Tender calls search using a procurement product named entity recogniser. (April 2018)

• Record Type: Journal Article
• Authors: Mehrbod, Ahmad; Grilo, António
• Is Part Of: Advanced engineering informatics. Volume 36(2018)
• Page Start: 216
• Page End: 228
• Publication Date: 2018-04
• Subjects: Product named entity recognition -- e-Procurement marketplace -- Tender; Computer-aided engineering -- Periodicals; Engineering -- Data processing -- Periodicals; 620.00285
• DOI: 10.1016/j.aei.2018.04.005
• Languages: English
• ISSNs: 1474-0346

Abstract

Abstract: A product search service in an e-Procurement Marketplace can help the suppliers to find the best suitable tenders according to their products. Various possible ways to define and specify a product by different companies make it difficult to match a tender as a product request with the similar products offered by the suppliers. Semantic search engines try to overcome this problem by understanding the intent and contextual meaning of the words within a search domain. A fundamental part of such search engines can be a named entity recogniser that extracts desired searchable elements from the search context. This paper develops a recogniser that can extract "Procurement Product" mentions from tenders and other procurement documents. A self-learning approach has been adopted in order to train the model for extracting product mentions. The proposed approach uses already known product mentions in tenders as the training data to train the model and then use the trained model to recognize the product mentions from other tenders. The accuracy of the model has been tested evaluated using tenders that have been published in public procurement e-marketplaces. The results show that the proposed approach achieved high values of precision and recall in different test datasets. The recogniser can be used as the search element extractor for semantic search in procurement e-marketplaces. Therefore, the improvement of search performance by using the recogniser is also tested in finding tenders from different public procurement resources. The results show the semantic search process which uses the recogniser improves the search precision by about 25%.