The Vadalog System: Datalog-based Reasoning for Knowledge Graphs

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 knowl\-edge-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.Comment: Extended version of VLDB paper <https://doi.org/10.14778/3213880.3213888

Pace of Change: A Preliminary YesWorkflow Case Study

YesWorkflow (YW) is a grassroots initiative that “aims to provide a number of the benefits of using a scientific workflow management system without having to rewrite scripts and other scientific software.” YW represents an improvement to workflow modeling by allowing scientists and scholars to bypass traditional workflow engines by utilizing a small suite of modeling annotations that can be integrated as comments directly into an existing script. To date, YesWorkflow is still in its prototyping and development phase, but a handful of early adopters have already begun piloting the tool in a variety of scientific domains. To test the potential of utilizing YesWorkflow in a digital humanities context, this case study explores Professor Ted Underwood’s research outlined in the 2015 article “How Quickly do Literary Standards Change”, co-authored with Jordan Sellers and posted to FigShare with a link to the project’s GitHub repository.Ope

A Brief Tour through Provenance in Scientific Workflows and Databases

Within computer science, the term provenance has multiple meanings, due to different motivations, perspectives, and assumptions prevalent in the respective communities. This chapter provides a high-level “sightseeing tour” of some of those different notions and uses of provenance in scientific workflows and databases.Ope

A semantic and agent-based approach to support information retrieval, interoperability and multi-lateral viewpoints for heterogeneous environmental databases

PhDData stored in individual autonomous databases often needs to be combined and interrelated. For example, in the Inland Water (IW) environment monitoring domain, the spatial and temporal variation of measurements of different water quality indicators stored in different databases are of interest. Data from multiple data sources is more complex to combine when there is a lack of metadata in a computation forin and when the syntax and semantics of the stored data models are heterogeneous. The main types of information retrieval (IR) requirements are query transparency and data harmonisation for data interoperability and support for multiple user views. A combined Semantic Web based and Agent based distributed system framework has been developed to support the above IR requirements. It has been implemented using the Jena ontology and JADE agent toolkits. The semantic part supports the interoperability of autonomous data sources by merging their intensional data, using a Global-As-View or GAV approach, into a global semantic model, represented in DAML+OIL and in OWL. This is used to mediate between different local database views. The agent part provides the semantic services to import, align and parse semantic metadata instances, to support data mediation and to reason about data mappings during alignment. The framework has applied to support information retrieval, interoperability and multi-lateral viewpoints for four European environmental agency databases. An extended GAV approach has been developed and applied to handle queries that can be reformulated over multiple user views of the stored data. This allows users to retrieve data in a conceptualisation that is better suited to them rather than to have to understand the entire detailed global view conceptualisation. User viewpoints are derived from the global ontology or existing viewpoints of it. This has the advantage that it reduces the number of potential conceptualisations and their associated mappings to be more computationally manageable. Whereas an ad hoc framework based upon conventional distributed programming language and a rule framework could be used to support user views and adaptation to user views, a more formal framework has the benefit in that it can support reasoning about the consistency, equivalence, containment and conflict resolution when traversing data models. A preliminary formulation of the formal model has been undertaken and is based upon extending a Datalog type algebra with hierarchical, attribute and instance value operators. These operators can be applied to support compositional mapping and consistency checking of data views. The multiple viewpoint system was implemented as a Java-based application consisting of two sub-systems, one for viewpoint adaptation and management, the other for query processing and query result adjustment

