Where possible, terms are linked to relevant parts of the documentation for more information.
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A way to provide users suggestions for possible matching queries before they have finished typing. In LucidWorks Enterprise, this relies on an index of terms to be created on a regular basis by scheduling it as an activity.
These control the inclusion or exclusion of keywords in a query by using operators such as AND, OR, and NOT.
Click Scoring Relevance Framework
A method of changing the relevance ranking of a document based on the number of times other users have clicked on the same document.
One or more documents grouped together for the purposes of searching. See also Document.
A part of LucidWorks Enterprise that has been designed to stand alone or can be run independently from other components. LucidWorks Enterprise has two main components: LWE Core, which runs Solr, indexing, and other critical application functions and LWE UI, which runs the Administrative UI, the front-end search interface, and the alerting functionality.
Defines the metadata required to connect to a location containing content to be indexed. It could be a file system path, a Web URL, a JDBC connection, or some other set of values.
One or more Fields. See also Field.
Inverse Document Frequency (IDF)
A measure of the general importance of a term. It is calculated as the number of total Documents divided by the number of Documents that a particular word occurs in the collection. See http://en.wikipedia.org/wiki/Tf-idf and http://lucene.apache.org/java/2_3_2/scoring.html for more info on TF-IDF based scoring and Lucene scoring in particular. See also Term Frequency.
A way of creating a searchable index that lists every word and the documents that contain those words, similar to an index in the back of a book which lists words and the pages on which they can be found. When performing keyword searches, this method is considered more efficient than the alternative, which would be to create a list of documents paired with every word used in each document. Since users search using terms they expect to be in documents, finding the term before the document saves processing resources and time.
Natural Language Query
A search that is entered as a user would normally speak or write, as in, "What is aspirin?"
A query parser processes the terms entered by a user.
The appropriateness of a document to the search conducted by the user.
A method of copying a master index from one server to one or more "slave" or "child" servers. In LucidWorks Enterprise, the master continues to manage updates to the index, while queries are handled by the slaves. This approach enables LucidWorks Enterprise to properly manage query load and ensure responsiveness.
An alternative way of controlling LucidWorks Enterprise without accessing the user interface.
Solr Schema (schema.xml)
The Apache Solr index schema. The schema defines the fields to be indexed and the type for the field (text, integers, etc.) The schema is stored in schema.xml and is located in the Solr home conf directory.
Solr Config (solrconfig.xml)
The Apache Solr configuration file. Defines indexing options, RequestHandlers, highlighting, spellchecking and various other configurations. The file, solrconfig.xml is located in the Solr home conf directory.
The ability to suggest alternative spellings of search terms to a user, as a check against spelling errors causing few or zero results. In LucidWorks Enterprise, effective spell checking requires an index to be built on a regular basis by scheduling it as an [activity].
Generally, words that have little meaning to a user's search but which may have been entered as part of a natural language query. Stopwords are generally very small pronouns, conjunctions and prepositions (such as, "the", "with", or "and")
Synonyms generally are terms which are near to each other in meaning and may substitute for one another. In a search engine implementation, synonyms may be abbreviations as well as words, or terms that are not consistently hyphenated. Examples of synonyms in this context would be "Inc." and "Incorporated" or "iPod" and "i-pod".
The number of times a word occurs in a given document. See http://en.wikipedia.org/wiki/Tf-idf and http://lucene.apache.org/java/2_3_2/scoring.html for more info on TF-IDF based scoring and Lucene scoring in particular.
See also Inverse Document Frequency (IDF).
A wildcard allows a substitution of one or more letters of a word to account for possible variations in spelling or tenses. In LucidWorks Enterprise, there are two ways to use them. One is to use an asterisk (*) at the end of a term to find all documents that contain words that start with that pattern. For example, paint* would find paint, painter and painting. A second way is to use a question mark (?) in the middle of a term to substitute for one character in that term. Such as, c?t would find cat, cot and cut. It's also possible to use wildcards at the start of a term in the same way - either to replace a single letter (using the ? symbol) or to find documents that contain words that end with a pattern using a *. For example, *sphere would find ecosphere and stratosphere.