Full-text search

Last updated May 27, 2024

In text retrieval, full-text search refers to techniques for searching a single computer-stored document or a collection in a full-text database. Full-text search is distinguished from searches based on metadata or on parts of the original texts represented in databases (such as titles, abstracts, selected sections, or bibliographical references).

In a full-text search, a search engine examines all of the words in every stored document as it tries to match search criteria (for example, text specified by a user). Full-text-searching techniques appeared in the 1960s, for example IBM STAIRS from 1969, and became common in online bibliographic databases in the 1990s.^{[ verification needed ]} Many websites and application programs (such as word processing software) provide full-text-search capabilities. Some web search engines, such as the former AltaVista, employ full-text-search techniques, while others index only a portion of the web pages examined by their indexing systems.^[1]

Indexing

When dealing with a small number of documents, it is possible for the full-text-search engine to directly scan the contents of the documents with each query, a strategy called "serial scanning". This is what some tools, such as grep, do when searching.

However, when the number of documents to search is potentially large, or the quantity of search queries to perform is substantial, the problem of full-text search is often divided into two tasks: indexing and searching. The indexing stage will scan the text of all the documents and build a list of search terms (often called an index, but more correctly named a concordance). In the search stage, when performing a specific query, only the index is referenced, rather than the text of the original documents.^[2]

The indexer will make an entry in the index for each term or word found in a document, and possibly note its relative position within the document. Usually the indexer will ignore stop words (such as "the" and "and") that are both common and insufficiently meaningful to be useful in searching. Some indexers also employ language-specific stemming on the words being indexed. For example, the words "drives", "drove", and "driven" will be recorded in the index under the single concept word "drive".

The precision vs. recall tradeoff

Diagram of a low-precision, low-recall search Full-text-search-results.png — Diagram of a low-precision, low-recall search

Recall measures the quantity of relevant results returned by a search, while precision is the measure of the quality of the results returned. Recall is the ratio of relevant results returned to all relevant results. Precision is the ratio of the number of relevant results returned to the total number of results returned.

The diagram at right represents a low-precision, low-recall search. In the diagram the red and green dots represent the total population of potential search results for a given search. Red dots represent irrelevant results, and green dots represent relevant results. Relevancy is indicated by the proximity of search results to the center of the inner circle. Of all possible results shown, those that were actually returned by the search are shown on a light-blue background. In the example only 1 relevant result of 3 possible relevant results was returned, so the recall is a very low ratio of 1/3, or 33%. The precision for the example is a very low 1/4, or 25%, since only 1 of the 4 results returned was relevant.^[3]

Due to the ambiguities of natural language, full-text-search systems typically includes options like filtering to increase precision and stemming to increase recall. Controlled-vocabulary searching also helps alleviate low-precision issues by tagging documents in such a way that ambiguities are eliminated. The trade-off between precision and recall is simple: an increase in precision can lower overall recall, while an increase in recall lowers precision.^[4]

False-positive problem

Full-text searching is likely to retrieve many documents that are not relevant to the intended search question. Such documents are called false positives (see Type I error). The retrieval of irrelevant documents is often caused by the inherent ambiguity of natural language. In the sample diagram to the right, false positives are represented by the irrelevant results (red dots) that were returned by the search (on a light-blue background).

Clustering techniques based on Bayesian algorithms can help reduce false positives. For a search term of "bank", clustering can be used to categorize the document/data universe into "financial institution", "place to sit", "place to store" etc. Depending on the occurrences of words relevant to the categories, search terms or a search result can be placed in one or more of the categories. This technique is being extensively deployed in the e-discovery domain.^{[ clarification needed ]}

Performance improvements

The deficiencies of full text searching have been addressed in two ways: By providing users with tools that enable them to express their search questions more precisely, and by developing new search algorithms that improve retrieval precision.

