BOWL:augmenting the semantic web with beliefs

As the Semantic Web is an open, complex and constantly evolving medium, it is the norm, but not exception that information at different sites is incomplete or inconsistent. This poses challenges for the engineering and development of agent systems on the Semantic Web, since autonomous software agents need to understand, process and aggregate this information. Ontology language OWL provides core language constructs to semantically markup resources on the Semantic Web, on which software agents interact and cooperate to accomplish complex tasks. However, as OWL was designed on top of (a subset of) classic predicate logic, it lacks the ability to reason about inconsistent or incomplete information. Belief-augmented Frames (BAF) is a frame-based logic system that associates with each frame a supporting and a refuting belief value. In this paper, we propose a new ontology language Belief-augmented OWL (BOWL) by integrating OWL DL and BAF to incorporate the notion of confidence. BOWL is paraconsistent, hence it can perform useful reasoning services in the presence of inconsistencies and incompleteness. We define the abstract syntax and semantics of BOWL by extending those of OWL. We have proposed reasoning algorithms for various reasoning tasks in the BOWL framework and we have implemented the algorithms using the constraint logic programming framework. One example in the sensor fusion domain is presented to demonstrate the application of BOWL.

Truthiness and falsiness of trivia claims depend on judgmental contexts

When people rapidly judge the truth of claims presented with or without related but nonprobative photos, the photos tend to inflate the subjective truth of those claims-a "truthiness" effect (Newman et al., 2012). For example, people more often judged the claim "Macadamia nuts are in the same evolutionary family as peaches" to be true when the claim appeared with a photo of a bowl of macadamia nuts than when it appeared alone. We report several replications of that effect and 3 qualitatively new findings: (a) in a within-subjects design, when people judged claims paired with a mix of related, unrelated, or no photos, related photos produced truthiness but unrelated photos had no significant effect relative to no photos; (b) in a mixed design, when people judged claims paired with related (or unrelated) and no photos, related photos produced truthiness and unrelated photos produced "falseness;" and © in a fully between design, when people judged claims paired with either related, unrelated, or no photos, neither truthiness nor falsiness occurred. Our results suggest that photos influence people's judgments when a discrepancy arises in the expected ease of processing, and also support a mechanism in which-against a backdrop of an expected standard-related photos help people generate pseudoevidence to support claims. (PsycINFO Database Record)