University of Western Sydney, 2004. / "A thesis submitted for the degree of Doctor of Philosophy at the University of Western Sydney" Bibliography : leaves 223-242.

Thesis (M.S.)--University of Texas at El Paso, 2008. / Title from title screen. Vita. CD-ROM. Includes bibliographical references. Also available online.

Thesis (M.S.)--Kent State University, 2007. / Title from PDF t.p. (viewed July 2, 2007). Advisor: David B. Robins. Keywords: information retrieval, search engine, social bookmarking, tagging, folksonomy, Internet, World Wide Web. Includes survey instrument. Includes bibliographical references (p. 137-141).

Thesis (M.Sc.)--Acadia University, 2000. / Includes bibliographical references (leaves 86-89). Also available on the Internet via the World Wide Web.

Thesis (Ph. D.)--Ohio State University, 2007. / Full text release at OhioLINK's ETD Center delayed at author's request

Thesis (Ph. D.)--University of Washington, 2007. / Vita. Includes bibliographical references (leaves 129-139).

Oι πληροφορίες που τροφοδοτουν τη βάση δεδομένων των Μηχανών Αναζήτησης προέρχονται από τον Παγκόσμιο Ιστό. Ένα τρέχων ζητούμενο στο πεδίο έρευνας των Μηχανών Αναζήτησης είναι η ανάπτυξη λογισμικού με σκοπό να δίνει την δυνατότητα στον χρήστη να δεικτοδοτεί προσωπικά έγγραφα έτσι ώστε παράλληλα να μπορεί να κάνει αναζητήσεις για εύρεση πληροφοριών και σε έγγραφα που προέρχονται από τον Παγκόσμιο Ιστό αλλά και σε προσωπικά του έγγραφα όλα δεικτοδοτημένα σε μία βάση. Αυτό είναι και το κύριο πρόβλημα που επιλύουμε στην παρούσα εργασία. Με την δυνατότητα αυτήν ουσιαστικά ενοποιείται η διαδικασία της αναζήτησης πληροφοριών στις δυο διαφορετικές πηγές πληροφοριών, τα έγγραφα του Παγκόσμιου Ιστού και τα προσωπικά έγγραφα του χρήστη. / -