Knowledge Graph for Solubility Big Data: Construction and Applications

Abstract

Dissolution refers to the process in which solvent molecules and solute molecules attract and combine with each other. The extensive solubility data generated from the dissolution of various compounds under different conditions, is distributed across structured or semi‐structured formats in various media, such as text, web pages, tables, images, and databases. These data exhibit multi‐source and unstructured features, aligning with the typical 5 V characteristics of big data. A solubility big data technology system has emerged under the fusion of solubility data and big data technologies. However, the acquisition, fusion, storage, representation, and utilization of solubility big data are encountering new challenges. Knowledge Graphs, known as extensive systems for representing and applying knowledge, can effectively describe entities, concepts, and relations across diverse domains. The construction of solubility big data knowledge graph holds substantial value in the retrieval, analysis, utilization, and visualization of solubility knowledge. Throwing out a brick to attract a jade, this paper focuses on the solubility big data knowledge graph and, firstly, summarizes the architecture of solubility knowledge graph construction. Secondly, the key technologies such as knowledge extraction, knowledge fusion, and knowledge reasoning of solubility big data are emphasized, along with summarizing the common machine learning methods in knowledge graph construction. Furthermore, this paper explores application scenarios, such as knowledge question answering and recommender systems for solubility big data. Finally, it presents a prospective view of the shortcomings, challenges, and future directions related to the construction of solubility big data knowledge graph. This article proposes the research direction of solubility big data knowledge graph, which can provide technical references for constructing a solubility knowledge graph. At the same time, it serves as a comprehensive medium for describing data, resources, and their applications across diverse fields such as chemistry, materials, biology, energy, medicine, and so on. It further aids in knowledge retrieval and mining, analysis and utilization, and visualization across various disciplines.