Implementation of Web Query Languages Reconsidered

Visions of the next generation Web such as the "Semantic Web" or the "Web 2.0" have triggered the emergence of a multitude of data formats. These formats have different characteristics as far as the shape of data is concerned (for example tree- vs. graph-shaped). They are accompanied by a puzzlingly large number of query languages each limited to one data format. Thus, a key feature of the Web, namely to make it possible to access anything published by anyone, is compromised. This thesis is devoted to versatile query languages capable of accessing data in a variety of Web formats. The issue is addressed from three angles: language design, common, yet uniform semantics, and common, yet uniform evaluation. % Thus it is divided in three parts: First, we consider the query language Xcerpt as an example of the advocated class of versatile Web query languages. Using this concrete exemplar allows us to clarify and discuss the vision of versatility in detail. Second, a number of query languages, XPath, XQuery, SPARQL, and Xcerpt, are translated into a common intermediary language, CIQLog. This language has a purely logical semantics, which makes it easily amenable to optimizations. As a side effect, this provides the, to the best of our knowledge, first logical semantics for XQuery and SPARQL. It is a very useful tool for understanding the commonalities and differences of the considered languages. Third, the intermediate logical language is translated into a query algebra, CIQCAG. The core feature of CIQCAG is that it scales from tree- to graph-shaped data and queries without efficiency losses when tree-data and -queries are considered: it is shown that, in these cases, optimal complexities are achieved. CIQCAG is also shown to evaluate each of the aforementioned query languages with a complexity at least as good as the best known evaluation methods so far. For example, navigational XPath is evaluated with space complexity O(q d) and time complexity O(q n) where q is the query size, n the data size, and d the depth of the (tree-shaped) data. CIQCAG is further shown to provide linear time and space evaluation of tree-shaped queries for a larger class of graph-shaped data than any method previously proposed. This larger class of graph-shaped data, called continuous-image graphs, short CIGs, is introduced for the first time in this thesis. A (directed) graph is a CIG if its nodes can be totally ordered in such a manner that, for this order, the children of any node form a continuous interval. CIQCAG achieves these properties by employing a novel data structure, called sequence map, that allows an efficient evaluation of tree-shaped queries, or of tree-shaped cores of graph-shaped queries on any graph-shaped data. While being ideally suited to trees and CIGs, the data structure gracefully degrades to unrestricted graphs. It yields a remarkably efficient evaluation on graph-shaped data that only a few edges prevent from being trees or CIGs

A Knowledge Graph Based Integration Approach for Industry 4.0

The fourth industrial revolution, Industry 4.0 (I40) aims at creating smart factories employing among others Cyber-Physical Systems (CPS), Internet of Things (IoT) and Artificial Intelligence (AI). Realizing smart factories according to the I40 vision requires intelligent human-to-machine and machine-to-machine communication. To achieve this communication, CPS along with their data need to be described and interoperability conflicts arising from various representations need to be resolved. For establishing interoperability, industry communities have created standards and standardization frameworks. Standards describe main properties of entities, systems, and processes, as well as interactions among them. Standardization frameworks classify, align, and integrate industrial standards according to their purposes and features. Despite being published by official international organizations, different standards may contain divergent definitions for similar entities. Further, when utilizing the same standard for the design of a CPS, different views can generate interoperability conflicts. Albeit expressive, standardization frameworks may represent divergent categorizations of the same standard to some extent, interoperability conflicts need to be resolved to support effective and efficient communication in smart factories. To achieve interoperability, data need to be semantically integrated and existing conflicts conciliated. This problem has been extensively studied in the literature. Obtained results can be applied to general integration problems. However, current approaches fail to consider specific interoperability conflicts that occur between entities in I40 scenarios. In this thesis, we tackle the problem of semantic data integration in I40 scenarios. A knowledge graphbased approach allowing for the integration of entities in I40 while considering their semantics is presented. To achieve this integration, there are challenges to be addressed on different conceptual levels. Firstly, defining mappings between standards and standardization frameworks; secondly, representing knowledge of entities in I40 scenarios described by standards; thirdly, integrating perspectives of CPS design while solving semantic heterogeneity issues; and finally, determining real industry applications for the presented approach. We first devise a knowledge-driven approach allowing for the integration of standards and standardization frameworks into an Industry 4.0 knowledge graph (I40KG). The standards ontology is used for representing the main properties of standards and standardization frameworks, as well as relationships among them. The I40KG permits to integrate standards and standardization frameworks while solving specific semantic heterogeneity conflicts in the domain. Further, we semantically describe standards in knowledge graphs. To this end, standards of core importance for I40 scenarios are considered, i.e., the Reference Architectural Model for I40 (RAMI4.0), AutomationML, and the Supply Chain Operation Reference Model (SCOR). In addition, different perspectives of entities describing CPS are integrated into the knowledge graphs. To evaluate the proposed methods, we rely on empirical evaluations as well as on the development of concrete use cases. The attained results provide evidence that a knowledge graph approach enables the effective data integration of entities in I40 scenarios while solving semantic interoperability conflicts, thus empowering the communication in smart factories