Enterprises are creating domain-specific knowledge graphs by curating and integrating their business data from multiple sources. The data in these knowledge graphs can be described using ontologies, which provide a semantic abstraction to define the content in terms of the entities and the relationships of the domain. The rich semantic relationships in an ontology contain a variety of opportunities to reduce edge traversals and consequently improve the graph query performance. Although there has been a lot of effort to build systems that enable efficient querying over knowledge graphs, the problem of schema optimization for query performance has been largely ignored in the graph setting. In this work, we show that graph schema design has significant impact on query performance, and then propose optimization algorithms that exploit the opportunities from the domain ontology to generate efficient property graph schemas. To the best of our knowledge, we are the first to present an ontology-driven approach for property graph schema optimization. We conduct empirical evaluations with two real-world knowledge graphs from medical and financial domains. The results show that the schemas produced by the optimization algorithms achieve up to 2 orders of magnitude speed-up compared to the baseline approach.
The design space of visual tools that aim to help people create schemas for property graphs is explored. Interviews are conducted with experts in the domain of property graphs and data management in general. Through this collaboration, we determine how a schema extraction tool can provide value. These insights are used to establish design requirements and design a UI prototype, which are then relayed back to the experts. Positive reactions were received, which encourage future work in the property graph schema space.
In this paper, we propose a new approach for the computation of the statistics of knowledge graphs. We introduce a schema graph that represents the main framework for the computation of the statistics. The core of the procedure is an algorithm that determines the sub-graph of the schema graph affected by the insertion of one triple into the triple-store. We first present the algorithms that use the minimal schema and the complete schema of a knowledge graph. Furthermore, we propose an algorithm in which the size of the schema graph can be controlled -- it is based on the $n$ levels from the up…
We present a case study in applied category theory written from the point of view of an applied domain: the formalization of the widely-used property graphs data model in an enterprise setting using elementary constructions from type theory and category theory, including limit and co-limit sketches. Observing that algebraic data types are a common foundation of most of the enterprise schema languages we deal with in practice, for graph data or otherwise, we introduce a type theory for algebraic property graphs wherein the types denote both algebraic data types in the sense of functional progra…
Despite the maturity of commercial graph databases, little consensus has been reached so far on the standardization of data definition languages (DDLs) for property graphs (PG). The discussion on the characteristics of PG schemas is ongoing in many standardization and community groups. Although some basic aspects of a schema are already present in Neo4j 3.5, like in most commercial graph databases, full support is missing allowing to constraint property graphs with more or less flexibility. In this paper, we focus on two different perspectives from which a PG schema should be considered, as be…
Transforming Shape Schemas with Composable Property-Graph Queries (Extended Version)
The Software Languages Team（软件语言团队）；University of Koblenz（科伦茨大学）；Institute for Artificial Intelligence（人工智能研究所）；University of Stuttgart（斯图加特大学）；University of Southampton（南安普顿大学）
Property graph manages data by vertices and edges. Each vertex and edge can have a property map, storing ad hoc attribute and its value. Label can be attached to vertices and edges to group them. While this schema-less methodology is very flexible for data evolvement and for managing explosive graph element, it has two shortcomings-- 1) data dependency 2) less compression. Both problems can be solved by a schema based approach. In this paper, a type system used to model property graph is defined. Based on the type system, the associated data definition language (DDL) is proposed and multiple g…
Free University of Bozen-Bolzano（博尔扎诺自由大学）；University of Trento（特伦托大学）
One of the major barriers to the training of algorithms on knowledge graph schemas, such as vocabularies or ontologies, is the difficulty that scientists have in finding the best input resource to address the target prediction tasks. In addition to this, a key challenge is to determine how to manipulate (and embed) these data, which are often in the form of particular triples (i.e., subject, predicate, object), to enable the learning process. In this paper, we describe the LiveSchema initiative, namely a gateway that offers a family of services to easily access, analyze, transform and exploit …
RDF triplestores and property graph databases are two approaches for data management which are based on modeling, storing, and querying graph-like data. In spite of such common principles, they present special features that complicate the task of database interoperability. While there exist some methods to transform RDF graphs into property graphs, and vice versa, they lack compatibility and a solid formal foundation. This paper presents three direct mappings (schema-dependent and schema-independent) for transforming an RDF database into a property graph database, including data and schema. We…
It is increasingly common to find complex data represented through the graph model. Contrary to relational models, graphs offer a high capacity for executing analytical tasks on complex data. Since a huge amount of data is still presented in terms of relational tables, it is necessary to understand how to translate this data into graphs. This paper proposes a complete mapping process that allows transforming any relational database (schema and instance) into a property graph database (schema and instance). Contrary to existing mappings, our solution preserves the three fundamental mapping prop…
