Building Semantic Knowledge Graphs from (Semi-)Structured Data: A Review

Knowledge graphs have, for the past decade, been a hot topic both in public and private domains, typically used for large-scale integration and analysis of data using graph-based data models. One of the central concepts in this area is the Semantic Web, with the vision of providing a well-defined meaning to information and services on the Web through a set of standards. Particularly, linked data and ontologies have been quite essential for data sharing, discovery, integration, and reuse. In this paper, we provide a systematic literature review on knowledge graph creation from structured and semi-structured data sources using Semantic Web technologies. The review takes into account four prominent publication venues, namely, Extended Semantic Web Conference, International Semantic Web Conference, Journal of Web Semantics, and Semantic Web Journal. The review highlights the tools, methods, types of data sources, ontologies, and publication methods, together with the challenges, limitations, and lessons learned in the knowledge graph creation processes.publishedVersio

Semantic Knowledge Graph Creation From Structured Data: A Systematic Literature Review

The term "knowledge graph" has for the past, almost 10 years, been a hot topic within the internet industry, both in public and private domains. Central within this technology is the concept of the Semantic Web, with the vision of providing semantics to the internet through standards set by the World Wide Web Consortium. But even though the Semantic Web vision was introduced in the early 2000s, the concept seems to not have gotten the same traction as knowledge graphs in general. One of the reasons for this is arguably the lack of consolidation of tools and methods used in the knowledge graph creation process, for the Semantic Web. This thesis provides a systematic literature review of the publication statistics, technical details and issues and limitations relating to knowledge graph creations for the Semantic Web from structured data. The review highlights the tools, methods, types of data sources, ontologies, publication formats, together with the limitations, issues and lessons learned in the creation processes. It includes 36 studies, using The Semantic Web Journal, Journal of Web Semantics, the International Semantic Web Conference and the Extended Semantic Web Conference as sources. The main findings from this research are that there have been substantially fewer publications the two last years than the two prior, the most described construction phases are ontology development, the RDF mapping process and publication, and there are limited tools available for non-experts. The findings are discussed based on current methodologies, and potential future work is presented

Building Semantic Knowledge Graphs from (Semi-)Structured Data: A Review

Knowledge graphs have, for the past decade, been a hot topic both in public and private domains, typically used for large-scale integration and analysis of data using graph-based data models. One of the central concepts in this area is the Semantic Web, with the vision of providing a well-defined meaning to information and services on the Web through a set of standards. Particularly, linked data and ontologies have been quite essential for data sharing, discovery, integration, and reuse. In this paper, we provide a systematic literature review on knowledge graph creation from structured and semi-structured data sources using Semantic Web technologies. The review takes into account four prominent publication venues, namely, Extended Semantic Web Conference, International Semantic Web Conference, Journal of Web Semantics, and Semantic Web Journal. The review highlights the tools, methods, types of data sources, ontologies, and publication methods, together with the challenges, limitations, and lessons learned in the knowledge graph creation processes

Building Semantic Knowledge Graphs from (Semi-)Structured Data: A Review

Knowledge graphs have, for the past decade, been a hot topic both in public and private domains, typically used for large-scale integration and analysis of data using graph-based data models. One of the central concepts in this area is the Semantic Web, with the vision of providing a well-defined meaning to information and services on the Web through a set of standards. Particularly, linked data and ontologies have been quite essential for data sharing, discovery, integration, and reuse. In this paper, we provide a systematic literature review on knowledge graph creation from structured and semi-structured data sources using Semantic Web technologies. The review takes into account four prominent publication venues, namely, Extended Semantic Web Conference, International Semantic Web Conference, Journal of Web Semantics, and Semantic Web Journal. The review highlights the tools, methods, types of data sources, ontologies, and publication methods, together with the challenges, limitations, and lessons learned in the knowledge graph creation processes