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Mining information requires the right reliable source and lots of hard work. It is very common for Wikipedia editors to add a citation , such as to a newspaper or magazine article, a book chapter, or other hopefully reliable publication, to source the verifiability of a single fact in an article.
AMiner published several datasets for academic research purpose, including Open Academic Graph, [6] DBLP+citation [7] (a data set augmenting citations into the DBLP data from Digital Bibliography & Library Project), Name Disambiguation, [8] Social Tie Analysis. [9] For more available datasets and source codes for research, please refer to. [10]
The final step of knowledge discovery from data is to verify that the patterns produced by the data mining algorithms occur in the wider data set. Not all patterns found by the algorithms are necessarily valid. It is common for data mining algorithms to find patterns in the training set which are not present in the general data set.
Provides an RDF data set about scientific publications and related entities, such as authors, institutions, journals, and fields of study. The data set is based on the Microsoft Academic Graph. [105] [106] Free University of Freiburg: MyScienceWork: Science Database includes more than 70 million scientific publications and 12 million patents. Free
Data mining is a particular data analysis technique that focuses on statistical modeling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analysis that relies heavily on aggregation, focusing mainly on business information. [4]
Wikipedia and its sister projects—e.g. Wikimedia Commons, WikiSource—supported by the Wikimedia Foundation are hosted by servers (see Wikimedia servers on Meta-Wiki) at a data center in the state of Virginia, with an emergency backup data center in the state of Texas; caching servers are located in the Netherlands and Singapore.
Search engines and academic databases are often used to find sources. When searching for sources, it is wise to skim-read everything available (including abstracts of papers you cannot fully access) to get a feel for expert opinion on the most important aspects of a topic. Each system has quirks, advantages, and disadvantages.
Knowledge extraction is the creation of knowledge from structured (relational databases, XML) and unstructured (text, documents, images) sources.The resulting knowledge needs to be in a machine-readable and machine-interpretable format and must represent knowledge in a manner that facilitates inferencing.