Mini-ME Matchmaker and Reasoner for the Semantic Web of Things

Mini-ME Matchmaker and Reasoner for the Semantic Web of Things

Floriano Scioscia (Polytechnic University of Bari, Italy), Michele Ruta (Polytechnic University of Bari, Italy), Giuseppe Loseto (Polytechnic University of Bari, Italy), Filippo Gramegna (Polytechnic University of Bari, Italy), Saverio Ieva (Polytechnic University of Bari, Italy), Agnese Pinto (Polytechnic University of Bari, Italy) and Eugenio Di Sciascio (Polytechnic University of Bari, Italy)
DOI: 10.4018/978-1-5225-5042-6.ch010
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The Semantic Web of Things (SWoT) aims to support smart semantics-enabled applications and services in pervasive contexts. Due to architectural and performance issues, most Semantic Web reasoners are often impractical to be ported: they are resource consuming and are basically designed for standard inference tasks on large ontologies. On the contrary, SWoT use cases generally require quick decision support through semantic matchmaking in resource-constrained environments. This paper describes Mini-ME (the Mini Matchmaking Engine), a mobile inference engine designed from the ground up for the SWoT. It supports Semantic Web technologies and implements both standard (subsumption, satisfiability, classification) and non-standard (abduction, contraction, covering, bonus, difference) inference services for moderately expressive knowledge bases. In addition to an architectural and functional description, usage scenarios and experimental performance evaluation are presented on PC (against other popular Semantic Web reasoners), smartphone and embedded single-board computer testbeds.
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Semantic Web technologies have been acknowledged to promote interoperability and intelligent information processing in ubiquitous computing. Scenarios include supply chain management (Giannakis & Louis, 2016), ubiquitous commerce (Liu, 2013; De Virgilio, Di Sciascio, Ruta, Scioscia, & Torlone 2011), peer-to-peer resource discovery (Ruta, Di Sciascio, & Scioscia, 2011; Ruta, Scioscia, Ieva, Capurso & Di Sciascio, 2017) and so on. The ever-increasing computational resources and communications effectiveness of mobile devices enable ubiquitous processing and exchange of rich and structured information for context-aware resource discovery and decision support. The Semantic Web and the Internet of Things paradigms are converging more and more toward the so-called Semantic Web of Things (SWoT) (Ruta, Scioscia & Di Sciascio, 2012; Pfisterer et al., 2011). It enables semantic-enhanced pervasive computing by embedding intelligence into ordinary objects and environments through a plethora of heterogeneous micro-devices conveying short information seeds.

Such a vision requires increased flexibility and autonomy of ubiquitous knowledge-based systems in information encoding, management, dissemination and discovery. User agents running on mobile personal devices should be able to discover dynamically the best available resources according to user’s profile and preferences, in order to support her current tasks through unobtrusive and context-dependent suggestions. Reasoning and query answering are particularly critical issues, stimulating the need for further specialized inference services in addition to classical ones (like subsumption and satisfiability check). Furthermore, mobile computing platforms (e.g., smartphones, tablets) are still constrained by hardware/software limitations with respect to typical setups for Semantic Web reasoning engines. In fact, architectural and performance issues affect the porting of current OWL-based reasoners, designed for the Semantic Web, to mobile devices (Bobed, Yus, Bobillo, & Mena, 2015).

This chapter describes the Mini Matchmaking Engine (Mini-ME) (Scioscia et al., 2014b), a compact matchmaker and reasoner for the attributed language with unqualified number restrictions (ALN) Description Logic (DL). It is aimed to semantic matchmaking for resource/service discovery in mobile and ubiquitous contexts, although it is also a general-purpose Semantic Web inference engine. Optimized non-standard inference services allow a fine-grained categorization and ranking of matching resources w.r.t. a request, providing both a distance metric and a logic-based explanation of the outcomes. Mini-ME is suitable to a widespread class of applications where large sets of low-complexity component resources can be aggregated to build composed services with growing semantic complexity. This is fit for the computational and power supply limitations of resource providers in ubiquitous contexts and to their short storage availability. An “agile” service discovery architectures able to select, assemble and orchestrate on the fly many elementary components is more manageable and effective in mobile and pervasive applications.

Mini-ME uses the OWL API (Horridge & Bechhofer, 2011) to parse and manipulate Knowledge Bases in all supported syntaxes of Web Ontology Language (OWL) version 2 (World Wide Web Consortium [W3C], 2012). It exploits structural inference algorithms on unfolded and CNF (Conjunctive Normal Form) normalized concept expressions for efficient computations also on resource-constrained platforms. Mini-ME implements both standard reasoning tasks for Knowledge Base (KB) management (subsumption, satisfiability, classification) and non-standard inference services for semantic-based resource discovery and ranking (abduction, contraction, covering, bonus, difference). The reasoner is developed in Java, with Android as the main target computing platform. Furthermore, reasoner and graphical user interface (GUI) plug-ins have been developed for the Protégé ontology editor (Musen, 2015). Mini-ME has already been employed in prototypical testbeds on mobile and embedded devices for ubiquitous and pervasive computing scenarios.

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