Improved querying tools

Keywords. Document creators (or trained indexers) are asked to supply a list of words that describe the subject of the text, including synonyms of words that describe this subject. Keywords improve recall, particularly if the keyword list includes a search word that is not in the document text.
Field-restricted search. Some search engines enable users to limit full text searches to a particular field within a stored data record, such as "Title" or "Author."
Boolean queries. Searches that use Boolean operators (for example, "encyclopedia" AND "online" NOT "Encarta") can dramatically increase the precision of a full text search. The AND operator says, in effect, "Do not retrieve any document unless it contains both of these terms." The NOT operator says, in effect, "Do not retrieve any document that contains this word." If the retrieval list retrieves too few documents, the OR operator can be used to increase recall; consider, for example, "encyclopedia" AND "online" OR "Internet" NOT "Encarta". This search will retrieve documents about online encyclopedias that use the term "Internet" instead of "online." This increase in precision is very commonly counter-productive since it usually comes with a dramatic loss of recall.^[5]
Phrase search. A phrase search matches only those documents that contain a specified phrase, such as "Wikipedia, the free encyclopedia."
Concept search. A search that is based on multi-word concepts, for example Compound term processing. This type of search is becoming popular in many e-discovery solutions.
Concordance search. A concordance search produces an alphabetical list of all principal words that occur in a text with their immediate context.
Proximity search. A phrase search matches only those documents that contain two or more words that are separated by a specified number of words; a search for "Wikipedia" WITHIN2 "free" would retrieve only those documents in which the words "Wikipedia" and "free" occur within two words of each other.
Regular expression. A regular expression employs a complex but powerful querying syntax that can be used to specify retrieval conditions with precision.
Fuzzy search will search for document that match the given terms and some variation around them (using for instance edit distance to threshold the multiple variation)
Wildcard search. A search that substitutes one or more characters in a search query for a wildcard character such as an asterisk. For example, using the asterisk in a search query "s*n" will find "sin", "son", "sun", etc. in a text.

Improved search algorithms

The PageRank algorithm developed by Google gives more prominence to documents to which other Web pages have linked.^[6] See Search engine for additional examples.

Software

The following is a partial list of available software products whose predominant purpose is to perform full-text indexing and searching. Some of these are accompanied with detailed descriptions of their theory of operation or internal algorithms, which can provide additional insight into how full-text search may be accomplished.

Free and open source software

Related Research Articles

Information retrieval (IR) in computing and information science is the task of identifying and retrieving information system resources that are relevant to an information need. The information need can be specified in the form of a search query. In the case of document retrieval, queries can be based on full-text or other content-based indexing. Information retrieval is the science of searching for information in a document, searching for documents themselves, and also searching for the metadata that describes data, and for databases of texts, images or sounds.

In computing, a search engine is an information retrieval software system designed to help find information stored on one or more computer systems. Search engines discover, crawl, transform, and store information for retrieval and presentation in response to user queries. The search results are usually presented in a list and are commonly called hits. The most widely used type of search engine is a web search engine, which searches for information on the World Wide Web.

A query language, also known as data query language or database query language (DQL), is a computer language used to make queries in databases and information systems. In database systems, query languages rely on strict theory to retrieve information. A well known example is the Structured Query Language (SQL).

<span class="mw-page-title-main">Metasearch engine</span> Online information retrieval tool

A metasearch engine is an online information retrieval tool that uses the data of a web search engine to produce its own results. Metasearch engines take input from a user and immediately query search engines for results. Sufficient data is gathered, ranked, and presented to the users.

Document retrieval is defined as the matching of some stated user query against a set of free-text records. These records could be any type of mainly unstructured text, such as newspaper articles, real estate records or paragraphs in a manual. User queries can range from multi-sentence full descriptions of an information need to a few words.

Stop words are the words in a stop list which are filtered out before or after processing of natural language data (text) because they are deemed insignificant. There is no single universal list of stop words used by all natural language processing tools, nor any agreed upon rules for identifying stop words, and indeed not all tools even use such a list. Therefore, any group of words can be chosen as the stop words for a given purpose. The "general trend in [information retrieval] systems over time has been from standard use of quite large stop lists to very small stop lists to no stop list whatsoever".

The Text REtrieval Conference (TREC) is an ongoing series of workshops focusing on a list of different information retrieval (IR) research areas, or tracks. It is co-sponsored by the National Institute of Standards and Technology (NIST) and the Intelligence Advanced Research Projects Activity, and began in 1992 as part of the TIPSTER Text program. Its purpose is to support and encourage research within the information retrieval community by providing the infrastructure necessary for large-scale evaluation of text retrieval methodologies and to increase the speed of lab-to-product transfer of technology.

In computer science, an inverted index is a database index storing a mapping from content, such as words or numbers, to its locations in a table, or in a document or a set of documents. The purpose of an inverted index is to allow fast full-text searches, at a cost of increased processing when a document is added to the database. The inverted file may be the database file itself, rather than its index. It is the most popular data structure used in document retrieval systems, used on a large scale for example in search engines. Additionally, several significant general-purpose mainframe-based database management systems have used inverted list architectures, including ADABAS, DATACOM/DB, and Model 204.

A search engine is a software system that provides hyperlinks to web pages and other relevant information on the Web in response to a user's query. The user inputs a query within a web browser or a mobile app, and the search results are often a list of hyperlinks, accompanied by textual summaries and images. Users also have the option of limiting the search to a specific type of results, such as images, videos, or news.

