Representing Fashion Product Data with Schema.org: Approach and Use Cases

Stolz, Alex; Hepp, Martin; Hemminger, Aleksei

doi:10.1007/978-3-319-69459-7_18

Cited by 1 publication

(1 citation statement)

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“…Since the outcome of ontology modeling techniques is strongly dependent on the nature of the data itself ( Saeeda et al, 2020 ), researchers have proposed specialized ontologies to better describe various aspects of a domain. As an example, Stolz, Hepp & Hemminger (2017) proposed a vocabulary for the domain of garments that can be used to express web page content and enable the Semantic web. However, such efforts remain limited in creating a large-scale impact unless officially adopted by Schema.org as a standard.…”

Section: Literature Reviewmentioning

confidence: 99%

Autonomous schema markups based on intelligent computing for search engine optimization

Abbasi

Fatima²,

Mukhtar

et al. 2022

PeerJ Computer Science

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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.

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Section: Literature Reviewmentioning

confidence: 99%