What is semantic integration?
"semantic integration" is a term used in several contexts across different areas of computer design, programming, management and management. In general, the aggregation of information from one or more heterogeneous resources to create a system in which information is organized in a way that makes sense to the user. Semantic integration often deals with defining and creating metadata connection or relationships between different parts of different data sources to logically structured. This might include the creation of a relational connection between two separate databases, creating a graph of how parts of different websites are related, or integrating factual data from an unknown, arbitrary format into a brief structure of the record. There are many practical applications for a fully implemented semantic integration system, including research libraries or networks, organic search engine algorithms that can extrapolate the context of from search and finally - using publishedMetadata - trouble -free integration of various computer data exchange systems.
The final goal of semantic integration in most cases is to be able to combine information in a dynamic way. In a very simple example, this could mean that it is able to assign a field in one database with fields in another database, although they are not accurate matches, such as a relationship with the "Size" field in the "Height" field. This Association could be done through user -defined rules that specifically interconnect them, or it could be done with algorithms that compare numeric field data and determine a probable match. The words "size" and "height" will then become the terms of metadata, which other external semantic integration systems could be able to use information for users without having to know that you will determine any only single system.
inComplex semantic integration systems such as research systems, metadata and sharing publications are a key part of operation. Metadata can be excluded from documents for creating large relational data structures that can help in questions. This means that research work on any topic can be integrated into a system that measures and records the frequency of words, and these words can help users search, allowing related topics to be based on any source without the need for specific conversions.
One of the challenges facing designers of semantic integration systems is how to aggregate data. The use of people to classify and establish relationships between data from different sources can be time -consuming and ultimately very dependent on the individual experience of a person. If algorithms are used to automatically create an association, certain relationships can be overlooked because of a little difference that the algorithm is unable to solve. ImplemensTace semantic integration on a large scale uses algorithms based on learning in conjunction with managing rules based on human rules and in some cases real human decision making during the process.