The use of external background knowledge can be beneficial for the task of matching schemas or ontologies automatically. In this paper, we exploit six general-purpose knowledge graphs as sources of background knowledge for the matching task. The background sources are evaluated by applying three different exploitation strategies. We find that explicit strategies still outperform latent ones and that the choice of the strategy has a greater impact on the final alignment than the actual background dataset on which the strategy is applied. While we could not identify a universally superior resour…
Considering relational databases having powerful capabilities in handling security, user authentication, query optimization, etc., several commercial and academic frameworks reuse relational databases to store and query semi-structured data (e.g., XML, JSON) or graph data (e.g., RDF, property graph). However, these works concentrate on managing one of the above data models with RDBMSs. That is, it does not exploit the underlying tools to automatically generate the relational schema for storing multi-model data. In this demonstration, we present a novel reinforcement learning-based tool called …
We present a formal semantics and proof of soundness for shapes schemas, an expressive schema language for RDF graphs that is the foundation of Shape Expressions Language 2.0. It can be used to describe the vocabulary and the structure of an RDF graph, and to constrain the admissible properties and values for nodes in that graph. The language defines a typing mechanism called shapes against which nodes of the graph can be checked. It includes an algebraic grouping operator, a choice operator and cardinality constraints for the number of allowed occurrences of a property. Shapes can be combined…
We present Shape Expressions (ShEx), an expressive schema language for RDF designed to provide a high-level, user friendly syntax with intuitive semantics. ShEx allows to describe the vocabulary and the structure of an RDF graph, and to constrain the allowed values for the properties of a node. It includes an algebraic grouping operator, a choice operator, cardinalitiy constraints for the number of allowed occurrences of a property, and negation. We define the semantics of the language and illustrate it with examples. We then present a validation algorithm that, given a node in an RDF graph an…
Free University of Bozen-Bolzano, Italy（博洛尼亚-博兹纳自由大学，意大利）；Université Paris-Saclay, CNRS, LISN, France（巴黎-萨克雷大学，法国 CNRS LISN）；Birkbeck, University of London, UK（伦敦大学伯克贝克学院，英国）；University of Huddersfield, UK（赫德斯菲尔德大学，英国）；Relational AI, Berkeley, CA, USA（关系AI，美国加州伯克利）；Hasselt University, Hasselt, Belgium（哈塞尔特大学，比利时）；University of Wrocław, Poland（沃林福大学，波兰）
Knowledge graphs have emerged as expressive data structures for Web data. Knowledge graph potential and the demand for ecosystems to facilitate their creation, curation, and understanding, is testified in diverse domains, e.g., biomedicine. The Shapes Constraint Language (SHACL) is the W3C recommendation language for integrity constraints over RDF knowledge graphs. Enabling quality assements of knowledge graphs, SHACL is rapidly gaining attention in real-world scenarios. SHACL models integrity constraints as a network of shapes, where a shape contains the constraints to be fullfiled by the sam…
Given a set of documents from a specific domain (e.g., medical research journals), how do we automatically build a Knowledge Graph (KG) for that domain? Automatic identification of relations and their schemas, i.e., type signature of arguments of relations (e.g., undergo(Patient, Surgery)), is an important first step towards this goal. We refer to this problem as Relation Schema Induction (RSI). In this paper, we propose Schema Induction using Coupled Tensor Factorization (SICTF), a novel tensor factorization method for relation schema induction. SICTF factorizes Open Information Extraction (O…
Schema.org is a widely adopted vocabulary for semantic annotation of content and data. However, its generic nature makes it complicated for data publishers to pick right types and properties for a specific domain and task. In this paper we propose a formal approach, a domain specification process that generates domain specific patterns by applying operators implemented in SHACL to the schema.org vocabulary. These patterns can support knowledge generation and assessment processes for specific domains and tasks. We demonstrated our approach with use cases in tourism domain.
University of Trento（特伦托大学）；Free University of Bozen-Bolzano（博尔扎诺自由大学）
When building a new application we are more and more confronted with the need of reusing and integrating pre-existing knowledge, e.g., ontologies, schemas, data of any kind, from multiple sources. Nevertheless, it is a fact that this prior knowledge is virtually impossible to reuse as-is. This difficulty is the cause of high costs, with the further drawback that the resulting application will again be hardly reusable. It is a negative loop which consistently reinforces itself. iTelos is a general purpose methodology aiming at minimizing as much as possible the effects of this loop. iTelos is b…
We address the problem of semantic querying of relational databases (RDB) modulo knowledge bases using very expressive knowledge representation formalisms, such as full first-order logic or its various fragments. We propose to use a first-order logic (FOL) reasoner for computing schematic answers to deductive queries, with the subsequent instantiation of these schematic answers using a conventional relational DBMS. In this research note, we outline the main idea of this technique -- using abstractions of databases and constrained clauses for deriving schematic answers. The proposed method can …
TU Wien（维也纳技术大学）；Univ. Lille（里尔大学）；CNRS（法国国家科学研究中心）；Inria（法国国家信息与自动化研究所）；Centrale Lille（里尔中央理工学院）；Birkbeck, University of London（伦敦大学伯贝克学院）；University of Oviedo（奥维耶多大学）；University of Bayreuth（拜罗伊特大学）；Università di Napoli Federico II（那不勒斯费德里科二世大学）；University of Warsaw（华沙大学）；Paderborn University（帕德博恩大学）；WU Wien（维也纳经济大学）；CSH Vienna（维也纳计算机科学中心）；Free University of Bolzano（波尔扎诺自由大学）；University of Bialystok（比亚韦斯托克大学）
Some property graph databases do not have a fixed schema, which can result in data type inconsistencies for properties on nodes and relationships, especially when importing data into a running database. Here we present a tool which can rapidly produce a detailed report on every property in the graph. When executed on a large knowledge graph, it allowed us to debug a complex ETL process and enforce 100% data type consistency.
