Types of Web Queries
– Three broad categories: informational, navigational, and transactional
– Also known as do, know, go
– Empirically validated classification based on actual search engine queries
– Informational queries cover broad topics with thousands of relevant results
– Navigational queries seek a single website or web page of a single entity
– Transactional queries reflect the intent to perform a particular action
– Search engines also support connectivity queries that report on the connectivity of the indexed web graph

Characteristics of Web Queries
– Most commercial web search engines do not disclose their search logs
– Research studies on web searches started appearing in 1998
– Average length of a query was 2.4 terms
– About half of the users entered a single query, while less than a third entered three or more unique queries
– Close to half of the users examined only the first one or two pages of results
– Less than 5% of users used advanced search features
– Top four most frequently used terms were empty search, and, of, and sex
– 19% of queries contained a geographic term
– Predictable patterns of how users change their queries exist
– 33% of queries from the same users were repeat queries, and 87% clicked on the same result
– 30% of queries are navigational queries
– Query term frequency distributions conform to the power law or long tail distribution curves
– Average length of queries has grown steadily over time, especially non-English language queries
– Hummingbird update implemented by Google in 2013 to handle longer search queries

Structured Queries
– Boolean operators and parentheses can be used in search engines to create structured queries
– Technique traditionally used by librarians
– Faceted queries are a conjunction of characteristic words describing different topics or facets
– Faceted queries can find documents even if some words are omitted
– Example of a faceted query: (electronic OR computerized OR DRE) AND (voting OR elections OR election OR balloting OR electoral)

Related Concepts
– Information retrieval
– Web search engine
– Web query classification
– Taxonomy for search engines
– User intent

References
– Broder, A. (2002). A taxonomy of Web search. SIGIR Forum, 36(2), 3–10.
– Jansen, B. J., Booth, D., and Spink, A. (2008). Determining the informational, navigational, and transactional intent of Web queries, Information Processing & Management. 44(3), 1251-1266.
– Moore, Ross. Connectivity servers. Cambridge University Press.
– Dawn Kawamoto and Elinor Mills (2006), AOL apologizes for release of user search data
– Jansen, B. J., Spink, A., Bateman, J., and Saracevic, T. 1998. Real life information retrieval: A study of user queries on the web. SIGIR Forum, 32(1), 5 -17.
– Silverstein, C., Henzinger, M., Marais, H., & Moricz, M. (1999). Analysis of a very large Web search engine query log. SIGIR Forum,33(1), 6–12.
– Amanda Spink; Dietmar Wolfram; Major B. J. Jansen; Tefko Saracevic (2001). Searching the web: The public and their queries (PDF). Journal of the American Society for Information Science and Technology. 52 (3): 226–234.
– Mark Sanderson & Janet Kohler (2004). Analyzing geographic queries. Proceedings of the Workshop on Geographic Information (SIGIR 04)
– Jansen, B. J., Booth, D. L., & Spink, A. (2009). Patterns of query modification during Web searching. Journal of the American Society for Information Science and Technology. 60(3), 557-570.
– Jaime Teevan; Eytan Adar; Rosie Jones; Michael Potts (2005). History repeats itself: Repeat Queries in Yahoos query logs (PDF). Proceedings of the 29th Annual ACM Conference on Research and Development in Information Retrieval (SIGIR 06)
– Bing Making search yours – Search Blog – Site Blogs – Bing Community.
– Ricardo Baeza-Yates (2005). Applications of Web Query Mining. Advances in Information Retrieval. Lecture Notes in Computer Science. Vol.3408. Springer Berlin / Heidelberg.
– Alejandro Figueroa (2015). Exploring effective features  Source: https://en.wikipedia.org/wiki/Web_query

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