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Product discovery utilizing the semantic data model
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AI
DOI：10.1007/s11042-022-13804-0.png)
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摘要
En 中文
Most of the existing techniques to product discovery and recommendations rely on syntactic approaches, thus ignoring valuable and specific semantic information of the underlying standards during the process. The product data comes from different heterogeneous sources and formats (text and multimedia) giving rise to the problem of interoperability. Above all, due to the continuously increasing influx of data, the manual labeling is getting costlier. Integrating the descriptions of different products into a single representation requires organizing all the products across vendors in a single taxonomy. Practically relevant and quality product categorization standards are still limited in number; and that too in academic research projects where we can majorly see only prototypes as compared to industry. This work presents a cost-effective aggregator semantic web portal for product catalogues on the Data Web as a digital marketplace. The proposed architecture creates a knowledge graph of available products through the ETL (Extract-Transform-Load)) approach and stores the resulting RDF serializations in the Jena triple store. User input textual and multimedia specifications for certain products are matched against the available product categories to recommend matching products with price comparison across the vendors. The experimental results show that semantic intelligence technologies could provide the necessary data integration and interoperability for efficient product/service discovery including multimedia.
Keyword:
Knowledge graph
Ontology
Engineering equipment
Multimedia data
Product categorization
Product matching
Recommendation
期刊
IF:
3
论文数:
2.0W
被引数:
3.2W
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