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Resource-Constrained Reasoning Using a Reasoner Composition Approach

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Resource-Constrained Reasoning Using a Reasoner Composition Approach Resource-Constrained Reasoning Using a Reasoner Composition Approach Editor(s): Name Surname, University, Country Solicited review(s): Name Surname, University, Country Open review(s): Name Surname, University, Country Wei Tai a, *, John Keeney b, Declan O’Sullivan a a Knowledge and Data Engineering Group, School of Computer Science and Statistics, Trinity College Dublin, Dublin 2, Ireland b Network Management Lab, LM Ericsson, Athlone, Ireland Abstract: The marriage between semantic web and sensor-rich systems can semantically link the data related to the physical world with the existent machined-readable domain knowledge encoded on the web. This has allowed a better understanding of the inherently heterogeneous sensor data by allowing data access and processing based on semantics. Research in different domains has been started seeking a better utilization of data in this manner. Such research often assumes that the semantic data is processed in a centralized server and that reliable network connections are always available, which are not realistic in some critical situations. For such critical situations, a more robust semantic system needs to have some local- autonomy and hence on-device semantic data processing is required. As a key enabling part for this strategy, semantic reasoning needs to be developed for resource-constrained environments. This paper shows how reasoner composition (i.e. to automatically adjust a reasoning approach to preserve only the amount of reasoning needed for the ontology to be reasoned over) can achieve resource-efficient semantic reasoning. Two novel reasoner composition algorithms have been designed and implemented. Evaluation indicates that the reasoner composition algorithms greatly reduce the resources required for OWL reasoning, facilitating greater semantic data processing on sensor devices. Keywords: Reasoner Composition, Reasoning, OWL, Rule, Mobile, Resource Constrained, Semantic Web 1. Introduction some critical situations such as emergency situations, this infrastructure is prone to interference or damage, The marriage between semantic web and sensor- causing part of the system with only limited network rich systems such as mobile devices and wireless connections to the central server or even totally sensor network can semantically link the data related detached from the rest of the system. In some other to the physical world with the vast amount of situations such as seafloor monitoring or forest fire machined-readable domain knowledge encoded on monitoring, the surrounding environment could be the web. This increases the interoperability and the too harsh (e.g. limited power supply and hostile value of the data, and hence allows applications to environment) to be suitable for either an onsite have a better understanding of the inherently powerful centralized server or a fast internet heterogeneous sensor data through data access and connection to services in the cloud. In other cases, processing on the semantic level. Quite a lot of the overhead involved in relaying raw un-processed research has been started seeking better utilization of data can be very high, and the centralized intelligent data in domains in this manner, such as hazard data processing overhead at the central processing monitoring and rescue [15], context-aware node acts as a bottleneck in the system. Reality often computing [27], environmental monitoring [4], field requires a robust system to be able to retain part (or studies [35], internet of things [18], and so on. all) of data processing over the networked devices, A centralized paradigm is often assumed in such e.g. sensor data can be aggregated on devices to research: all the data processing (such as semantic meaningful events to minimize network data annotation, semantic data fusion, semantic data transmission, or decisions/actuations are made aggregation, decision making, and so on) is handled locally on the device itself or on a cluster basis [36]. on a powerful centralized machine either as a local For such a robust semantic-enabled system, it makes gateway [15] or as remote services on the web [4, 35, sense to push semantic reasoning towards the edge 27], and hence the data transmission relies on of the system which are lower-specification devices reliable (and fast) network connections. However, used only for data gathering, actuation, data such assumptions do not always hold in reality. In aggregation or routing. In the extreme cases they concentrated on memory and time performance may be only sensors with very limited resources. measures. Results indicate that our reasoner Existing semantic reasoners are too resource- composition algorithms significantly reduce the intensive to be directly implemented on such resource consumption required for rule-entailment resource-constrained devices [3, 34, 38]. This is reasoning, thus facilitating semantic reasoning in particularly the case for rule-entailment reasoners. A resource-constrained environments. study in [10] shows the reasoning can take tens or The paper is organized as follows. Section 2 several hundred KB of memory per triple. Hence presents related work, including a categorization of reasoning over a small ontology can easily drain the existing OWL reasoning approaches and a resource on a very small device as those deployed discussion of the optimizations and composition on the edge of the systems (e.g. Sun SPOT has approaches used by existing resource-constrained 180MHz CPU, 512KB RAM, and 4MB Flash). reasoners. Section 3 elaborates the design of the two Distinct ontologies often vary in characteristics, composition algorithms. Implementation details for such as expressivity, the used language vocabularies, a prototype implementation (“COROR”) are structure and so on. The varieties of such provided in section 4. Section 5 follows with an characteristics can cause a difference in the evaluation of our work. Section 6 concludes with a requirement of reasoning. A simple example to discussion of the significance, limitations and show this difference would be that reasoning over a directions for future work of the research. RDFS ontology only needs RDFS rules whereas reasoning over full OWL 2 RL ontology requires the 2. Background and Related Work entire OWL 2 RL rule set. This difference shows a potential approach for cutting the resource This section discusses the background and related consumption for semantic reasoning. A reasoning work from three aspects. Firstly, a categorization of approach could automatically adjust itself based on OWL reasoning approaches is outlined and a the characteristics of the ontology to be reasoned rationale is provided for choosing forward-chaining over to provide “just enough” reasoning for the rule-entailment reasoning as a base technology for particular ontology. In this way unneeded reasoning our work (section 2.1 and 2.2). A second aspect is a can be avoided, therefore lowering the need for discussion of the optimizations or composition resources in semantic reasoning. This automatic approaches (if any) adopted by the existing self-adjustment of reasoning capabilities according resource-constrained OWL reasoners (section 2.3). to characteristics of ontologies is called reasoner Finally, a discussion of the main mechanism for composition [2]. implementing a rule engine – RETE – is presented, In our research, we concentrate on the along with some RETE optimizations from which composition of rule-entailment reasoning. This is for some composition algorithms are inspired (section two reasons. First, our motivating scenario of 2.4). resource-constrained reasoning requires domain- specific reasoning to be supported. Modeling and 2.1. A Categorization of OWL Reasoning deploying domain-specific rules is the most straight- Approaches forward approach. Second, the loose-coupling of rules shows better composability than the other Considering the quite different reasoning reasoning approaches [2]. approaches adopted in OWL reasoners, a Two novel composition algorithms were devised categorization of OWL reasoning approaches is in the course of our research to compose rule- important (1) to provide us with a grounding in entailment reasoning from two different OWL reasoning approaches, and (2) to enable the perspectives: a selective rule loading algorithm investigation of reasoner composition based on a composes on the rule set, and a two-phase RETE category of reasoning approaches rather than a algorithm composes in the reasoning algorithm. The particular reasoner implementation. A survey of the composition algorithms were implemented in a reasoning approaches was conducted among a group proof-of-concept resource-constrained OWL of 26 OWL reasoners from the state of the art. By reasoner (COROR). As the goal of this work was to examining the reasoning approaches used, a reduce resource consumption for embedded rule- categorization was developed such that the reasoners entailment reasoning, our evaluation strategy could be classified into five categories, i.e. Description Logic (DL) tableau reasoners, Resolution-based reasoners perform OWL (forward-chaining) rule-entailment reasoners, reasoning using resolution engines which could be a resolution-based reasoners, reasoners using hybrid Prolog engine [40, 41], a Datalog engine [16] or a approaches, and finally reasoners using full First Order Logic (FOL) theorem prover [25]. miscellaneous approaches. Depending on the type of resolution engine in use, a DL tableau reasoners convert OWL entailment resolution-based reasoner
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