Abstract
Given the huge amount of heterogeneous data stored in different locations, it needs to be federated and semantically interconnected for further use. This paper introduces WINFRA, a comprehensive open-access platform for semantic web data and advanced analytics based on natural language processing (NLP) and data mining techniques (e.g., association rules, clustering, classification based on associations). The system is designed to facilitate federated data analysis, knowledge discovery, information retrieval, and new techniques to deal with semantic web and knowledge graph representation. The processing step integrates data from multiple sources virtually by creating virtual databases. Afterwards, the developed RDF Generator is built to generate RDF files for different data sources, together with SPARQL queries, to support semantic data search and knowledge graph representation. Furthermore, some application cases are provided to demonstrate how it facilitates advanced data analytics over semantic data and showcase our proposed approach toward semantic association rules.
Highlights
Semantic Web is a technology that aims to make knowledge understandable and machine-readable on the Web
We proposed an approach (Figure 3) based on association rules mining by using natural language processing (NLP) techniques to generate new relations from text data that can be used to enrich knowledge bases and knowledge graph representation, myPersonality knowledge base
We used the Apriori algorithm on the generated semantic transaction to extract frequent semantic itemset, which satisfies the minimum support requirements defined by the user and generate semantic association rules based on the user-defined confidence threshold
Summary
Semantic Web is a technology that aims to make knowledge understandable and machine-readable on the Web. The primary issue is incompleteness and insufficient integrated solution To deal with this issue, we applied data federation, semantic Web, NLP, and data mining techniques to develop a federated data system and proposed a new approach for semantic association rules extraction. The system allows users to interact with different data sources through SPARQL queries to do advanced data analytics and interactively visualize the result. Provide data analytics, including data exploration, which empowers users to explore the data via data mining algorithms (e.g., association rules, classification, clustering, semantic association rules), and search by queries to lead advanced analytics. Propose an approach to extract semantic association rules based on named entity recognition.
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