Query Processing is the process of query term weight calculation, query augmentation, query context defining, and more. Query understanding and Query clustering are related to Information Retrieval tasks for the search engines. To provide a better search engine optimization effort and project result, the organic search performance optimizers need to implement query processing methodologies. Digital marketing and SEO are connected to each other. Understanding a query includes query parsing, query rewriting, question generation, and answer pairing. Multi-stages Query Processing, Candidate Answer Passages, or Candidate Answer Passages and Answer Term Weighting are some of the concepts from the Google Search Engine to parse the queries.
The presentation of The Secret Life of Queries, Parsing, Rewriting & SEO has been presented at the Brighton SEO Event in April 2022. The event speech focused on explaining the theoretical SEO and practical SEO examples together.
Query Processing methodologies are beyond synonym matching or synonym finding. It involves multiple aspects of the words, and meanings of the words. The theme of words, the centrality of words, attention windows, context windows, and word co-occurrence matrices, GloVe, Word2Vec, word embeddings, character embeddings, and more.
Themes of words contain the word probability like in Continues Bag of Window.
The search engine optimization community focuses on keyword research by matching the queries. Query processing involves query word order change, query word type change, query word combination change, query phrase synonym usage, query question generation, query clustering. Query processing and document processing are correlational. Query processing is to understand a query while document processing is to process a web document. Both of the processes are for ranking algorithms. Providing a better ranking algorithm requires a better query understanding. And providing better rankings as SEOs require better search engine understanding. Thus, understanding the methods of query processing is necessary.
Search Query Processing is implementing the query processing for thesearch engines. Search query refers to the phrase that search engine users use for searching. Search intent understanding and search intent grouping are two different things. But, query templates, questions templates, and document templates work together. Search query is for organic search behaviors. A web search engine answers millions of queries every day. Search query processing is a fundamental task for search engine optimization and search engine result page optimization.
The "Semantic Search Engine: Query Processing" slides from Koray Tuğberk GÜBÜR supported the presentation of "Search Query Processing: The Secret Life of Queries, Parsing, Rewriting & SEO". The presentation has been created by Dear Rebecca Berbel.
Many thanks to the Google engineers that created the Semantic Search Engine patents including Larry Page.
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Search Query Processing: The Secret Life of Queries, Parsing, Rewriting & SEO
1. The Secret Life of Queries:
Parsing, Rewriting & SEO
Rebecca Berbel | Oncrawl
Koray Tuǧberk Gübür | Holistic SEO
slideshare.net/Oncrawl
@RebBerbel @KorayGubur
http://norvig.com/ - Peter Norvig, Director of Research at GOogle (Levenshtein Distance)
2011 / 2013
Keyword stuffing
Misspellings - 10% of queries ?Pre-2014 tool for "PPC"Digg, August 2008
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Probabilistic Graphical Model for the Keywords to Questions system’s “User Inquiry Intent Model” (K2Q)
Probabilistic Graphical Model for the Keywords to Questions system’s “User Inquiry Intent Model” (K2Q)
This is by Jakob Uszkoreit, whose work today is almost exclusively on attention-based transformers (NLP)
2011 / 2013
"Candidate answer passages are generated from both structured content and unstructured content according to corresponding selection criteria. This allows the user to not only receive prose-type explanations but also to receive a combination of prose-type and factual information, which, in turn, may be highly relevant to the user's informational need."
MuM
"Neural matching"
SERP features
Information retrieval processesCost optimization
2011 / 2013
Putting keywords on a page is not sufficient
LSI keywords don't exist
Websites are evaluated as a whole, not page-by-page