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Converting unstructured and semi-structured data into knowledge

With the rapid growth in number and dimension of databases and database applications in business, administrative, industrial and other fields, it is necessary to examine the automatic extraction of knowledge from these large databases. Due to knowledge extraction from databases, these have become rich and safe sources for generating and verification of knowledge, and the knowledge discovery can be applied in software management, querying process, making decisions, process control and many other fields of interest. At the same time, there is a challenge in managing unstructured data. Among organizations with large concentration of unstructured information, there is a greater tendency to devote more resources to this kind of data. The acquisition of knowledge from unstructured data is often difficult and expensive. Some possible solutions on extracting useful information (knowledge) from unstructured data are provided. Knowledge extraction is the process of creation of knowledge from structured, unstructured and semi-structured data. The objective of this paper is to present the possibilities of extracting knowledge from unstructured and semi-structured data particularly. The theories and tools for knowledge extraction are the subject of the emerging field of knowledge discovery in databases (KDD). Definitions of KDD are provided and the general multistep KDD process is outlined. A brief summary of recent KDD real-world applications is also provided. Finally, the article enumerates challenges for future research and development in KDD systems.