PatOMat - Versatile Framework for Pattern-Based Ontology Transformation

The purpose of the PatOMat transformation framework is to bridge between different modeling styles of web ontologies. We provide a formal model of pattern-based ontology transformation, explain its implementation in PatOMat, and manifest the flexibility of the framework on diverse use cases

Towards Building a Link Set Backed by Domain Experts using the Alignment Tool

Discovering semantic relations between entities (entity linking) is one of the most important activity for both semantic web and linked data areas. Either we need link sets of instances or concepts we can rely on automatic systems only to a certain extent. As a result, an automatic linking is accompanied with a user interaction which enables to increase the quality of resulted link sets. Often, in order to reach as much quality of link set as possible the user should be a domain expert for an area of linking task. This user specifics should be considered by designers of interactive entity linking tools. This work presents an experience from an experiment of building a link set for two fiscal code lists where domain experts have been involved. The experiment has been done using the Alignment tool

SPARQL-DL queries for antipattern detection

Ontology antipatterns are structures that reflect ontology modelling problems, they lead to inconsistencies, bad reasoning performance or bad formalisation of domain knowledge. Antipatterns normally appear in ontologies developed by those who are not experts in ontology engineering. Based on our experience in ontology design, we have created a catalogue of such antipatterns in the past, and in this paper we describe how we can use SPARQL-DL to detect them. We conduct some experiments to detect them in a large OWL ontology corpus obtained from the Watson ontology search portal. Our results show that each antipattern needs a specialised detection method

Antipattern detection in web ontologies: an experiment using SPARQL queries

Ontology antipatterns are structures that reflect ontology modelling problems because they lead to inconsistencies, bad reasoning performance or bad formalisation of domain knowledge. We propose four methods for the detection of antipatterns using SPARQL queries.We conduct some experiments to detect antipattern in a corpus of OWL ontologies

SPARQL-based Detection of Antipatterns in OWL Ontologies

Ontology anti-patterns are structures that reflect ontology modeling problems because they lead to inconsistencies or to bad reasoning performance. Based on a collection of anti-patterns coming from our experience in ontology engineering projects and bad modeling practices found in the literature, we propose to represent them as SPARQL queries and conduct an experiment to detect them in an ontology corpus obtained from the Watson ontology search portal

Augmenting the Ontology Visualization Tool Recommender: Input Pre-Filling and Integration with the OOSP Ontological Benchmark Builder

ABSTRACT Ontology visualization is an important functionality for ontology users. Since many visualization methods have been proposed and implemented, it is not an easy task for an ontology user to select a proper ontology visualization tool based on the user's requirements. In order to recommend an ontology visualization tool, some requirements should be provided manually by the user. However, other requirements could be assessed automatically. This demo paper presents a partial automation of the pre-existing Ontology Visualization Tools Recommender input; the recommender knowledge base is also updated with three new ontology visualization tools. Further, we present an integration of the Ontology Visualization Tools Recommender into another web-based tool, Online Ontology Set Picker, where proper ontology visualization can assist in ontology benchmark construction

Focused categorization power of ontologies: General framework and study on simple existential concept expressions

When reusing existing ontologies for publishing a dataset in RDF (or developing a new ontology), preference may be given to those providing extensive subcategorization for important classes (denoted as focus classes). The subcategories may consist not only of named classes but also of compound class expressions. We define the notion of focused categorization power of a given ontology, with respect to a focus class and a concept expression language, as the (estimated) weighted count of the categories that can be built from the ontology’s signature, conform to the language, and are subsumed by the focus class. For the sake of tractable initial experiments we then formulate a restricted concept expression language based on existential restrictions, and heuristically map it to syntactic patterns over ontology axioms (so-called FCE patterns). The characteristics of the chosen concept expression language and associated FCE patterns are investigated using three different empirical sources derived from ontology collections: first, the concept expression pattern frequency in class definitions; second, the occurrence of FCE patterns in the Tbox of ontologies; and last, for class expressions generated from the Tbox of ontologies (through the FCE patterns); their ‘meaningfulness’ was assessed by different groups of users, yielding a ‘quality ordering’ of the concept expression patterns. The complementary analyses are then compared and summarized. To allow for further experimentation, a web-based prototype was also implemented, which covers the whole process of ontology reuse from keyword-based ontology search through the FCP computation to the selection of ontologies and their enrichment with new concepts built from compound expressions

MultiFarm: A benchmark for multilingual ontology matching

In this paper we present the MultiFarm dataset, which has been designed as a benchmark for multilingual ontology matching. The MultiFarm dataset is composed of a set of ontologies translated in different languages and the corresponding alignments between these ontologies. It is based on the OntoFarm dataset, which has been used successfully for several years in the Ontology Alignment Evaluation Initiative (OAEI). By translating the ontologies of the OntoFarm dataset into eight different languages – Chinese, Czech, Dutch, French, German, Portuguese, Russian, and Spanish – we created a comprehensive set of realistic test cases. Based on these test cases, it is possible to evaluate and compare the performance of matching approaches with a special focus on multilingualism

Results of the ontology alignment evaluation initiative 2020

The Ontology Alignment Evaluation Initiative (OAEI) aims at comparing ontology matching systems on precisely defined test cases. These test cases can be based on ontologies of different levels of complexity and use different evaluation modalities (e.g., blind evaluation, open evaluation, or consensus). The OAEI 2020 campaign offered 12 tracks with 36 test cases, and was attended by 19 participants. This paper is an overall presentation of that campaign