Guntis Barzdins

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Two extensions to the AMR smatch scoring script are presented. The first extension combines the smatch scoring script with the C6.0 rule-based classifier to produce a human-readable report on the error patterns frequency observed in the scored AMR graphs. This first extension results in 4% gain over the state-of-art CAMR baseline parser by adding to it a(More)
There have been several attempts to visualize OWL ontologies with UML style diagrams. Unlike ODM approach of defining a UML profile for OWL, we propose an extension to UML class diagrams (hard extension) that allows a more compact OWL visualization. The compactness is achieved through the native power of UML class diagrams extended with optional Manchester(More)
The paper presents an ongoing research that aims at OWL ontology authoring and verbalization using a deterministic controlled natural language (CNL) that would be as natural and intuitive as possible. Moreover, we focus on a multilingual CNL interface to OWL by considering both highly analytical and highly synthetic languages (namely, English and Latvian).(More)
This chapter introduces the UML profile for OWL as an essential instrument for bridging the gap between the legacy relational databases and OWL ontologies. We address one of the long-standing relational database design problems where initial conceptual model (a semantically clear domain conceptualization ontology) gets “lost” during conversion into the(More)
In this paper we show how semantic web technologies are used in a real application in the domain of national medical databases where an important technological gap between the legacy relation databases and OWL ontologies is bridged by the recently standardized UML profile for OWL. After data has been exported from multiple relational databases into a single(More)
We present an original Protégé plugin developed for the deep consistency checking of OWL ontologies. The plugin constructs and visualizes a minimal satisfiability model of the ontology, which is likely to uncover potential ontological errors: if the constructed model contradicts the author’s intentions, then the ontology itself is either wrong or(More)
The OWLGrEd ontology editor allows graphical visualization and authoring of OWL 2.0 ontologies using a compact yet intuitive presentation that combines UML class diagram notation with textual Manchester syntax for expressions. We present an extension mechanism for OWLGrEd that allows adding custom information areas, rules and visual effects to the ontology(More)
By addressing both text-to-AMR parsing and AMR-to-text generation, SemEval2017 Task 9 established AMR as a powerful semantic interlingua. We strengthen the interlingual aspect of AMR by applying the multilingual Grammatical Framework (GF) for AMR-to-text generation. Our current rule-based GF approach completely covered only 12.3% of the test AMRs, therefore(More)