In this paper we provide a comprehensive introduction to knowledge graphs,
which have recently garnered significant attention from both industry and
academia in scenarios that require exploiting diverse, dynamic, large-scale
collections of data. After some opening remarks, we motivate and contrast
various graph-based data models and query languages that are used for knowledge
graphs. We discuss the roles of schema, identity, and context in knowledge
graphs. We explain how knowledge can be represented and extracted using a
combination of deductive and inductive techniques. We summarise methods for the
creation, enrichment, quality assessment, refinement, and publication of
knowledge graphs. We provide an overview of prominent open knowledge graphs and
enterprise knowledge graphs, their applications, and how they use the
aforementioned techniques. We conclude with high-level future research
directions for knowledge graphs.

Knowledge Graphs

We present the framework of ontology-based data access, a semantic paradigm for providing a convenient and user-friendly access to data repositories, which has been actively developed and studied in the past decade. Focusing on relational data sources, we discuss the main ingredients of ontology-based data access, key theoretical results, techniques, applications and future challenges.

/pdf/ontology-based-data-access-a-survey-46e31grqvo.pdf

Ontology-based data access: a survey

Mit seiner Publikation “One Size Fits All: An Idea Whose Time Has Come and Gone?“ [St05] hat Michael Stonebraker 2005 die Datenbankszene provoziert. Er vertritt dort die These, dass die eingeführten und in vielen Kontexten eingesetzten „traditionellen“ Datenbanksysteme zwar für viele datenzentrische Anwendungen eingesetzt werden, aber keineswegs die optimalen Systeme für viele dieser Anwendungen (OLAP, Stream Processing, XML Datenbanken, wissenschaftliche Datenbanken, Textsuche) sind. In einer späteren Publikation [St07] stellte er sogar die These auf, dass es keine Anwendungen gibt, für die die traditionellen Datenbanksysteme die beste Alternative sind. Er sagte voraus, dass sich in der kommerziellen Welt eine Anzahl unabhängiger spezialisierter „neuer“ Datenbanksysteme aufsplitten werde, die bestenfalls durch ein gemeinsames Frontend verbunden sind.

"One Size Fits All": An Idea Whose Time Has Come and Gone?

https://cyberleninka.org/article/n/813784.pdf

The use of ontologies for effective knowledge modelling and information retrieval

Over the past years, there has been a resurgence of Datalog-based systems in the database community as well as in industry. In this context, it has been recognized that to handle the complex knowledge-based scenarios encountered today, such as reasoning over large knowledge graphs, Datalog has to be extended with features such as existential quantification. Yet, Datalog-based reasoning in the presence of existential quantification is in general undecidable. Many efforts have been made to define decidable fragments. Warded Datalog+/- is a very promising one, as it captures PTIME complexity while allowing ontological reasoning. Yet so far, no implementation of Warded Datalog+/- was available. In this paper we present the Vadalog system, a Datalog-based system for performing complex logic reasoning tasks, such as those required in advanced knowledge graphs. The Vadalog system is Oxford's contribution to the VADA research programme, a joint effort of the universities of Oxford, Manchester and Edinburgh and around 20 industrial partners. As the main contribution of this paper, we illustrate the first implementation of Warded Datalog+/-, a high-performance Datalog+/- system utilizing an aggressive termination control strategy. We also provide a comprehensive experimental evaluation.

/pdf/the-vadalog-system-datalog-based-reasoning-for-knowledge-2bu3vrvv9v.pdf

The Vadalog system: datalog-based reasoning for knowledge graphs

We present Ontop, an open-source Ontology-Based Data Access (OBDA) system that allows for querying relational data sources through a conceptual representation of the domain of interest, provided in terms of an ontology, to which the data sources are mapped. Key features of Ontop are its solid theoretical foundations, a virtual approach to OBDA, which avoids materializing triples and is implemented through the query rewriting technique, extensive optimizations exploiting all elements of the OBDA architecture, its compliance to all relevant W3C recommendations (including SPARQL queries, R2RML mappings, and OWL2QL and RDFS ontologies), and its support for all major relational databases.

/pdf/ontop-answering-sparql-queries-over-relational-databases-3r4toru69s.pdf

Ontop: Answering SPARQL queries over relational databases

In this paper, we present the virtual knowledge graph (VKG) paradigm for data integration and access, also known in the literature as Ontology-based Data Access. Instead of structuring the integrat...

Virtual Knowledge Graphs: An Overview of Systems and Use Cases

Ontop is a popular open-source virtual knowledge graph system that can expose heterogeneous data sources as a unified knowledge graph. Ontop has been widely used in a variety of research and industrial projects. In this paper, we describe the challenges, design choices, new features of the latest release of Ontop v4, summarizing the development efforts of the last 4 years.

/pdf/the-virtual-knowledge-graph-system-ontop-2j34qqq085.pdf

The Virtual Knowledge Graph System Ontop

The database landscape has been significantly diversified during the last decade, resulting in the emergence of a variety of non-relational (also called NoSQL) databases, e.g., XML and JSON-document databases, key-value stores, and graph databases. To facilitate access to such databases and to enable data integration of non-relational data sources, we generalize the well-known ontologybased data access (OBDA) framework so as to allow for querying arbitrary databases through a mediating ontology. We instantiate this framework to MongoDB, a popular JSON-document database, and implement an prototype extension of the virtual OBDA system Ontop for answering SPARQL queries over MongoDB.

OBDA Beyond Relational DBs: A Study for MongoDB

OPTIONAL is a key feature in SPARQL for dealing with missing information While this operator is used extensively, it is also known for its complexity, which can make efficient evaluation of queries with OPTIONAL challenging We tackle this problem in the Ontology-Based Data Access (OBDA) setting, where the data is stored in a SQL relational database and exposed as a virtual RDF graph by means of an R2RML mapping We start with a succinct translation of a SPARQL fragment into SQL It fully respects bag semantics and three-valued logic and relies on the extensive use of the LEFT JOIN operator and COALESCE function We then propose optimisation techniques for reducing the size and improving the structure of generated SQL queries Our optimisations capture interactions between JOIN, LEFT JOIN, COALESCE and integrity constraints such as attribute nullability, uniqueness and foreign key constraints Finally, we empirically verify effectiveness of our techniques on the BSBM OBDA benchmark

/pdf/efficient-handling-of-sparql-optional-for-obda-1s4vanow0e.pdf

Benjamin Cogrel

Papers

Ontop: Answering SPARQL queries over relational databases

Virtual Knowledge Graphs: An Overview of Systems and Use Cases

The Virtual Knowledge Graph System Ontop

OBDA Beyond Relational DBs: A Study for MongoDB

Efficient Handling of SPARQL OPTIONAL for OBDA