Integrating updating domain knowledge data mining dev patel dating

The diverse big data and improved computer software and hardware enable an opportunity to understand the evolution of Earth system using simulation and data mining methods [6].

Many geoscience research outputs are recorded in the form of literature, making text data an integral part of geoscience big data [7].

In contrast, some domain-specific knowledge graphs only focus on one or a few topics.

For instance, the Music Brainz [28], Uni Prot KB [29], and Geo Name [30] are knowledge graphs in the music, biology, and geography fields, respectively.

Even if a researcher has only the basic skills in programming, he or she will be able to make a deep research using these libraries.

Nevertheless, due to the limitation of poor readability, automatic text summarization has yet to achieve satisfactory results.In recent years, many open knowledge graphs have been constructed based on text information, such as Google knowledge vault [21], DBpedia [22], Freebase [23], YAGO [24], Wikidata [25], Open IE [26], and NELL [27].These knowledge graphs devote to acquire entities and their links for various topics during the construction.Researchers can easily assemble publications of focused topics.In this regard, geological literature has become a big “mineral resource” for data mining and provides tremendous opportunities for new knowledge discovery.

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Knowledge graphs, as proposed by Google, are semantic networks with directed graph structure, which have provided new ideas to extract and represent the text information.

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