Information Retrieval System Explained: Types, Comparison & Components

An information retrieval (IR) system is a set of algorithms that facilitate the relevance of displayed documents to searched queries. In simple words, it works to sort and rank documents based on the queries of a user. There is uniformity with respect to the query and text in the document to enable document accessibility.

This also allows a matching function to be used effectively to rank a document formally using their Retrieval Status Value (RSV). The document contents are represented by a collection of descriptors, known as terms, that belong to a vocabulary V. An IR system also extracts feedback on the usability of the displayed results by tracking the user’s behaviour.

When we speak of search engines, we mean the likes of Google, Yahoo, and Bing among the general search engines. Other search engines include DBLP and Google Scholar. 

In this article, we will look at the different types of IR models, the components involved, and the techniques used in Information Retrieval to understand the mechanism behind search engines displaying results. 

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Types of Information Retrieval Model

An information retrieval comprises of the following four key elements:

  1. D − Document Representation.
  2. Q − Query Representation.
  3. F − A framework to match and establish a relationship between D and Q.
  4. R (q, di) − A ranking function that determines the similarity between the query and the document to display relevant information.

There are three types of Information Retrieval (IR) models:

1. Classical IR Model — It is designed upon basic mathematical concepts and is the most widely-used of IR models. Classic Information Retrieval models can be implemented with ease. Its examples include Vector-space, Boolean and Probabilistic IR models. In this system, the retrieval of information depends on documents containing the defined set of queries. There is no ranking or grading of any kind. The different classical IR models take Document Representation, Query representation, and Retrieval/Matching function into account in their modelling.

2. Non-Classical IR Model — They differ from classic models in that they are built upon propositional logic. Examples of non-classical IR models include Information Logic, Situation Theory, and Interaction models.

3. Alternative IR Model — These take principles of classical IR model and enhance upon to create more functional models like the Cluster model, Alternative Set-Theoretic Models Fuzzy Set model, Latent Semantic Indexing (LSI) model, Alternative Algebraic Models Generalized Vector Space Model, etc.

Let’s understand the most-adopted similarity-based classical IR models in further detail: 

1. Boolean Model — This model required information to be translated into a Boolean expression and Boolean queries. The latter is used to determine the information needed to be able to provide the right match when the Boolean expression is found to be true. It uses Boolean operations AND, OR, NOT to create a combination of multiple terms based on what the user asks. 

2. Vector Space Model — This model takes documents and queries denoted as vectors and retrieves documents depending on how similar they are. This can result in two types of vectors which are then used to rank search results either 

  • Binary in Boolean VSM.
  • Weighted in Non-binary VSM.

3. Probability Distribution Model — In this model, the documents are considered as distributions of terms and queries are matched based on the similarity of these representations. This is made possible using entropy or by computing the probable utility of the document. They are if two types:

  • Similarity-based Probability Distribution Model
  • Expected-utility-based Probability Distribution Model

4. Probabilistic Models — The probabilistic model is rather simple and takes the probability ranking to display results. To put it simply, documents are ranked based on the probability of their relevance to a searched query. 

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Components of Information Retrieval Model

Here are the prerequisites for an IR model: 

  1. An automated or manually-operated indexing system used to index and search techniques and procedures.
  2. A collection of documents in any one of the following formats: text, image or multimedia.
  3. A set of queries that serve as the input to a system, via a human or machine.
  4. An evaluation metric to measure or evaluate a system’s effectiveness (for instance, precision and recall). For instance, to ensure how useful the information displayed to the user is. 

The  various components of an Information Retrieval Model include: 

Step 1

Acquisition
The IR system sources documents and multimedia information from a variety of web resources. This data is compiled by web crawlers and is sent to database storage systems.

Step 2

Representation
The free-text terms are indexed, and the vocabulary is sorted, both using automated or manual procedures. For instance, a document abstract will contain a summary, meta description, bibliography, and details of the authors or co-authors.

Step 3

File Organization
File organization is carried out in one of two methods, sequential or inverted. Sequential file organization involves data contained in the document. The Inverted file comprises a list of records, in a term by term manner.

Step 4

Query
An IR system is initiated on entering a query. User queries can either be formal or informal statements highlighting what information is required. In IR systems, a query is not indicative of a single object in the database system. It could refer to several objects whichever match the query. However, their degrees of relevance may vary. 

Difference Between Information Retrieval and Data Retrieval

Data Retrieval systems directly retrieve data from database management systems like ODBMS by identifying keywords in the queries provided by users and matching them with the documents in the database. 

Whereas the Information Retrieval system in DBMS is a set of algorithms or programs that involve storing, retrieving, evaluation of document and query representations, esp text-based, to display results based on similarity.

S.No Information Retrieval Data Retrieval
1 Retrieves information based on the similarity between the query and the document. Retrieves data based on the keywords in the query entered by the user.
2 Small errors are tolerated and will likely go unnoticed. There is no room for errors since it results in complete system failure.
3 It is ambiguous and doesn’t have a defined structure. It has a defined structure with respect to semantics.
4 Does not provide a solution to the user of the database system. Provides solutions to the user of the database system.
5 Information Retrieval system produces approximate results Data Retrieval system produces exact results.
6 Displayed results are sorted by relevance  Displayed results are not sorted by relevance.
7 The IR model is probabilistic by nature. The Data Retrieval model is deterministic by nature.

Conclusion

This brings us to the end of the article. We hope you found the information helpful. If you are looking for more knowledge on Data Science concepts, you should check out India’s 1st NASSCOM certified PG Diploma in Data Science from IITB on upGrad. 

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