Machine learning and ontology-based novel semantic document indexing for information retrieval

Sharma, Añil; Kumar, Suresh

doi:10.1016/j.cie.2022.108940

Cited by 21 publications

(8 citation statements)

References 46 publications

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“…The model improves efficiency by proposing automated means of image retrieval (Zangeneh and McCabe, 2020). A novel hybrid semantic indexing technique is proposed with the combination of machine learning and ontology for unstructured text content (Sharma and Kumar, 2023). For the purpose to achieve higher performance and processing quantum machine learning is used effectively to compute different tasks (Umer and Sharif, 2022).…”

Section: Methodsmentioning

confidence: 99%

Enhancing Semantic Web Retrieval Through Ontology-Driven Feature Extraction: A Novel Proposition

Khan,

Sharif,

Mehmood

et al. 2024

Journal of Computer Science

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Web images represent unstructured data sets which often lead to challenges when users try to locate distinct images via text-based searches on the web. Such difficulties stem from different factors, e.g., redundant image storage, irrelevant metadata tags, and incorrect associations. To overcome this issue, we propose a semantic model based on the ontology language that enables users to find images that exactly match their queries. The proposed technique employs a simple procedure where users generate image captions by constructing an ontology for each image in the repository. In order to fit the existing ontology domains, the ontology generation relies on information gathered from the image's visual and textual elements, including low-level features like color, name, and shape. Next, constructing the ontology establishes accurate relationships with existing ontology concepts using the "an" and "is a part of" relationships. The resulting text with immersed ontology information yields accurate results, leading to easy retrieval using semantics keyword searches. Our framework relies on two main ontology concepts, i.e., animals and vehicles. In this study, we used a dataset of MAT files comprising images, content, and information to study the ontology of animals (e.g., wolves, foxes, and dogs) as well as the ontology of vehicles. The overall comparative evaluation of the proposed framework was performed under various conditions to obtain valuable insights.

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Section: Methodsmentioning

confidence: 99%

Enhancing Semantic Web Retrieval Through Ontology-Driven Feature Extraction: A Novel Proposition

Khan,

Sharif,

Mehmood

et al. 2024

Journal of Computer Science

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show abstract

“…A controlled vocabulary or term list is an ordered set of limited words and phrases, which are used to index content [31]. Vocabulary control is used to standardize the naming and provide uniformity, which improves indexing, browsing, and retrieval of data [32].…”

Section: ) Controlled Vocabularymentioning

confidence: 99%

“…Another issue is that the primary goals of digitization are to preserve analog information resources and their long-term storage as digital copies, as well as to enable access to these copies via digital products and networks and to gather them in digital libraries [31]. Access to information on the global network is made possible through digitization.…”

Section: ) Intellectual Propertymentioning

confidence: 99%

Cultural Heritage Information Retrieval: Past, Present, and Future Trends

Ranjgar,

Sadeghi-Niaraki,

Shakeri

et al. 2024

IEEE Access

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Knowledge organization and development of better information retrieval techniques were of great importance from a very early time period in human history. The need has grown high for such systems with the advent of digitization and the web era. Computer systems and web have offered easier retrieval of information in almost no time. However, as the amount of data increased, these systems were not able to work well in terms of accuracy and precision of retrieval. Semantic Web concept was introduced to overcome the issue by converting the web of documents to a web of data. Semantic Web technologies makes data machine-understandable so that information retrieval can be more precise and accurate. The Cultural Heritage (CH) community, with the goal of preserving and dissemination of the historical information to people and society, is one of the first domains to adopt Semantic Web recommendations and technologies, which can provide interoperability between various organizations by creating a shared understanding in the community. The data in the CH domain differs widely with types and formats. Also, a lot of organizations and experts from various fields interact through different processes within this community. Due to the mentioned needs, the CH community employed Semantic Web technologies step by step along its evolution process for better knowledge management and a uniform understanding among the community. In this study, we present a comprehensive conceptual framework that spans cultural heritage, information modeling, and information retrieval. Our model addresses early solutions in knowledge organization systems, highlighting the evolution from classification systems and controlled vocabularies to the significance of metadata schemas. We delve into the limitations of traditional knowledge organization systems and the necessity of formal ontologies, particularly in the cultural heritage domain. The comparative analysis of CRM vs. EDM, ontology-based metadata interoperability, and ontology technologies elucidate our contributions to the field. This paper outlines the process from the initial steps of adopting Semantic Web technologies in the CH domain to the latest developments in CH information retrieval. In this paper, we also reviewed intelligent applications and services developed in the CH domain after establishing semantic data models and Knowledge Organization Systems. Finally, challenges and possible future research directions are discussed. The findings revealed that GLAMs (Galleries, Libraries, Archives, and Museums) are excellent and comprehensive sources of CH information. The CH community has put in a lot of time and effort to develop data models and knowledge organization tools; now it's time to use this valuable resource to construct smart applications that are still in their early phases. This could benefit the CH industry even more.

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“…For example, understanding the user's current location or recent activities can impact the relevance of search results. Reinforcement Learning: Some PIR systems use reinforcement learning to adapt and improve recommendations over time [12], [13]. These models can learn from user feedback, such as clicks and engagement, to fine-tune the recommendations.…”

Section: Personalized Information Retrievalmentioning

confidence: 99%

“…Relevant Documents (based on gold standard): Documents 2,5,6,8,9,11,12,15,17,19 Search Engine Retrieval: Documents 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20 Now, calculation of several evaluation metrics for this scenario is shown below: These calculations provide a comprehensive evaluation of the search engine's performance for the given query, considering various evaluation metrics.…”

Section: Example Of Evaluation Metricsmentioning

confidence: 99%

Evaluating Semantic and Personalized Information Retrieval with Deep Learning Models: A Performance Metrics Analysis

Seema Verma

2023

tjjpt

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Evaluation of the Information Retrieval (IR) model is an important, complex process that involves evaluating many aspects of performance using quantitative measurements called evaluation metrics. This work explores the efficacy of Deep learning (DL) models in personalized and semantic information retrieval (SIR), an area that has witnessed significant advancements in the last few years. A thorough analysis of several DL models for search engine index retrieval is provided. The particular task, relevant standards, and desirable qualities, among others, influence the use of evaluation metrics in IR. A comprehensive evaluation of the performance of an IR system uses several metrics. The paper emphasizes how DL models can produce IR results that are more accurate, with an emphasis on choosing important metrics for evaluation. Among these metrics, on a given input query, a recall of 1.0 indicates full coverage in detecting pertinent cases, while a precision of 0.5 indicates 50% accuracy in positive predictions on a given input query. At 0.6667, the F1-score strikes a compromise between recall and precision. The model's ability to rank and prioritize is assessed by Mean Average Precision (MAP) at 0.5426 and Normalized Discounted Cumulative Gain (NDCG) at 0.8896. Precision in the top 5 suggestions is evaluated by Precision@5, which has a value of 0.6. This is important in situations where user attention or recommendation space are restricted. These measurements offer priceless information on the efficacy of IR models.

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Machine learning and ontology-based novel semantic document indexing for information retrieval

Cited by 21 publications

References 46 publications

Enhancing Semantic Web Retrieval Through Ontology-Driven Feature Extraction: A Novel Proposition

Enhancing Semantic Web Retrieval Through Ontology-Driven Feature Extraction: A Novel Proposition

Cultural Heritage Information Retrieval: Past, Present, and Future Trends

Evaluating Semantic and Personalized Information Retrieval with Deep Learning Models: A Performance Metrics Analysis

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