Knowledge Graph Exploration: A Usability Evaluation of Query Builders for Laypeople

SPARQL enables users to access and browse knowledge graphs in a precise way. However, using SPARQL requires knowledge that many casual users lack. To counter this, specific tools have been created that enable more casual users to browse and query results. This paper evaluates and compares the most prominent techniques, QueryVOWL, SPARKLIS and the Wikidata Query Service (WQS), through a usability evaluation, using a mixed-method evaluation based on usability metrics and heuristics, containing both quantitative and qualitative data. The findings show that while WQS achieved the best results, usability problems were encountered in all tools. Key aspects for usability, extracted from the evaluation, serve as important contributions for future query builders

SPARQL Playground: A block programming tool to experiment with SPARQL

SPARQL is a powerful query language for SemanticWeb data sources but one which is quite complex to master. As the block programming paradigm has been succesfully used to teach programming skills, we propose a tool that allows users to build and run SPARQL queries on an endpoint without previous knowledge of the syntax of SPARQL and the model of the data in the endpoint (vocabularies and semantics). This user interface attempts to close the gap between tools for the lay user that do not allow to express complex queries and overtly complex technical tools

Visuelle Suchanfragen auf graphbasierten Datenstrukturen

Die Menge an verfügbaren Daten nimmt stetig zu. Durch standardisierte Datenformate wird die Verknüpfung verschiedener Datenquellen und dadurch auch die Zusammenführung unterschiedlicher Datenelemente je nach Anwendungszweck ermöglicht. Dies führt wiederum zu noch umfassenderen Datenbeständen, in denen die eigentlich gewünschten Informationen teilweise nur schwer gefunden werden können. Handelt es sich bei den Daten um unstrukturierte oder gleichförmige Informationen, so beschränken sich Suchmöglichkeiten auf die Suche nach Übereinstimmungen von Mustern mit Datenelementen oder Teilen davon - beispielsweise Zeichenketten oder regulären Ausdrücken, die mit Teilen von textuellen Datenelementen übereinstimmen. In zunehmendem Maß stehen jedoch auch strukturierte Daten zur Verfügung. Bei diesen wird entweder von Anfang an zwischen unterschiedlichen Facetten pro Datenelement unterschieden, oder es wurden ursprünglich unstrukturierte Daten entsprechend angereichert. Da die einzelnen Facetten auch Verknüpfungen zu anderen Datenelementen darstellen können, entstehen hierbei Graphstrukturen, welche sich für Ansätze der facettierten Suche eignen. Eine Interoperabilität zwischen Datenquellen wird hier unter anderem über die Konzepte und Techniken des Semantic Web erreicht. Zahlreiche Arbeiten haben sich mit der Darstellung der gesamten Datenmengen als Übersicht oder von festgelegten Ausschnitten der Datenmengen im Detail auseinandergesetzt. Jedoch ist das Auffinden bestimmter Daten nach wie vor ein Problem. Die Schwierigkeit liegt dabei darin, die Suchkriterien präzise auszudrücken. Da sich zwischen den einzelnen Kriterien komplexe Zusammenhänge ergeben können, bietet sich auch hier genau wie bei der Übersicht der Datenmengen eine visuelle Darstellung an. Eine Besonderheit dieses Einsatzszenarios für Visualisierungen besteht darin, dass nicht zwangsläufig Daten vorliegen. Statt dessen muss die Visualisierung auch ohne verfügbare Daten die konzeptuelle Idee einer Suchanfrage ausdrücken. Frühere Arbeiten zu diesem Problem befassen sich mit der visuellen Repräsentation von Suchanfragen und Filterausdrücken in Bezug auf relationale Datenbanken und Objektdatenbanken. Viele neuere Arbeiten gehen vermehrt auch auf den Kontext des Semantic Webs ein. Einige dieser Konzepte sind jedoch nicht auf abstrakte Weise klar definiert. Bei komplexeren Anfragen treten zum Teil auch Skalierungsprobleme auf. Zudem wurde bisher kaum betrachtet, wie sich unterschiedliche Konzepte miteinander in Verbindung bringen lassen, um die Vorteile aus unterschiedlichen Anfragevisualisierungen nutzen zu können. Diese Dissertation adressiert die beschriebenen Probleme und stellt sechs Konzepte für die visuelle Darstellung von Suchanfragen vor. Es wird sowohl auf Visualisierungen für allgemeine Einsatzzwecke - also für die Filterung beliebiger strukturierter Informationen -, als auch für spezielle Domänen oder Arten von Informationen eingegangen. Bestehende Ansätze