Named Entities as a Metadata Resource for Indexing and Searching Information

Izo, Flávio; Oliveira, Elias; Badué, Claudine

doi:10.1007/978-3-030-96308-8_78

Cited by 1 publication

(1 citation statement)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the previous sub-section, we discussed that the advances in deep learning and NLP-based models have led to an unprecedented level of sophistication in understanding user queries. On the upside, all these developments are related to search engine indexing and content processing and no changes have been applied to the content available on the web ( Saini et al, 2022 ; Izo, Oliveira & Badue, 2022 ). In other words, while the decades-old web pages remain untouched on the web servers, it is only the search engine's algorithms that have been updated to perform improved analysis on the indexed content.…”

Section: Introductionmentioning

confidence: 99%

Autonomous schema markups based on intelligent computing for search engine optimization

Abbasi

Fatima²,

Mukhtar

et al. 2022

PeerJ Computer Science

View full text Add to dashboard Cite

With advances in artificial intelligence and semantic technology, search engines are integrating semantics to address complex search queries to improve the results. This requires identification of well-known concepts or entities and their relationship from web page contents. But the increase in complex unstructured data on web pages has made the task of concept identification overly complex. Existing research focuses on entity recognition from the perspective of linguistic structures such as complete sentences and paragraphs, whereas a huge part of the data on web pages exists as unstructured text fragments enclosed in HTML tags. Ontologies provide schemas to structure the data on the web. However, including them in the web pages requires additional resources and expertise from organizations or webmasters and thus becoming a major hindrance in their large-scale adoption. We propose an approach for autonomous identification of entities from short text present in web pages to populate semantic models based on a specific ontology model. The proposed approach has been applied to a public dataset containing academic web pages. We employ a long short-term memory (LSTM) deep learning network and the random forest machine learning algorithm to predict entities. The proposed methodology gives an overall accuracy of 0.94 on the test dataset, indicating a potential for automated prediction even in the case of a limited number of training samples for various entities, thus, significantly reducing the required manual workload in practical applications.

show abstract

Section: Introductionmentioning

confidence: 99%