Association and Recomendation for Geosciences Data Attributes Based on Semantic Similarity Measurement
Shaohua Gao, Jia Song, Yunqiang Zhu, Chenyan Ma · 2018
With the arrival of the age of Big Data, geosciences data have been increasing at a tremendous speed. The complexity of geosciences data is increasing, and the demand for geosciences data is also expanding. Obtaining target data and associated data from massive multi-disciplinary, multi-sources, and multi-type geosciences data quickly and accurately, and then recommend it to the users is essential to promote the current development of geosciences data sharing. Semantic-oriented metadata association is an applicable approach to solve the association and recommendation of geosciences data. Based on the systematic analysis on metadata of geosciences data, this study presents a semantic similarity calculation model, which is used to calculate semantic similarity for geosciences data attributes. First, a detailed analysis is conducted on several existing geosciences data metadata standards within different research areas, and then metadata elements, which can stand for the geosciences data attributes, are extracted from them. Eight metadata elements between the extracted metadata elements, title, keywords, abstract, data type, data format, discipline, spatial range and temporal range, are selected as associated items. Thereafter, the semantic similarity of each associated item is calculated by taking four kinds of relations, the text relation, the category hierarchy, the time relation, and the spatial relation into account. In addition, to synthesize the results of the semantic similarity calculation of all associated items, the strategy of aggregation of multi-dimensional similarity scores is implemented to get the overall geosciences data semantic similarity. This study investigates the semantic association between geosciences data. The geosciences data can be recommended to the users in order of overall geosciences data semantic similarity. As a case study, the proposed association model has been applied to the retrieval of metadata from the Scientific Data Submission and Sharing Service platform for National Special Program on Basic Science and Technology Research of China. Compared with the retrieval method only relying on keyword matching, by taking consideration of the multiple geosciences data attributes, this model has the advantages that can explore the deeper semantic association between geosciences data, which is conducive to enrich the retrieval results, and has strong measurability, which is conducive to efficiently fulfill the users' requirements. Results of the experiment shows that this model is able to establish semantic relevancy on geosciences data attributes and recommend geosciences data from different research areas to the users. The association method of this study has a certain reference to the development of the accurate discovery, intelligent recommendation and sharing for massive geosciences data.