wurden teilweise auf die Gegebenheiten graphbasierter Datenstrukturen angepasst. Ebenso werden neue Ansätze präsentiert, die gezielt auf diese Art von Datenstrukturen ausgelegt sind. Dazu wird jeweils erörtert, inwiefern sich die Anfragevisualisierungen auch ohne Vorhandensein einer zu filternden Datensammlung einsetzen lassen. Zudem wird erklärt, wie bei Vorhandensein einer solchen eine Vorschau auf die Ergebnisse des Filtervorgangs gewährt werden kann. Abschließend werden Verbindungsmöglichkeiten der unterschiedlichen Visualisierungskonzepte präsentiert. Dieser Verbindungsansatz eignet sich dazu, beliebige Anfragevisualisierungen systematisch miteinander zu kombinieren. Mit dem Verbindungskonzept können Benutzer verschiedene Bestandteile einer Anfrage mittels unterschiedlicher Visualisierungskonzepte ausdrücken, um gleichzeitig von den Stärken unterschiedlicher Anfragevisualisierungen zu profitieren. Auf diese Weise können nun Anfragen visuell definiert und dargestellt werden, die sowohl komplexe Bedingungen als auch komplexe Zusammenhänge zwischen den Bedingungen aufweisen, ohne die visuelle Übersicht über einen dieser Aspekte zu verlieren.The total amount of available data is steadily increasing. Standardized data formats allow for connecting different data sources, which can include merging of different data items depending on the use case. This creates even more comprehensive datasets that render finding a particular piece of information difficult. If the data consist of unstructured of homogenous information, searching can only be done by matching patterns with data items or parts thereof - for instance, character strings or regular expressions that match parts of textual data items. However, the availability of structured data is increasing. This kind of data is either stored as distinct facets of each data item from the outset, or originally unstructured data has been enriched to form a structure. As each facet can indicate a link to another data item, the entire dataset forms a graph that is suitable for faceted search conepts. At this point, some interoperability across data sources can be achieved by employing Semantic Web approaches and techniques. Numerous works have attempted to visualize an overview of the entire dataset, or details of a particular excerpt of the dataset. Finding specific data remains a problem, however, as the precise specification of search criteria is difficult. As these criteria can be connected in complex ways, just like the overview of datasets, this issue lends itself to using visual representations. A special trait of this application of visualization is the possible absence of any data. Instead, the visualization must be capable of conveying the conceptual idea of a search query without displaying any data. Former works related to this problem focused on the visual representation of search queries and filter expressions for relational and object-oriented databases. More recent works increasingly address a Semantic Web context. Various of these concepts, however, lack a clear abstract definition. Also, scalability issues appear in the case of complex queries. Furthermore, little attention was paid to how to connect several concepts in order to combine advantages of different query visualizations. This dissertation considers the described problems and presents six concepts for query visualization. Both generic visualizations - that is, for filtering any kind of structured data - and domain-specific or type-specific visualizations are addressed. In part, existing approaches have been adapted to the particularities of graph-based data structures. Likewise, several new approaches specifically designed for this kind of data are presented. For each of these concepts, the necessity of a dataset is discussed. Moreover, options for providing a preview on query results from such a dataset, if available, are considered. Finally, ways for connecting the query visualization concepts are presented. This connection approach is suitable for systematically linking together arbitrary query visualizations. By means of the connection approach, users can express different parts of a query using different visualization concepts, in order to benefit from the advantages of several query visualizations at a time. Like this, queries that include complex criteria as well as complex relations between criteria can now be defined and displayed visually without losing the visual overview of any of these aspects

