One challenge to creating realistic cognitive models of memory is the inability to account for the vast common–sense knowledge of human participants. Large computational knowledge bases such as WordNet and DBpedia may offer a solution to this problem but may pose other challenges. This paper explores some of these difficulties through a semantic network spreading activation model of the Deese–Roediger–McDermott false memory task. In three experiments, we show that these knowledge bases only capture a subset of human associations, while (...) irrelevant information introduces noise and makes efficient modeling difficult. We conclude that the contents of these knowledge bases must be augmented and, more important, that the algorithms must be refined and optimized, before large knowledge bases can be widely used for cognitive modeling. (shrink)

One challenge to creating realistic cognitive models of memory is the inability to account for the vast common–sense knowledge of human participants. Large computational knowledge bases such as WordNet and DBpedia may offer a solution to this problem but may pose other challenges. This paper explores some of these difficulties through a semantic network spreading activation model of the Deese–Roediger–McDermott false memory task. In three experiments, we show that these knowledge bases only capture a subset of human associations, while (...) irrelevant information introduces noise and makes efficient modeling difficult. We conclude that the contents of these knowledge bases must be augmented and, more important, that the algorithms must be refined and optimized, before large knowledge bases can be widely used for cognitive modeling. (shrink)

The research presented in this article is motivated by the increasing importance of complex human relations in linked data, either extracted from social networks, or found in existing databases. The FOAF vocabulary, targeted in our research, plays a central role in those data, and is a model for lightweight ontologies largely used in linked data, such as the DBpedia ontology and schema-org. We provide an overview of FOAF and other approaches for describing human relations, followed by a detailed analysis (...) and critique of the FOAF Relationship Vocabulary, the most important FOAF extension. We propose an explicit formal axiomatization of this vocabulary, and an ontological analysis concerning the properties used to describe human relationships. We analyze the distribution of human relations based on their epistemological status, and define an ontoepistemic meta-property as characteristic of some of these predicates. Our analysis is generalizable to semantic modeling of social networks. Additionally, the modeling patterns used in other relevant linked data vocabularies are analyzed for comparison. (shrink)