Web Semantics and Knowledge Graph

Abstract

In this modern era of big data, according to the researchers, it is estimated that almost 80% of the enterprise data is unstructured data, which means it is in the form of documents, research reports, surveys, articles, slides and even on the emails, etc. In the crucial time our world is where technology reign supreme, data is considered as the most important resources and even though we are surrounded by data, we cannot analyze it because of our traditional data technologies. Although there are new technologies on the way yet that is far from technologies, we need to unleash the full potential of data. Most of the unstructured data which comes from documents, reports, etc. are in the form of language phase, and it can entirely be a natural language sentence. In order for it to be analyzed, we need to transform, extract and process the relevant information from it. Hence for searching or merging the data from different data resources or platforms, semantic technology comes into the picture. Semantics application is steered by knowledge graph and that you do not need to go to hassle of data migration, as semantics connects it. Semantics can be used for all standard-based technologies removing the need for using different technologies for different data forms. While there are different enterprise catalogue products in the market, there is not a solution that fully understands the context of a user’s input, it can be either from a domain perspective, the depth of the knowledge the user is asking for or just comprehensively addressing the questions coming from the user perspective. For visualization purpose, you can compare semantic technology to a Google search; it is comparable in terms of results though Google uses natural language understanding (NLU), natural language processing (NLP) and other rich metadata. Semantic technologies also use some of the key points like metadata enrichment, ontologies and search and graph technologies to achieve the same feat and understanding the question from user perspective. For specific domains, there can be EKG or enterprise knowledge graph which consists of data which can be structured or unstructured. It can use information repositories and ontologies for building better domain-specific graphs. The end goal of semantic technologies is to make machine understand the data; semantic technologies use widely accepted RDF. Knowledge graphs are specific graphs containing the nodes and edges containing the useful information for a particular domain. Subject matter experts can be consulted with while building the knowledge graph in order for it to contain all the domain information it needs for answering the queries modeling knowledge domains can be considered as the core for semantic technologies, knowledge models defines entities, relations, attributes and values. Semantic technologies can also be integrated with machine learning models for in order to increase the effectiveness of the search engine. RDF or any other data model can be processed in a way of how human thinking works making it effective as it contains triples, i.e., subject, predicate, object which can be just natural language statements or very close to natural language phase.