Search engine indexing is the collecting, parsing, and storing of data to facilitate fast and accurate information retrieval. Index design incorporates interdisciplinary concepts from linguistics, cognitive psychology, mathematics, informatics, and computer science. An alternate name for the process, in the context of search engines designed to find web pages on the Internet, is web indexing.

Query expansion (QE) is the process of reformulating a given query to improve retrieval performance in information retrieval operations, particularly in the context of query understanding. In the context of search engines, query expansion involves evaluating a user's input and expanding the search query to match additional documents. Query expansion involves techniques such as:

A web query or web search query is a query that a user enters into a web search engine to satisfy their information needs. Web search queries are distinctive in that they are often plain text and boolean search directives are rarely used. They vary greatly from standard query languages, which are governed by strict syntax rules as command languages with keyword or positional parameters.

Enterprise search is software technology for searching data sources internal to a company, typically intranet and database content. The search is generally offered only to users internal to the company. Enterprise search can be contrasted with web search, which applies search technology to documents on the open web, and desktop search, which applies search technology to the content on a single computer.

Subject indexing is the act of describing or classifying a document by index terms, keywords, or other symbols in order to indicate what different documents are about, to summarize their contents or to increase findability. In other words, it is about identifying and describing the subject of documents. Indexes are constructed, separately, on three distinct levels: terms in a document such as a book; objects in a collection such as a library; and documents within a field of knowledge.

A concept search is an automated information retrieval method that is used to search electronically stored unstructured text for information that is conceptually similar to the information provided in a search query. In other words, the ideas expressed in the information retrieved in response to a concept search query are relevant to the ideas contained in the text of the query.

Ranking of query is one of the fundamental problems in information retrieval (IR), the scientific/engineering discipline behind search engines. Given a query $q$ and a collection $D$ of documents that match the query, the problem is to rank, that is, sort, the documents in $D$ according to some criterion so that the "best" results appear early in the result list displayed to the user. Ranking in terms of information retrieval is an important concept in computer science and is used in many different applications such as search engine queries and recommender systems. A majority of search engines use ranking algorithms to provide users with accurate and relevant results.

Vector space model or term vector model is an algebraic model for representing text documents as vectors such that the distance between vectors represents the relevance between the documents. It is used in information filtering, information retrieval, indexing and relevancy rankings. Its first use was in the SMART Information Retrieval System.

Legal information retrieval is the science of information retrieval applied to legal text, including legislation, case law, and scholarly works. Accurate legal information retrieval is important to provide access to the law to laymen and legal professionals. Its importance has increased because of the vast and quickly increasing amount of legal documents available through electronic means. Legal information retrieval is a part of the growing field of legal informatics.

Evaluation measures for an information retrieval (IR) system assess how well an index, search engine, or database returns results from a collection of resources that satisfy a user's query. They are therefore fundamental to the success of information systems and digital platforms.

Query understanding is the process of inferring the intent of a search engine user by extracting semantic meaning from the searcher’s keywords. Query understanding methods generally take place before the search engine retrieves and ranks results. It is related to natural language processing but specifically focused on the understanding of search queries. Query understanding is at the heart of technologies like Amazon Alexa, Apple's Siri. Google Assistant, IBM's Watson, and Microsoft's Cortana.

References

↑ In practice, it may be difficult to determine how a given search engine works. The search algorithms actually employed by web-search services are seldom fully disclosed out of fear that web entrepreneurs will use search engine optimization techniques to improve their prominence in retrieval lists.
↑ "Capabilities of Full Text Search System". Archived from the original on December 23, 2010.
↑ Coles, Michael (2008). Pro Full-Text Search in SQL Server 2008 (Version 1 ed.). Apress Publishing Company. ISBN 978-1-4302-1594-3.
↑ B., Yuwono; Lee, D. L. (1996). Search and ranking algorithms for locating resources on the World Wide Web. 12th International Conference on Data Engineering (ICDE'96). p. 164.
↑ Studies have repeatedly shown that most users do not understand the negative impacts of boolean queries.
↑ US 6285999,Page, Lawrence,"Method for node ranking in a linked database",published 1998-01-09,issued 2001-09-04. "A method assigns importance ranks to nodes in a linked database, such as any database of documents containing citations, the world wide web or any other hypermedia database. The rank assigned to a document is calculated from the ranks of documents citing it. In addition, the rank of a document is..."
↑ "SAP Adds HANA-Based Software Packages to IoT Portfolio | MarTech Advisor". www.martechadvisor.com.