Semantic Knowledge Graphing Finds Application in Several Problem Solving Applications, Including Business Intelligence, Internet Marketing, and Complex Systems Analysis

 

Semantic Knowledge Graphing (SKG) is integration of information into an ontology. The term knowledge graph was first introduced by Google in 2012. A knowledge graph acquires and integrates information into an ontology and applies a reasoner to derive new knowledge. SKG encodes knowledge graphs, or knowledge trees, and associated infrastructures such as knowledge bases, knowledge databases, and knowledge bases used in high semantic associations (such as those underlying natural language processing and image processing models).

The knowledge graph provides a structure for representing the meaning of sentences in an object-oriented language such as English. This framework was originally developed at the University of Sydney, Australia, by professors Richard Lazarus and John Heard. In its earliest form, semantic knowledge graphing, used data from a large-scale experiment in the Australian wool market, showed that the price difference in price could be predicted from the purchase history of a particular item. From the initial studies, knowledge graph representation formed the basis for SKG and has now been further developed to incorporate a wide range of problem solving applications, including business intelligence, internet marketing, complex systems analysis, e-business, financial markets, and social networks.

In its most recent paper, the researchers noted that big data can help achieve significant progress when the analysis and modeling process are able to accommodate a wide range of domain expertise, varying domains, and changing dimensions of expertise across domains. For this reason, semantic knowledge graphing is expected to play an important role in the formulation of new enterprise value models and in enabling technology innovation. Another recent publication by these researchers suggests that knowledge graph representation may also help to solve other practical problems. In the study, researchers examined whether the knowledge graph representation of domains in a knowledge base facilitates analysis and optimization. Although the question is more difficult than it seems, given that knowledge graph representations vary widely between domains and are often dynamic, the researchers suggest that knowledge graph representation may help with knowledge discovery and knowledge application. They did find that, for certain domains, a knowledge graph significantly reduced the dimensionality of the domain, and they suggested developing a new framework, in which the domain knowledge graph is coupled to an important knowledge extraction method such as the occurrence model or the knowledge discovery method.

Various semantic knowledge graphing solution providers are focused on making semantic data management technologies enterprise-ready and usable for business users. For instance, in January 2021, eccenca, a Germany-based provider of multi-disciplinary integrative platform for managing rules, constraints, capabilities, configurations and data in a single application, launched version 20.12 of its flagship knowledge graph software eccenca Corporate Memory. 

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