DataCat: Attention-based Open Government Data (OGD) Category Recommendation Framework

A data category recommendation framework for Thailand’s open government data portal (ThOGD) is proposed to assist data providers when publishing and registering a new dataset into the portal’s data catalog. However, existing approaches such as a multi-label classification problem, have not adopted the semantic features of data categories sufficiently. Deep learning model for Natural Language Processing has recently demonstrated to achieve high potential in learning the different degrees of semantic feature abstraction because all layers of multi-head attention blocks are provided with different fragments of metadata descriptions and corresponding tags. To obtain a robust recommendation result, this paper proposes DataCat: a Category Recommendation Framework using the attention-based framework through the ThOGD portal. Within this framework, the integrated multi-layers with particular semantic information are directly attached to the output layer of a network to enhance the effectiveness of information retrieval. The results point out that the attention-based framework has a weighted effect on loss of optimization. The performance when looking at the macro average of precision and F1-score improves by 0.664% and 0.557%, respectively. The micro average of those improves by 0.806%, and 0.698%, respectively.