Google’s Query Refinements by milliardaireat23


									Google’s Query
   Brought to you by:

     Margaret Ortiz
   Affilorama Jetpack
Search engines aim to provide the most relevant results in response to
queries but limitations can be seen on what is actually returned based on
the queries used. Search queries can either be too specific or too general for
search engines to recognize good results. Google has filed patent
applications regarding alternative query terms or query refinements to offer
a solution.

The Google Solution

Search queries that are not too effective in providing good results include
homonyms which are words that have the same sound or spelling but
different meanings. Improper contexts in the choice of words can also be
very confusing especially to search engines. Very general terms provide
results that are too broad while very narrow terms can be very restrictive
and may provide non-responsive search results.

Google presents a system and method that attempts to address this
particular problem. In this system, a stored query and a stored document are
associated as a logical pairing. The pairing is assigned a weight thus when a
search query is issued, a set of search documents is produced. There is at
least one search document that matches at least one document. Retrieval is
done when the stored query and the assigned weight associated with it
matches at least one stored document. A cluster is formed through this and
scoring is done on at least one cluster relative to at least one other cluster.
At least one such scored query is suggested as a set of query refinements.

The process starts when Google finds results by choosing the top 100
documents for clustering. During this phase, term vectors are computed for
each of the said documents which were ranked by relevance score. The
documents are matched to a stored document listed in an association
database. Alternative query terms are found by looking at associations with
queries that had been computed beforehand for the matched stored

Term vectors are also created for alternative query terms. Clusters are
created from both sets of term vectors to form groupings. Each cluster has a
calculated cluster centroid. Search queries associated with a search
document in the cluster are scored according to the distance from this
centroid and the percent of stored documents occurring in the cluster. The
best suggested query refinement contains the highest number search query
terms and the most frequently seen in the documents in the cluster.

Other clusters and query names may be created to come up with additional
suggested query refinements. Refinements are sorted by relevance scores.
Alternative queries can include negated forms of terms appearing in the set
of refinements but does not appear on the original search query. A number
of predetermined search queries selected from past user queries can be
used to arrive at a precomputed possible set of refinements. The
predetermined queries would be issued while search results are maintained
in a database for future user search requests. The refined queries would be
provided to the user together with the results of the original search.

The precomputation stage happens before any query is entered into the
search engine. It is best described with the use of at least four parts –
associator, selector, regenerator and inverter.

The associator creates relevance-weighted relationships between stored
queries and stored documents. The selector decides which stored
documents and stored queries should be retrieved. The regenerator looks at
query logs and selects stored documents based on previous searches. The
inverter looks at the cached data and selects documents and associated
queries based on the cached data.
The query refinements system itself has four parts. A matcher matches one
or more stored documents to the actual search documents which have been
generated by the search engine to answer a search query. It also identifies
the stored queries and assigned weights using the associations
corresponding to the matched stored documents. A clusterer forms one or
more clusters using term vectors formed from the terms occurring in the
matched stored queries and corresponding weights. The scorer computes
centroids which represent the weighted center of each cluster’s term vector.
A presenter identifies the highest scoring search queries as one or more
query refinements to the user. The interesting aspect about this approach is
how user data is incorporated into results through the use of log files and
cached information.

The patent application shows one way of achieving query refinements but
no one really knows for sure exactly how Google comes up with alternative
results. However, it offers some hints on how to create contents on websites
and how to show up in these alternative results. By taking into careful
consideration the words that people will probably search for and what
appears in Google’s results for search phrases, a clue can be provided on
how the search refinements approach will treat a website.

Multi-Stage Query Processing

The determination of page relevancy in responding to queries from
searchers considers how a term or phrase is used in the context of a page. A
patent application that looks into the possible ways of considering the
context of these words was likewise submitted by Google. It describes a
multi-stage process that determines relevancy and finds results to a search.

The possible actions to be taken as described in this document can be
divided into stages. The first stage deals with deletion of stop words, term
stemming and expansion of queries to use things like synonyms and related
terms that commonly co-occur with them. During this stage, the relevancy
scores are created between query and each document computed with one
or more scoring algorithms. The second stage uses adjacency and proximity
of terms to rank documents. The third stage reviews the term attributes
such as determining whether terms are titles, headings, metadata or
whether these terms possess certain font characteristics. The fourth and last
stage is the generation of snippets to return with results.

Interactive query refinements have shown that it can promote effective
retrieval. Major search engines use the history of a user’s actions such as
queries or clicks to personalize search results. The query-specific web
recommendations (QSRs) retroactively answer queries from the user’s
history as new results arise. Its main goal is to recommend new web pages
for user’s old queries. However, this will not be of any use unless the user
has a standing interest in a particular query. Focus can also be shifted from
individual queries to query sessions which includes all actions associated
with a given initial query. A query is considered a query refinement of the
previous one if both queries contain at least one common term.
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