53,888 research outputs found
Extended RDF as a Semantic Foundation of Rule Markup Languages
Ontologies and automated reasoning are the building blocks of the Semantic
Web initiative. Derivation rules can be included in an ontology to define
derived concepts, based on base concepts. For example, rules allow to define
the extension of a class or property, based on a complex relation between the
extensions of the same or other classes and properties. On the other hand, the
inclusion of negative information both in the form of negation-as-failure and
explicit negative information is also needed to enable various forms of
reasoning. In this paper, we extend RDF graphs with weak and strong negation,
as well as derivation rules. The ERDF stable model semantics of the extended
framework (Extended RDF) is defined, extending RDF(S) semantics. A distinctive
feature of our theory, which is based on Partial Logic, is that both truth and
falsity extensions of properties and classes are considered, allowing for truth
value gaps. Our framework supports both closed-world and open-world reasoning
through the explicit representation of the particular closed-world assumptions
and the ERDF ontological categories of total properties and total classes
Fireground location understanding by semantic linking of visual objects and building information models
This paper presents an outline for improved localization and situational awareness in fire emergency situations based on semantic technology and computer vision techniques. The novelty of our methodology lies in the semantic linking of video object recognition results from visual and thermal cameras with Building Information Models (BIM). The current limitations and possibilities of certain building information streams in the context of fire safety or fire incident management are addressed in this paper. Furthermore, our data management tools match higher-level semantic metadata descriptors of BIM and deep-learning based visual object recognition and classification networks. Based on these matches, estimations can be generated of camera, objects and event positions in the BIM model, transforming it from a static source of information into a rich, dynamic data provider. Previous work has already investigated the possibilities to link BIM and low-cost point sensors for fireground understanding, but these approaches did not take into account the benefits of video analysis and recent developments in semantics and feature learning research. Finally, the strengths of the proposed approach compared to the state-of-the-art is its (semi -)automatic workflow, generic and modular setup and multi-modal strategy, which allows to automatically create situational awareness, to improve localization and to facilitate the overall fire understanding
Invariant Synthesis for Incomplete Verification Engines
We propose a framework for synthesizing inductive invariants for incomplete
verification engines, which soundly reduce logical problems in undecidable
theories to decidable theories. Our framework is based on the counter-example
guided inductive synthesis principle (CEGIS) and allows verification engines to
communicate non-provability information to guide invariant synthesis. We show
precisely how the verification engine can compute such non-provability
information and how to build effective learning algorithms when invariants are
expressed as Boolean combinations of a fixed set of predicates. Moreover, we
evaluate our framework in two verification settings, one in which verification
engines need to handle quantified formulas and one in which verification
engines have to reason about heap properties expressed in an expressive but
undecidable separation logic. Our experiments show that our invariant synthesis
framework based on non-provability information can both effectively synthesize
inductive invariants and adequately strengthen contracts across a large suite
of programs
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