A Knowledge Engineering Primer

The aim of this primer is to introduce the subject of knowledge engineering in a concise but synthetic way to develop the reader's intuition about the area

Test-Driven Development of Ontologies

Emerging ontology authoring methods to add knowledge to an ontology focus on ameliorating the validation bottleneck. The verification of the newly added axiom is still one of trying and seeing what the reasoner says, because a systematic testbed for ontology authoring is missing. We sought to address this by introducing the approach of testdriven development for ontology authoring. We specify 36 generic tests, as TBox queries and TBox axioms tested through individuals, and structure their inner workings in an ‘open box’-way, which cover the OWL 2 DL language features. This is implemented as a Protege plugin so that one can perform a TDD test as a black box test. We evaluated the two test approaches on their performance. The TBox queries were faster, and that effect is more pronounced the larger the ontology is

The TDDonto Tool for Test-Driven Development of DL Knowledge bases

Adding knowledge to an ontology lacks a verification step by the modeller in most cases, other than `try and see what the reasoner says about it'. This is due to the lack of a systematic testbed for ontology authoring. Reusing the notion of {\em Test-Driven Development} (TDD) from software engineering for ontology development resulted in the specification of 42 test types for the $\mathcal{SROIQ}$ language features, as TBox tests using its axioms and as ABox-driven tests with explicitly introduced individuals. We developed TDDOnto, which implements that subset of the TDD tests that could be done by leveraging extant technologies. We examined what the most efficient implementation strategy is with 82 ontologies. The TBox SPARQL queries with OWL-BGP were faster than the ABox-based approach except for disjointness, that effect is more pronounced with larger ontologies, and the OWL API approach is faster than the SPARQL queries for OWL 1 ontologies. A significant difference in performance between OWL and OWL 2 DL ontologies was observed. On average, the TDD tests are faster than classification reasoning, indicating that TDD tests are a promising alternative to the `try and see' approach in ontology authoring

SciBERT-based semantification of bioassays in the open research knowledge graph

As a novel contribution to the problem of semantifying biological assays, in this paper, we propose a neural-network-based approach to automatically semantify, thereby structure, unstructured bioassay text descriptions. Experimental evaluations, to this end, show promise as the neural-based semantification significantly outperforms a naive frequencybased baseline approach. Specifically, the neural method attains 72% F1 versus 47% F1 from the frequency-based method. The work in this paper aligns with the present cutting-edge trend of the scholarly knowledge digitalization impetus which aim to convert the long-standing document-based format of scholarly content into knowledge graphs (KG). To this end, our selected data domain of bioassays are a prime candidate for structuring into KG

Exploring Reasoning with the DMOP Ontology

We describe the Data Mining OPtimization Ontology (DMOP), which was developed to support informed decision-making at various choice points of the knowledge discovery (KD) process. DMOP contains in-depth descriptions of DM tasks, data, algorithms, hypotheses, and workflows. Its development raised a number of non-trivial modeling problems, the solution to which demanded maximal exploitation of OWL 2 representational potential. The choices made led to v5.4 of the DMOP ontology. We report some evaluations on processing DMOP with a standard reasoner by considering different DMOP features

Categorize By: Deductive Aggregation of Semantic Web Query Results

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