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

Modelling Knowledge about Software Processes using Provenance Graphs and its Application to Git-based Version Control Systems

Using the W3C PROV data model, we present a general provenance model for software development processes and, as an example, specialized models for git services, for which we generate provenance graphs. Provenance graphs are knowledge graphs, since they have defined semantics, and can be analyzed with graph algorithms or semantic reasoning to get insights into processes

1st International Workshop on Tabular Data Analysis (TaDA)

With the advent of data lakes and open data repositories containing heterogeneous collections of structured datasets, there is an increasing need for automated methods to analyze tabular data collections for a wide range of applications in data management, data science, and decision support. Our goal in this workshop was to bring together researchers and practitioners working on building such tabular data analysis solutions. TaDa workshop aimed to provide a venue for the growing number of researchers in data management, AI, and Semantic Web communities working on a wide range of problems relevant to tabular data analysis. The first edition of the workshop included two keynote talks, a research track comprising presentations and posters, and invited posters and virtual talks of the work done in these communities

Wiktionary matcher results for OAEI 2020

This paper presents the results of the Wiktionary Matcher in the Ontology Alignment Evaluation Initiative(OAEI) 2020.Wiktionary Matcher is an ontology matching tool that exploits Wiktionary as external background knowledge source. Wiktionary is a large lexical knowledge resource that is collaboratively built online. Multiple current language versions of Wiktionary are merged and used for monolingual ontology matching by exploiting synonymy relations and for multilingual matching by exploiting the translations given in the resource. This is the second OAEI participation of the matching system. Wiktionary Matcher has been improved and is the best performing system on the knowledge graph track this year

A Simple Temporal Information Matching Mechanism for Entity Alignment Between Temporal Knowledge Graphs

Entity alignment (EA) aims to find entities in different knowledge graphs (KGs) that refer to the same object in the real world. Recent studies incorporate temporal information to augment the representations of KGs. The existing methods for EA between temporal KGs (TKGs) utilize a time-aware attention mechanism to incorporate relational and temporal information into entity embeddings. The approaches outperform the previous methods by using temporal information. However, we believe that it is not necessary to learn the embeddings of temporal information in KGs since most TKGs have uniform temporal representations. Therefore, we propose a simple graph neural network (GNN) model combined with a temporal information matching mechanism, which achieves better performance with less time and fewer parameters. Furthermore, since alignment seeds are difficult to label in real-world applications, we also propose a method to generate unsupervised alignment seeds via the temporal information of TKG. Extensive experiments on public datasets indicate that our supervised method significantly outperforms the previous methods and the unsupervised one has competitive performance.Comment: Accepted by COLING 202