Generative AI provides an innovative and exciting way to manage knowledge and data at any scale; for small projects, at the enterprise level, and even at a world wide web scale. It is tempting to think that Generative AI has made other knowledge-based technologies obsolete; that anything we wanted to do with knowledge-based systems, Knowledge Graphs or even expert systems can instead be done with Generative AI. Our position is counter to that conclusion.Our practical experience on implementing enterprise question answering systems using Generative AI has shown that Knowledge Graphs support this infrastructure in multiple ways: they provide a formal framework to evaluate the validity of a query generated by an LLM, serve as a foundation for explaining results, and offer access to governed and trusted data. In this position paper, we share our experience, present industry needs, and outline the opportunities for future research contributions.

Property graphs have reached a high level of maturity, witnessed by multiple robust graph database systems as well as the ongoing ISO standardization effort aiming at creating a new standard Graph Query Language (GQL). Yet, despite documented demand, schema support is limited both in existing systems and in the first version of the GQL Standard. It is anticipated that the second version of the GQL Standard will include a rich DDL. Aiming to inspire the development of GQL and enhance the capabilities of graph database systems, we propose PG-Schema, a simple yet powerful formalism for specifying property graph schemas. It features PG-Schema with flexible type definitions supporting multi-inheritance, as well as expressive constraints based on the recently proposed PG-Keys formalism. We provide the formal syntax and semantics of PG-Schema, which meet principled design requirements grounded in contemporary property graph management scenarios, and offer a detailed comparison of its features with those of existing schema languages and graph database systems.

Incomplete data, in the form of null values, has been extensively studied since the inception of the relational model in the 1970s. Anecdotally, one hears that the way in which SQL, the standard language for relational databases, handles nulls creates a myriad of problems in everyday applications of database systems. To the best of our knowledge, however, the actual shortcomings of SQL in this respect, as perceived by database practitioners, have not been systematically documented, and it is not known if existing research results can readily be used to address the practical challenges. Our goal is to collect and analyze the shortcomings of nulls and their treatment by SQL, and to re-evaluate existing research in this light. To this end, we designed and conducted a survey on the everyday usage of null values among database users. From the analysis of the results we reached two main conclusions. First, null values are ubiquitous and relevant in real-life scenarios, but SQL's features designed to deal with them cause multiple problems. The severity of these problems varies depending on the SQL features used, and they cannot be reduced to a single issue. Second, foundational research on nulls is misdirected and has been addressing problems of limited practical relevance. We urge the community to view the results of this survey as a way to broaden the spectrum of their researches and further bridge the theory-practice gap on null values.

Knowledge graph (KG) is the backbone to enable cognitive Artificial Intelligence (AI), which relies on cognitive computing and semantic reasoning. Knowledge graph is the connected data with the semantically enriched context. It is the necessary step for the next move of AI. Our daily activities have closely intermingled with various applications powered by knowledge graphs. It has even entered our healthcare system to facilitate clinical decision making and improve hospital efficiency. This workshop aims to bring researchers and practitioners to promote research and applications related to knowledge graph.

We report on a community effort between industry and academia to shape the future of property graph constraints. The standardization for a property graph query language is currently underway through the ISO Graph Query Language (GQL) project. Our position is that this project should pay close attention to schemas and constraints, and should focus next on key constraints. The main purposes of keys are enforcing data integrity and allowing the referencing and identifying of objects. Motivated by use cases from our industry partners, we argue that key constraints should be able to have different modes, which are combinations of basic restriction that require the key to be exclusive, mandatory, and singleton. Moreover, keys should be applicable to nodes, edges, and properties since these all can represent valid real-life entities. Our result is PG-Keys, a flexible and powerful framework for defining key constraints, which fulfills the above goals. PG-Keys is a design by the Linked Data Benchmark Council's Property Graph Schema Working Group, consisting of members from industry, academia, and ISO GQL standards group, intending to bring the best of all worlds to property graph practitioners. PG-Keys aims to guide the evolution of the standardization efforts towards making systems more useful, powerful, and expressive.

Graphs have become the best way we know of representing knowledge. The computing community has investigated and developed the support for managing graphs by means of digital technology. Graph databases and knowledge graphs surface as the most successful solutions to this program. This tutorial will provide a conceptual map of the data management tasks underlying these developments, paying particular attention to data models and query languages for graphs

Knowledge Graphs can be considered as fulfilling an early vision in Computer Science of creating intelligent systems that integrate knowledge and data at large scale. Stemming from scientific advancements in research areas of Semantic Web, Databases, Knowledge representation, NLP, Machine Learning, among others, Knowledge Graphs have rapidly gained popularity in academia and industry in the past years. The integration of such disparate disciplines and techniques give the richness to Knowledge Graphs, but also present the challenge to practitioners and theoreticians to know how current advances develop from early techniques in order, on one hand, take full advantage of them, and on the other, avoid reinventing the wheel. This tutorial will provide a historical context on the roots of Knowledge Graphs grounded in the advancements of Logic, Data and the combination thereof.

We report on a community effort between industry and academia to shape the future of graph query languages. We argue that existing graph database management systems should consider supporting a query language with two key characteristics. First, it should be composable, meaning, that graphs are the input and the output of queries. Second, the graph query language should treat paths as first-class citizens. Our result is G-CORE, a powerful graph query language design that fulfills these goals, and strikes a careful balance between path query expressivity and evaluation complexity.

R2RML is used to specify transformations of data available in relational databases into materialised or virtual RDF datasets. SPARQL queries evaluated against virtual datasets are translated into SQL queries according to the R2RML mappings, so that they can be evaluated over the underlying relational database engines. In this paper we describe an extension of a well-known algorithm for SPARQL to SQL translation, originally formalised for RDBMS-backed triple stores, that takes into account R2RML mappings. We present the result of our implementation using queries from a synthetic benchmark and from three real use cases, and show that SPARQL queries can be in general evaluated as fast as the SQL queries that would have been generated by SQL experts if no R2RML mappings had been used.

Mapping relational databases to RDF is a fundamental problem for the development of the Semantic Web. We present a solution, inspired by draft methods defined by the W3C where relational databases are directly mapped to RDF and OWL. Given a relational database schema and its integrity constraints, this direct mapping produces an OWL ontology, which, provides the basis for generating RDF instances. The semantics of this mapping is defined using Datalog. Two fundamental properties are information preservation and query preservation. We prove that our mapping satisfies both conditions, even for relational databases that contain null values. We also consider two desirable properties: monotonicity and semantics preservation. We prove that our mapping is monotone and also prove that no monotone mapping, including ours, is semantic preserving. We realize that monotonicity is an obstacle for semantic preservation and thus present a non-monotone direct mapping that is semantics preserving.

In the past two years, the amount of data published in RDF and following the Linked Data principles has increased dramatically. Everyday people are publishing datasets as Linked Data. However, applications that consume Linked Data are not mainstream yet. To overcome this issue, we present a beginners tutorial on consuming Linked Data. We will discuss existing techniques how users can currently consume Linked Data and use it in their current applications.