Specification and implementation of mapping rule visualization and editing : MapVOWL and the RMLEditor

Visual tools are implemented to help users in defining how to generate Linked Data from raw data. This is possible thanks to mapping languages which enable detaching mapping rules from the implementation that executes them. However, no thorough research has been conducted so far on how to visualize such mapping rules, especially if they become large and require considering multiple heterogeneous raw data sources and transformed data values. In the past, we proposed the RMLEditor, a visual graph-based user interface, which allows users to easily create mapping rules for generating Linked Data from raw data. In this paper, we build on top of our existing work: we (i) specify a visual notation for graph visualizations used to represent mapping rules, (ii) introduce an approach for manipulating rules when large visualizations emerge, and (iii) propose an approach to uniformly visualize data fraction of raw data sources combined with an interactive interface for uniform data fraction transformations. We perform two additional comparative user studies. The first one compares the use of the visual notation to present mapping rules to the use of a mapping language directly, which reveals that the visual notation is preferred. The second one compares the use of the graph-based RMLEditor for creating mapping rules to the form-based RMLx Visual Editor, which reveals that graph-based visualizations are preferred to create mapping rules through the use of our proposed visual notation and uniform representation of heterogeneous data sources and data values. (C) 2018 Elsevier B.V. All rights reserved

OptiqueVQS: A visual query system over ontologies for industry

An important application of semantic technologies in industry has been the formalisation of information models using OWL 2 ontologies and the use of RDF for storing and exchanging application data. Moreover, legacy data can be virtualised as RDF using ontologies following the ontology-based data access (OBDA) approach. In all these applications, it is important to provide domain experts with query formulation tools for expressing their information needs in terms of queries over ontologies. In this work, we present such a tool, OptiqueVQS, which is designed based on our experience with OBDA applications in Statoil and Siemens and on best HCI practices for interdisciplinary engineering environments. OptiqueVQS implements a number of unique techniques distinguishing it from analogous query formulation systems. In particular, it exploits ontology projection techniques to enable graph-based navigation over an ontology during query construction. Secondly, while OptiqueVQS is primarily ontology driven, it exploits sampled data to enhance selection of data values for some data attributes. Finally, OptiqueVQS is built on well-grounded requirements, design rationale, and quality attributes. We evaluated OptiqueVQS with both domain experts and casual users and qualitatively compared our system against prominent visual systems for ontology-driven query formulation and exploration of semantic data. OptiqueVQS is available online and can be downloaded together with an example OBDA scenario

Querying industrial stream-temporal data: An ontology-based visual approach

An increasing number of sensors are being deployed in business-critical environments, systems, and equipment; and stream a vast amount of data. The operational efficiency and effectiveness of business processes rely on domain experts’ agility in interpreting data into actionable business information. A domain expert has extensive domain knowledge but not necessarily skills and knowledge on databases and formal query languages. Therefore, centralised approaches are often preferred. These require IT experts to translate the information needs of domain experts into extract-transform-load (ETL) processes in order to extract and integrate data and then let domain experts apply predefined analytics. Since such a workflow is too time intensive, heavy-weight and inflexible given the high volume and velocity of data, domain experts need to extract and analyse the data of interest directly. Ontologies, i.e., semantically rich conceptual domain models, present an intelligible solution by describing the domain of interest on a higher level of abstraction closer to the reality. Moreover, recent ontology-based data access (OBDA) technologies enable end users to formulate their information needs into queries using a set of terms defined in an ontology. Ontological queries could then be translated into SQL or some other database query languages, and executed over the data in its original place and format automatically. To this end, this article reports an ontology-based visual query system (VQS), namely OptiqueVQS, how it is extended for a stream-temporal query language called STARQL, a user experiment with the domain experts at Siemens AG, and STARQL’s query answering performance over a proof of concept implementation for PostgreSQL

Semantic Systems. The Power of AI and Knowledge Graphs

This open access book constitutes the refereed proceedings of the 15th International Conference on Semantic Systems, SEMANTiCS 2019, held in Karlsruhe, Germany, in September 2019. The 20 full papers and 8 short papers presented in this volume were carefully reviewed and selected from 88 submissions. They cover topics such as: web semantics and linked (open) data; machine learning and deep learning techniques; semantic information management and knowledge integration; terminology, thesaurus and ontology management; data mining and knowledge discovery; semantics in blockchain and distributed ledger technologies

Promocijas darbs

Elektroniskā versija nesatur pielikumusDarbā izstrādātas oriģinālas metodes, kas ļauj vizuālus uz paplašinātām UML veida grafu diagrammām balstītus rīkus izmantot praktisku ontoloģiju un semantisko datu vaicājumu veidošanai un attēlošanai. OWL ontoloģiju vizuālas modelēšanas jomā izveidoti līdzekļi konkrētam lietojumam specifiskas notācijas uzdošanai un izmantošanai, tādi ka: mehānisms lietotāja definētu notāciju uzdošanai, ontoloģiju vizualizācijas parametru ietvars, ontoloģiju eksporta modulis un uz gramatikām balstīta priekšāteikšanas metode. Darbā piedāvāts risinājums vizuālai bagātīgu datu vaicājumu veidošanai pār RDF datubāzēm, un to translēšanai uz tekstuālu SPARQL valodu, kurā pierakstītie vaicājumi var tikt tieši izpildīti pār RDF datu bāzēm. Atslēgvārdi: OWL, OWLGrEd, teksta priekšāteicējs, domēnspecifiska ontoloģiju attēlošana, SPARQL, vizuāli vaicājumi, ViziQuerThe doctoral thesis develops original methods that allow visual tools that are based on extended UML-style graph diagrams to be used for creating and visualising practical ontologies and semantic data queries. In the field of visual modeling of OWL ontologies, tools have been developed for creating modeling notations specific to particular applications, such as a mechanism for creating user-defined notations, a framework for ontology visualisation parameters, an ontology export module and a grammar-based auto-completion method. The doctoral thesis presents a solution for the visual formulation of rich data queries over RDF databases, and their translation into the standard textual SPARQL query language. Keywords: OWL, OWLGrEd, text auto-completion, Domain-Specific Ontology Representation, SPARQL, Visual Queries, ViziQuer

Ontop: answering SPARQL queries over relational databases

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