A Mobile Matchmaker for the Ubiquitous Semantic Web

Abstract

The Semantic Web and Internet of Things visions are converging toward the so-called Semantic Web of Things (SWoT). It aims to enable smart semantic-enabled applications and services in ubiquitous contexts. Due to architectural and performance issues, it is currently impractical to use existing Semantic Web reasoners. They are resource consuming and are basically optimized 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 presents Mini-ME, a novel 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) inference services for moderately expressive knowledge bases. In addition to an architectural and functional description, usage scenarios are presented and an experimental performance evaluation is provided both on a PC testbed (against other popular Semantic Web reasoners) and on a smartphone.

An Argumentation-Based Perspective Over the Social IoT

“…This is a challenging task which partly requires low latency MOM and partly extremely efficient reasoning engines. Besides the aforementioned Akka framework, Apache ActiveMq Real Time [37] is a fork of Apache flagship IoT-focussed MOM explicitly aimed at real-time messaging, while MiniMe [38] is an extremely lightweight reasoning engine conceived especially for Semantic Web of Things (SWoT) applications. With the latter, for instance, carrying out simple reasoning tasks supporting argumentation, such as subsumption, entailment, abduction, and satisfiability, is particularly efficient, thus feasible on resource-constrained devices such as smartphones, RasPi, and directly on-chip, as already mentioned.…”

Social Internet of Things for domotics: A knowledge-based approach over LDP-CoAP

“…As stated before, this collaborative process leverages semantic matchmaking, i.e., the overall process allowing the retrieval and ranking of the most relevant resources for a given request, where both resources and requests are satisfiable concept expressions w.r.t. a common ontology T in a DL L. This paper refers to the OWL 2 subset corresponding to the ALN (Attributive Language with unqualified Number restrictions) Description Logics, as it is supported by an embedded matchmaking and reasoning engine which provides the required inference services [11]. Before applying any inference service, the loaded knowledge base is preprocessed performing unfolding and Conjunctive Normal Form (CNF) normalization, as detailed in [10].…”

“…Unfolding expands terminological axioms in concept expressions, allowing the reasoner to disregard the ontology in subsequent inference procedures. Normalization enables structural algorithms for both standard and non-standard reasoning tasks, with polynomial complexity for acyclic Terminological Boxes (TBoxes) under practical assumptions [11].…”

“…Resource discovery exploits semantic matchmaking between ontologybased annotations which describe requests and available resources. Non-standard, non-monotonic inferences [10] implemented in the Mini-ME mobile matchmaker and reasoner [11] allow supporting approximated matches, resource ranking and aggregation for covering complex requests. The framework also supports basic and legacy devices, which do not have computational power enough for on-board reasoning, by allowing them to select a more capable friend as inference facilitator.…”

IT and Optical Network Orchestration Framework: An Industrial Research Project

“…some initial preconditions P0 -from now on RF(P0)-it is possible to define a composite resource as complex element including elementary sources properly orchestrated among them by taking into account the IOPE paradigm. Non-standard reasoning services in [12] can be adopted for an automated compliance verification in quasi real-time. An executable resource r for RF (P0) is a resource which can be invoked after the execution of RF (P0), i.e., its preconditions are satisfied after the execution of RF(P0), and such that its effects are not already provided by RF (P0).…”