Linked Open Vocabularies (LOV): A gateway to reusable semantic vocabularies on the Web

Abstract

Abstract. This article addresses a number of limitations of state-of-the-art methods of Ontology Alignment: 1) they primarily address concepts and entities while relations are less well-studied; 2) many build on the assumption of the 'well-formedness' of ontologies which is unnecessarily true in the domain of Linked Open Data; 3) few have looked at schema heterogeneity from a single source, which is also a common issue particularly in very large Linked Dataset created automatically from heterogeneous resources, or integrated from multiple datasets. We propose a domain-and language-independent and completely unsupervised method to align equivalent relations across schemata based on their shared instances. We introduce a novel similarity measure able to cope with unbalanced population of schema elements, an unsupervised technique to automatically decide similarity threshold to assert equivalence for a pair of relations, and an unsupervised clustering process to discover groups of equivalent relations across different schemata. Although the method is designed for aligning relations within a single dataset, it can also be adapted for cross-dataset alignment where sameAs links between datasets have been established. Using three gold standards created based on DBpedia, we obtain encouraging results from a thorough evaluation involving four baseline similarity measures and over 15 comparative models based on variants of the proposed method. The proposed method makes significant improvement over baseline models in terms of F1 measure (mostly between 7% and 40%), and it always scores the highest precision and is also among the top performers in terms of recall. We also make public the datasets used in this work, which we believe make the largest collection of gold standards for evaluating relation alignment in the LOD context.

Linked data schemata: Fixing unsound foundations

“…In order to do this we rely on the extensive literature that catalogs the development and makeup of the Web of Data and the live reports from the Linked Open Vocabularies (LOV) site 5 [33]. At the time of writing LOV reported hosting 542 vocabularies.…”

Teaching Methods of Semantic Web to Software Developers

“…Прочитать описание основных классов и свойств словаря FOAF (Friend Of A Friend) 4 , затем создать описание собственной персоны в формате RDF/XML при помощи приложения FOAF-a-matic 5 , после чего вручную добавить к сгенерированному файлу в формате RDF/XML пять синтаксически правильных триплетов, использующих в качестве предикатов различные предикаты, описанные в разных наборах данных облака LOD. Проверить синтаксическую правильность полученного файла при помощи RDF-валидатора 6 .…”

“…Большое количество словарей может быть обнаружено при помощи поискового движка Watson 7 . Около 600 различных словарей представлены в специализированном наборе данных Linked Open Vocabularies (LOV) [4]. Набор данных LOV строит экосистему словарей, поддерживающую их повторное использование.…”

Utilizing Multimedia Ontologies in Video Scene Interpretation via Information Fusion and Automated Reasoning

“…The Linked Open Vocabularies (LOV) 20 catalogue is maintained to help determine which vocabularies and ontologies to use for formal descriptions. Even though the list of rigorous criteria to meet before a vocabulary or ontology will be listed on LOV assures design quality [27], it does no guarantee that the best vocabulary will be selected for a particular scenario. For example, when searching for the term "video," the LOV website suggests OpenGraph in the first, the Library extension of Schema.org in the second, and the NEPOMUK File Ontology in the third place.…”