Types of Queries in IR Systems
Different keywords are associated with the document set during the process of indexing. These keywords generally consist of words, phrases, and other characterizations of documents such as date created, author names, and type of document. They are used by an IR system to build an inverted index (see Section 27.5), which is then consulted during the search. The queries formulated by users are compared to the set of index keywords. Most IR systems also allow the use of Boolean and other operators to build a complex query. The query language with these operators enriches the expressiveness of a user’s information need.
1. Keyword Queries
Keyword-based queries are the simplest and most commonly used forms of IR queries: the user just enters keyword combinations to retrieve documents. The query keyword terms are implicitly connected by a logical AND operator. A query such as ‘database concepts’ retrieves documents that contain both the words ‘data-base’ and ‘concepts’ at the top of the retrieved results. In addition, most systems also retrieve documents that contain only ‘database’ or only ‘concepts’ in their text. Some systems remove most commonly occurring words (such as a, the, of, and so on, called stopwords) as a preprocessing step before sending the filtered query key-words to the IR engine. Most IR systems do not pay attention to the ordering of these words in the query. All retrieval models provide support for keyword queries.
2. Boolean Queries
Some IR systems allow using the AND, OR, NOT, ( ), + , and – Boolean operators in combinations of keyword formulations. AND requires that both terms be found. OR lets either term be found. NOT means any record containing the second term will be excluded. ‘( )’ means the Boolean operators can be nested using parentheses. ‘+’ is equivalent to AND, requiring the term; the ‘+’ should be placed directly in front of the search term. ‘–’ is equivalent to AND NOT and means to exclude the term; the ‘–’ should be placed directly in front of the search term not wanted. Complex Boolean queries can be built out of these operators and their combinations, and they are evaluated according to the classical rules of Boolean algebra. No ranking is possible, because a document either satisfies such a query (is “relevant”) or does not satisfy it (is “nonrelevant”). A document is retrieved for a Boolean query if the query is logically true as an exact match in the document. Users generally do not use combinations of these complex Boolean operators, and IR systems support a restricted version of these set operators. Boolean retrieval models can directly sup-port different Boolean operator implementations for these kinds of queries.
3. Phrase Queries
When documents are represented using an inverted keyword index for searching, the relative order of the terms in the document is lost. In order to perform exact phrase retrieval, these phrases should be encoded in the inverted index or implemented differently (with relative positions of word occurrences in documents). A phrase query consists of a sequence of words that makes up a phrase. The phrase is generally enclosed within double quotes. Each retrieved document must contain at least one instance of the exact phrase. Phrase searching is a more restricted and specific version of proximity searching that we mention below. For example, a phrase searching query could be ‘conceptual database design’. If phrases are indexed by the retrieval model, any retrieval model can be used for these query types. A phrase thesaurus may also be used in semantic models for fast dictionary searching for phrases.
4. Proximity Queries
Proximity search refers to a search that accounts for how close within a record multiple terms should be to each other. The most commonly used proximity search option is a phrase search that requires terms to be in the exact order. Other proximity operators can specify how close terms should be to each other. Some will also specify the order of the search terms. Each search engine can define proximity operators differently, and the search engines use various operator names such as NEAR, ADJ(adjacent), or AFTER. In some cases, a sequence of single words is given, together with a maximum allowed distance between them. Vector space models that also maintain information about positions and offsets of tokens (words) have robust implementations for this query type. However, providing support for complex proximity operators becomes computationally expensive because it requires the time-consuming preprocessing of documents, and is thus suitable for smaller document collections rather than for the Web.
5. Wildcard Queries
Wildcard searching is generally meant to support regular expressions and pattern matching-based searching in text. In IR systems, certain kinds of wildcard search support may be implemented—usually words with any trailing characters (for example, ‘data*’ would retrieve data, database, datapoint, dataset, and so on). Providing support for wildcard searches in IR systems involves preprocessing over-head and is not considered worth the cost by many Web search engines today. Retrieval models do not directly provide support for this query type.
6. Natural Language Queries
There are a few natural language search engines that aim to understand the structure and meaning of queries written in natural language text, generally as a question or narrative. This is an active area of research that employs techniques like shallow semantic parsing of text, or query reformulations based on natural language under-standing. The system tries to formulate answers for such queries from retrieved results. Some search systems are starting to provide natural language interfaces to provide answers to specific types of questions, such as definition and factoid questions, which ask for definitions of technical terms or common facts that can be retrieved from specialized databases. Such questions are usually easier to answer because there are strong linguistic patterns giving clues to specific types of sentences—for example, ‘defined as’ or ‘refers to’. Semantic models can provide support for this query type.
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