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

Knowledge management and Semantic Technology in the Health Care Revolution: Health 3.0 Model

Currently, the exploration, improvement, and application of knowledge management and semantic technologies to health care are in a revolution from Health 2.0 to Health 3.0. However, what accurately are knowledge management and semantic technologies and how can they improve a healthcare system? The study aims to review what constitute a Health 3.0 system, and identify key factors in the health care system. First, the study analyzes semantic web, definition of Health 2.0 and Health 3.0, new models for linked data: (1) semantic web and linked data graphs (2) semantic web and healthcare information challenges, OWL and linked knowledge, from linked data to linked knowledge, consistent knowledge representation, and Health 3.0 system. Secondly, the research analyzes two case studies of Health 3.0, and summarizes six key factors that constitute a Health 3.0 system. Finally, the study recommends the application of knowledge management and semantic technologies to Health 3.0 health care model requires the cooperation among emergency care, insurance companies, hospitals, pharmacies, government, specialists, academic researchers, and customer (patients)

Exploiting semantic web knowledge graphs in data mining

Data Mining and Knowledge Discovery in Databases (KDD) is a research field concerned with deriving higher-level insights from data. The tasks performed in that field are knowledge intensive and can often benefit from using additional knowledge from various sources. Therefore, many approaches have been proposed in this area that combine Semantic Web data with the data mining and knowledge discovery process. Semantic Web knowledge graphs are a backbone of many information systems that require access to structured knowledge. Such knowledge graphs contain factual knowledge about real word entities and the relations between them, which can be utilized in various natural language processing, information retrieval, and any data mining applications. Following the principles of the Semantic Web, Semantic Web knowledge graphs are publicly available as Linked Open Data. Linked Open Data is an open, interlinked collection of datasets in machine-interpretable form, covering most of the real world domains. In this thesis, we investigate the hypothesis if Semantic Web knowledge graphs can be exploited as background knowledge in different steps of the knowledge discovery process, and different data mining tasks. More precisely, we aim to show that Semantic Web knowledge graphs can be utilized for generating valuable data mining features that can be used in various data mining tasks. Identifying, collecting and integrating useful background knowledge for a given data mining application can be a tedious and time consuming task. Furthermore, most data mining tools require features in propositional form, i.e., binary, nominal or numerical features associated with an instance, while Linked Open Data sources are usually graphs by nature. Therefore, in Part I, we evaluate unsupervised feature generation strategies from types and relations in knowledge graphs, which are used in different data mining tasks, i.e., classification, regression, and outlier detection. As the number of generated features grows rapidly with the number of instances in the dataset, we provide a strategy for feature selection in hierarchical feature space, in order to select only the most informative and most representative features for a given dataset. Furthermore, we provide an end-to-end tool for mining the Web of Linked Data, which provides functionalities for each step of the knowledge discovery process, i.e., linking local data to a Semantic Web knowledge graph, integrating features from multiple knowledge graphs, feature generation and selection, and building machine learning models. However, we show that such feature generation strategies often lead to high dimensional feature vectors even after dimensionality reduction, and also, the reusability of such feature vectors across different datasets is limited. In Part II, we propose an approach that circumvents the shortcomings introduced with the approaches in Part I. More precisely, we develop an approach that is able to embed complete Semantic Web knowledge graphs in a low dimensional feature space, where each entity and relation in the knowledge graph is represented as a numerical vector. Projecting such latent representations of entities into a lower dimensional feature space shows that semantically similar entities appear closer to each other. We use several Semantic Web knowledge graphs to show that such latent representation of entities have high relevance for different data mining tasks. Furthermore, we show that such features can be easily reused for different datasets and different tasks. In Part III, we describe a list of applications that exploit Semantic Web knowledge graphs, besides the standard data mining tasks, like classification and regression. We show that the approaches developed in Part I and Part II can be used in applications in various domains. More precisely, we show that Semantic Web graphs can be exploited for analyzing statistics, building recommender systems, entity and document modeling, and taxonomy induction. %In Part III, we focus on semantic annotations in HTML pages, which are another realization of the Semantic Web vision. Semantic annotations are integrated into the code of HTML pages using markup languages, like Microformats, RDFa, and Microdata. While such data covers various domains and topics, and can be useful for developing various data mining applications, additional steps of cleaning and integrating the data need to be performed. In this thesis, we describe a set of approaches for processing long literals and images extracted from semantic annotations in HTML pages. We showcase the approaches in the e-commerce domain. Such approaches contribute in building and consuming Semantic Web knowledge graphs

Named Graphs as a Mechanism for Reasoning About Provenance

Named Graphs is a simple, compatible extension to the RDF abstract syntax that enables statements to be made about RDF graphs. This approach is in contrast to earlier attempts such as RDF reification, or knowledge-base specific extensions including quads and contexts. In this paper we demonstrate the use of Named Graphs and our experiences developing new kinds of semantic web application that build on Named Graphs for digital signatures, provenance, and semantic reasoning. We present a working example based on the Named Graphs for Jena (NG4J) API, from which we developed a semantic version control system for Software Engineering capable of reasoning about Named Graph-based provenance. We go on to discuss the implications of Named Graphs for Description Logics and semantic inference strategies