Information Retrieval is an area that has been experiencing increasing interest since the late 1950s. It is now becoming more important because of the value of information as a resource for the development of processes, for the acquisition of knowledge, to guarantee the sustainability of current organizations in the face of constant changes in the environment (customers, competitors, legislation, etc.).
The success of any operation within an organization depends on the quality of information available to decision-makers and individuals directly linked to the situation.
Information retrieval is the next step in determining information needs. It can be recovered through different tools: databases, Internet, thesauri, ontologies, maps... Knowing and managing these tools contribute to the quality recovery.
The recovery process is carried out through queries to the database where the structured information is stored, utilizing an appropriate interrogation language. It is necessary to take into account the key elements that allow the search, determining a greater degree of relevance and precision, such as indexes, keywords, thesauri and the phenomena that can occur in the process such as noise and documentary silence.
One of the problems that arise in the search for information is whether what we recover is "a lot or a little", that is, depending on the type of search, a multitude of documents can be recovered or simply a very small number. This phenomenon is called Silence or Documentary Noise.
Documentary silence: Those documents stored in the database but that have not been recovered, because the search strategy has been too specific or the keywords used are not adequate to define the search.
Documentary noise: Are those documents recovered by the system but are not relevant. This usually occurs when the search strategy has been defined too generic.
Tools for information retrieval:
- Internet (electronic journals, thematic and multi-thematic search engines, directories, met search engines)
- Smart agents
- Search equations
Information Recovery ModelsInformation retrieval models try to calculate the degree to which a given information element responds to a certain query. The three classic models and with greater use are:
Boolean: based on set theory and Boolean algebra. Measure the correspondence between the elements of the query and the documents.
Vectorial: it was raised and developed by Gerard Salton. Operates through vector algebra. It measures the degree to which the vectors that represent the query and the terms of the document diverged.
Probabilistic: it was proposed by Robertson and Spark-Jones. It is based on stochastic processes, operations of probability theory and Bayes theorem. The probability in which the document responds to the query is calculated. He frequently uses feedback with the user.
Main Problems of Information RetrievalInformation overload: the increasing volume of information on the Web to which users are exposed, and which generates problems in the moments of recovering it, since it returns to the user a large amount of information that is not relevant and relevant for the information query.
Documentary silence: information not recovered and that is relevant. This is because the search strategy has been too specific or the keywords used are not adequate to define the search.
Documentary noise: documents recovered by the system but not relevant. It usually occurs when the search strategy has been defined too generic.
Lexical phenomena: Polysemy: when a word has several meanings or meanings. Synonymy: two or more different words with the same meaning.
News Information retrieval techniquesDiffuse logic recovery systems
It allows queries with normal phrases and then the machine only processes the words that it considers relevant, not taking into account punctuation marks, articles, conjunctions, plurals, verb tenses, common words (which usually appear in all documents). The recovery is based on logical statements with values of true and false, taking into account the location of the word in the document. This technique allows us to refine our search because it eliminates punctuation marks, articles, conjunctions, plurals, verb tenses, and common words. In this way, the system will leave only the keywords increasing the accuracy of the search. Techniques for weighting terms
The weighting gives an adequate value to the search criteria, depending on the interests of the user; therefore the recovery of information depends on the assigned value. The most pertinent search document would be one that has all the search terms represented and also the one with the most value repeated more times, regardless of where it is located in the document. In this method, depending on the terms contained in the document and the frequency with which the system is repeated, each document will be valued, so it will order them according to its greater value.
It is a probabilistic model that allows the frequencies of the search terms in the retrieved documents. Some values (weights) are attributed that act as agents to group documents in order of importance, using ranking algorithms. It allows the frequency of the search term in the retrieved document. It is given some values that act as documentation grouping agents by hierarchy and by ranking algorithms.
Ranking algorithms represent a technique for document retrieval. One of the advantages of this method is that it eliminates the need to understand theoretical models, as in other algorithms; Ranking algorithms are oriented to the end user who can retrieve information using natural language, another feature is that the results are sorted by a Ranking based on a co-occurrence of the terms of the query.
Feedback techniques by relevance
After determining some search criteria and observing the recovered documents, the query is repeated but this time with the interesting elements, selected from the documents first recovered. Consists of maintaining the largest number of documents establishing different search strategies. Determining the search criteria and observing the recovery of documents, we repeat the query, but with more specialized terms.
In this technique the system will carry out two queries, in the first of them, some search criteria will be established with which documents will be obtained, and the second search will be made on the documents recovered in the first one, thus making a more exhaustive search
Eliminates the possible semantic confusions that can occur in the search of a concept, for it truncates the word and searches only for the root. The recovered documentation is based on the value obtained in the approval. The value depends on each of the relevant terms contained in the document and the repetition frequency.
This technique uses the morphology of the words instantiated in the search by truncating the prefixes and suffixes using different algorithms and leaving only the root, thus eliminating the semantic confusion that may occur in the search for the concept.
They intend to delimit efficiently the relevant documents. It achieves this through correct indexing in the process of processing documents with the help of indexes, thesauri, etc.; avoiding lexical and semantic ambiguities to establish consultations.
These are some of the most used techniques in the recovery of information. There have not been many updates on the techniques
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