WINFRA: A Web-Based Platform for Semantic Data Retrieval and Data Analytics

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.

Integroly: Automatic Knowledge Graph Population from Social Big Data in the Political Marketing Domain

“…In general, three main approaches can be distinguished when dealing with OP for extracting domain-specific terms [55]: (i) rule-based ontology population systems [15,58], (ii) ontology population systems using ML or NLP [54,59], and (iii) ontology population systems that use statistical approaches [57]. In rule-based systems (i), the rule group is designed and developed for the location, classification, and extraction of information in predefined categories such as people, organizations, time expressions, places, etc., known as named entities.…”

“…The main disadvantage is the supervision in the selection of the initial concepts and the schema construction. In the same line, a blend of techniques is described in [59] using association rules, clustering, classification based on associations through advanced analytics employing NLP and data mining techniques. The system uses federated data analysis, knowledge discovery, information retrieval, and new techniques to deal with semantic web and KG representation.…”

Rule-Guided Compositional Representation Learning on Knowledge Graphs with Hierarchical Types

“…In recent years, great progress has been made in the representation learning of KGs [15][16][17][18], and many models are based on translation operations. TransE [10] is the most classic and representative translation-based model.…”