Rule-Based OWL Reasoning for Specific Embedded Devices

Abstract

Abstract. Ontologies have been used for formal representation of knowledge for many years now. One possible knowledge representation language for ontologies is the OWL 2 Web Ontology Language, informally OWL 2. The OWL specification includes the definition of variants of OWL, with different levels of expressiveness. OWL DL and OWL Lite are based on Description Logics, for which sound and complete reasoners exits. Unfortunately, all these reasoners are too complex for embedded systems. But since evaluation of ontologies on these resource constrained devices becomes more and more necessary (e.g. for diagnostics) we developed an OWL reasoner for embedded devices. We use the OWL 2 sub language OWL 2 RL, which can be implemented using rule-based reasoning engines. In this paper we present our used embedded hardware, the implemented reasoning component, and results regarding performance and memory consumption.

Simulation of mobile robot navigation utilizing reinforcement and unsupervised weightless neural network learning algorithm

“…Embedded autonomous system face resource-constrained issues [12], [13]; processor speed, storage capacity, run-time memory and other hardware related matters; 4) Autonomous system constitutes of finite states/situations and actions; and expert knowledge is translated into computer program used to activate these actions in order to manipulate the environment and can be classified as followings: a) A system whereby anticipated states are known beforehand, therefore can be generalized by using pre-trained Neural Networks [14]- [17] and expert knowledge was preprogrammed to handle number of actions; b) Or, rather than using pre-programmed expert knowledge, a reinforcement learning algorithm can be applied in order to make the system learn as time progresses [2], [18], [19]. 5) Autonomous system applications developed by Bagnall, Claveau, Nurmaini, Strauss and others [3], [14], [16], [20], [21] demonstrates that both reinforcement learning and weightless neural network algorithm can be successfully applied in autonomous systems which implemented in resource constraint environment;…”

An Efficient Bit Vector Approach to Semantics-Based Machine Perception in Resource-Constrained Devices

“…A recent W3C Member Submission [26] proposes a general-purpose RDF binary format for efficient representation, exchange, and query of semantic data; however, OWL inference is not supported. Several approaches to implementing OWL inference on resourceconstrained devices include [10,27,28,29]. Preuveneers et al [28] have presented a compact ontology encoding scheme using prime numbers that is capable of classsubsumption.…”

Resource-constrained reasoning using a reasoner composition approach

“…Existing semantic reasoners are too resourceintensive to be directly ported on resourceconstrained devices [3,34,38]. A study in [10] shows that a desktop rule-entailment reasoner can take from tens to several hundred KBs of memory (depending on characteristics of the ontology) to reason each RDF triple loaded into memory.…”