Autonomous schema markups based on intelligent computing for search engine optimization

Abstract

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.

Enhancing knowledge graphs with microdata and LLMs: the case of Schema.org and Wikidata in touristic information

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Web Page Content Block Identification with Extended Block Properties

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Internet Web page content block dataset and solutions for its data labelling simplification

“…Automatizuotas duomenų išgavimas iš tinklalapių, remiantis HTML kodu, susiduria su iššūkiais, susijusiais su blokų hierarchinės prigimties atpažinimu, jų segmentavimu ir kategorizavimu (Cheng et al, 2019;Hashemi, 2020;Abbasi et al, 2022). Trūksta sprendimų, leidžiančių autonomiškai išgauti ir teisingai kategorizuoti duomenis iš nežinomų tinklalapio struktūrų, todėl siūlomas metodas, kurį taikant siekiama pagerinti tinklalapio segmentavimo sprendimus, apimant platesnį turinio blokų spektrą ir suteikiant įžvalgų apie jų vidinę struktūrą bei hierarchinius santykius.…”