Information Retrieval Through Ontologies

By Authors: Yadvi Nanda and Sunil K Singh


Access to information has become quite complex, due to excessive use of information acquisition applications. This is so because the end-user has to write convoluted web search applications[1]. Furthermore, the end-user now has to apprehend the complex website structure but also has to understand or review the semantic relationship between the data stored in a database. These problems need to be overcome, so researchers are now focused on representing information and generating queries using ontologies. This would help show peculiar sites which user has demanded through research by improving the link between data and search applications, which is the main aim of using ontology-based information retrieval. This article would be based on methods for including ontology-based information in the mainstream[2].

Information retrieval through structured query formulation is quite in usage because it helps end-user to compile complex database queries. But still, for system users on different levels, it becomes difficult to formulate queries with a smaller number of refinement techniques. This increasing complexity of information retrieval has no end. Well, it has escalated more due to the use of data mining[2], decision support, and business analytics applications. Now the domain ontologies step in for information retrieval through semantic-based approaches. Using domain ontologies has various advantages: an accepted and detailed model is presented for a particular problem, not to forget how a semantic model of data can be defined with related domain information[3-4], different types of semantic information can be linked, and last, not least development of specific research strategies.

Now, what is web search? Retrieving website information. What is the need to find effective methods for information retrieval? It is due to the increase in our need for formal and informal information contained in web sources. As we have seen, how the development of visual aids has helped made interaction[4] with sites a lot easier, via the extraction of information through visual tools. This has made human communication efficient, by creating web queries. Many methods are being used such as form-based, question by example (QBE) or question be a template (QBT), etc. These methods are of no use when it comes to retrieving semantic data, nonetheless, no support is provided to generate complex queries[5-6.

Whereas the ontology browser visualizes the ontology as a tree, in many ontology-based query systems. An idea or keywords are selected from a visual tree defined by ontology concepts.

Key Features

The main focus of query programs that process ontology-based information retrieval[7]through visual presentation is on:

(1) Formation of a visual or co-operative question

(2) Ways to communicate information

(3) Methods for developing questionnaires.


Ontology-based interactive websites use visual representation to illustrate the search processes. For the creation of collective site queries, systems are to be built on the RDF structure and need to support the specialization or general implementation of basic or complementary ontology concepts. Linking all site conditions and ontology concepts related to them can surely be difficult and time-taking because it is hard to store all the data as a part of an ontology for a specific domain. For a large amount of data, it needs to be loaded into the head to perform select query functions[8].

For representing domain metadata with associated semantics taken out from related website schema through domain ontology, the first is important:

  1. Get to know the much amount of domain metadata and the relationships from a related website that can be converted into an ontology schema domain.
  2. Find a systematic plan to modify this metadata for the selected domain and the relationship to the ontology domain schema.

Illustrating domain information in domain ontology in terms of concepts and structures can be said as a logical definition for representing information. Still, usage of all the definitions of OWLs to make queries is not required. This calls for the requirement to identify OWL structures, for purpose of making related site queries, which can be used to specify domain information as conceptual queries. Not to forget, the difference in the making of the OWL statement and the compilation of the relationship query statements[9].


In this article, we understood ontology-based information retrieval, as to how the complexity of web[5] structure is increasing. It was defined as to why this model should be used and its relevance was well stated. It was explained how information is retrieved in ontology-based information retrieval, and how semantically is this information related to other source information. Majorly the advantages of ontology-based information retrieval were shown.


[1] Gupta, D., et al. Evolution of the Web 3.0: History and the Future.

[2] Chopra, M. et. al (2022). Predicting Catastrophic Events Using Machine Learning Models for Natural Language Processing.

[3] B. Gupta et. al Data Mining Approaches for Big Data and Sentiment Analysis in Social Media (pp. 223-243). IGI Global. http://doi:10.4018/978-1-7998-8413- 2.ch010

[4] Sunil Kr Singh et. al , “CAD for Delay Optimization of Symmetrical FPGA Architecture through Hybrid LUTs/PLAs” ACIT, VoL 178, page 581-59 1, Springer, 2012.

[5] Sudhakar Kumar, et al. (2021), Brain Computer Interaction (BCI): A Way to Interact with Brain Waves. Insights2Techinfo, pp. 1

[6] G Khade, et al. 2012 4th International Conference on Intelligent  Human Computer Interaction (IHCI), 2012. Classification of web pages on attractiveness: A supervised learning approach.

[7] Gupta, B. B., et al. (2019). Deep learning models for human centered computing in fog and mobile edge networks. Journal of Ambient Intelligence and Humanized Computing10(8), 2907-2911.

[8] Zhang, Z., et al. (2018). Efficient compressed ciphertext length scheme using multi-authority CP-ABE for hierarchical attributes. IEEE Access6, 38273-38284.

[9] Casillo, M., et. al (2022). Context Aware Recommender Systems: A Novel Approach Based on Matrix Factorization and Contextual Bias. Electronics11(7), 1003.

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