Knowledge Graphs and Knowledge Graph Embeddings

Knowledge graphs provide machines with structured knowledge of the world. Structured, machine-readable knowledge is necessary for a wide variety of artificial intelligence tasks such as search, translation, and recommender systems. These knowledge graphs can be embedded into a dense matrix representation for easier usage and storage. We first discuss knowledge graph components and knowledge base population to provide the necessary background knowledge. We then discuss popular methods of embedding knowledge graphs in chronological order. Lastly, we cover how knowledge graph embeddings improve both knowledge base population and a variety of artificial intelligence tasks

TULIP: A Five-Star Table and List - From Machine-Readable to Machine-Understandable Systems

Currently, Linked Data is increasing at a rapid rate as the growth of the Web. Aside from new information that has been created exclusively as Semantic Web-ready, part of them comes from the transformation of existing structural data to be in the form of five-star open data. However, there are still many legacy data in structured and semi-structured form, for example, tables and lists, which are the principal format for human-readable, waiting for transformation. In this chapter, we discuss attempts in the research area to transform table and list data to make them machine-readable in various formats. Furthermore, our research proposes a novel method for transforming tables and lists into RDF format while maintaining their essential configurations thoroughly. And, it is possible to recreate their original form back informatively. We introduce a system named TULIP which embodied this conversion method as a tool for the future development of the Semantic Web. Our method is more flexible compared to other works. The TULIP data model contains complete information of the source; hence it can be projected into different views. This tool can be used to create a tremendous amount of data for the machine to be used at a broader scale

Automated Knowledge Graph Completion for Natural Language Understanding: Known Paths and Future Directions

Knowledge Graphs (KGs) are large collections of structured data that can model real world knowledge and are important assets for the companies that employ them. KGs are usually constructed iteratively and often show a sparse structure. Also, as knowledge evolves, KGs must be updated and completed. Many automatic methods for KG Completion (KGC) have been proposed in the literature to reduce the costs associated with manual maintenance. Motivated by an industrial case study aiming to enrich a KG specifically designed for Natural Language Understanding tasks, this paper presents an overview of classical and modern deep learning completion methods. In particular, we delve into Large Language Models (LLMs), which are the most promising deep learning architectures. We show that their applications to KGC are affected by several shortcomings, namely they neglect the structure of KG and treat KGC as a classification problem. Such limitations, together with the brittleness of the LLMs themselves, stress the need to create KGC solutions at the interface between symbolic and neural approaches and lead to the way ahead for future research in intelligible corpus-based KGC