The proliferation of ontologies and multilingual data available on the Web has motivated many researchers to con- tribute to multilingual and cross-lingual ontology enrichment. Cross-lingual ontology enrichment greatly facilitates ontology learning from multilingual text/ontologies in order to support collaborative ontology engineering process. This article proposes a cross-lingual ontology enrichment (CLOE) approach based on a multi-agent architecture in order to enrich ontologies from a multilingual text or ontology.
This paper describes an OWL ontology that is a Universal Moral Grammar (UMG). UMG has been hypothesized by students of Chomsky to play the same role in human ethics as Universal Grammar (UG) does in Linguistics. I.e., the UMG describes an innate genetic phenotype of moral reasoning just as UG describes the Language Faculty. This approach utilizes the modular view of the mind developed by Chomsky and currently utilized by many evolutionary psychology researchers. In this paper I describe the ontology and how it represents ethical choices, rules, scenarios, systems, and biological models.
In today’s age of (Industrial) Internet of Things, large amounts of data are generated in public and industrial settings every second. For enabling data analytics and aggregation, many companies currently focus on the approach of data lakes. While this approach allows the centralized storage of all available kinds of data, it leads to challenges as the stored data has to be found, understood and processed. One solution for describing semantics of data sources is the use of semantic models based on an available vocabulary.
The schema.org initiative led by the four major search engines curates a vocabulary for describing web content. The number of semantic annotations on the web are increasing, mostly due to the industrial incentives provided by those search engines. The annotations are not only consumed by search engines, but also by other automated agents like intelligent personal assistants (IPAs). However, only annotating data is not enough for automated agents to reach their full potential.