University of Twente Student Theses


Semantic-aware EWC Code Recommender System for Industrial Symbiosis Marketplace

Prakoso, Dimas Wibisono (2019) Semantic-aware EWC Code Recommender System for Industrial Symbiosis Marketplace.

[img] PDF
Abstract:The European Union (EU) has established 7th Environment Action program to 2020 as 'living well within the limits of the planet'. To support this program, the EU encourages its members to shift their economic system from a linear economy that focuses on resource use and disposal towards a circular economy. The EU views Industrial Symbiosis (IS) along with eco-design, remanufacturing, and eco-innovation as enabling factors to build the circular economy. IS is defined as a collaboration between company by exchanging materials, energy/utility, water, and by-products as feedstock for an industrial process. The EU funded a project of web-based IS marketplace platform called Sharebox to stimulate its member in adopting IS. Sharebox users can sell their secondary product or waste by registering it to the system and supplying it with waste item description and appropriate European Waste Catalogue (EWC) code. The process of labeling a waste item with EWC code is difficult because there are 841 EWC codes which are hard to memorize. Therefore, we need a system that is able to recommend the EWC Code accurately. This research aims to design methods that can recommend the EWC code accurately for certain waste items. We designed three methods, namely String-based (SB), Knowledge-based (KB) and Corpus-based (CB) EWC Code Recommender System (RS). The SB works by aggregating the string similarity between words contained in the waste item and EWC code description. However, it could not comprehend words and sentences that are lexically different but semantically similar. Therefore, we designed KB and CB methods, which have semantic awareness capabilities to address the problem. KB achieves this by utilizing WordNet-based word similarity, whereas SB by exploiting the relationship between word vectors produced by word2vec algorithm trained on a news corpus. The experiment result shows the incorporation of semantic-awareness could improve the performance of the EWC Code RS. In Top-10 EWC Code RS, the SB method could achieve recall, precision, and ARHR by 34.4%, 33.9%, and 15.4%. The KB which utilize semantic-awareness could achieve better performance by 38.3%, 35.2%, and 15.4%. The CB perform even higher by 39.2%, 35.9%, and 16.7%.
Item Type:Essay (Master)
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:54 computer science
Programme:Business Information Technology MSc (60025)
Link to this item:
Export this item as:BibTeX
HTML Citation
Reference Manager


Repository Staff Only: item control page