Agnese Pinto

A Nonmonotonic Approach to Semantic Matchmaking and Request Refinement in E-Marketplaces

Matchmaking in e-marketplaces consists of finding and retrieving promising counterparts for a given request from the set of available advertisements. This paper proposes the use of nonmonotonic inferences (concept contraction and concept abduction) in a semantic-matchmaking process for ranking resource descriptions. Concept contraction can be used to amend requests incompatible with the resource descriptions. The more amendments needed, the less is the degree of match. If a request is compatible with an advertisement but does not subsume it, concept abduction can be used to hypothesize extra features in the advertisement. The more it is necessary to hypothesize, the less is the degree of match. These basic ideas are utilized to compute a meaningful matchmaking ranking. Using logical explanations on matchmaking results, an approach and algorithms are proposed for the progressive refinement and revision of requests, up to an almost exact match. The related issue of user interaction is also tackled, and a user-friendly tool is presented that allows full utilization of the semantic-based query/revision/refinement process while completely hiding logical technicalities.

A Mobile Matchmaker for the Ubiquitous Semantic Web

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.

Resource Annotation, Dissemination and Discovery in the Semantic Web of Things: A CoAP-Based Framework

The Semantic Web of Things (SWoT) vision aims to provide more advanced resource management and discovery w.r.t. standard Internet of Things architectures, by means of the integration of knowledge representation and reasoning techniques originally devised for the Semantic Web. This paper proposes a novel SWoT framework, based on a backward-compatible extension of the Constrained Application Protocol (CoAP), supporting non-standard inference services for semantic matchmaking. It allows retrieval and logic-based ranking of annotated resources. A computationally efficient data mining is also integrated in the framework to process raw data gathered from the environment in order to detect high-level events and characterize them with machine-understandable metadata. In order to test the effectiveness of the proposed approach, a case study about environmental risk prevention for Vehicular Ad-hoc Networks (VANETs) is presented.

Ontology-based natural language parser for E-marketplaces

We propose an approach to Natural Language Processing exploiting knowledge domain in an e-commerce scenario. Based on such modeling an NLP parser is presented, aimed at translating demand/supply advertisements into structured Description Logic expressions, automatically mapping sentences with concept expressions related to a reference ontology.

Semantic-enhanced resource discovery for CoAP-based sensor networks

The integration of knowledge representation and reasoning techniques (originally devised for the Semantic Web) in most common Wireless Sensor Networks (WSNs) protocols can allow to reach higher levels of autonomicity w.r.t. classic network architectures that basically provide only simplistic discovery capabilities. This paper presents a complete Semantic Sensor Network (SSN) framework, supporting a resource discovery based on non-standard inferences. A backward-compatible extension of Constrained Application Protocol (CoAP) has been proposed to support semantic matchmaking for retrieving and ranking resources annotated w.r.t. a reference ontology. Data mining procedures were also exploited to detect high-level events from gathered raw data. A case study on environmental monitoring has been proposed to test the effectiveness of our approach.

A semantic-based approach for Machine Learning data analysis

Pervasive applications and services are increasingly based on the intelligent interpretation of data gathered via heterogeneous sensors dipped in the environment. Classical Machine Learning (ML) techniques often do not go beyond a basic classification, lacking a meaningful representation of the detected events. This paper introduces a early proposal for a semantic-enhanced machine learning analysis on data of sensors streams, performing better even on resource-constrained pervasive smart objects. The framework merges an ontology-driven characterization of statistical data distributions with non-standard matchmaking services, enabling a fine-grained event detection by treating the typical classification problem of ML as a resource discovery.

Knowledge discovery and sharing in the IoT: the physical semantic web vision

The Physical Semantic Web is proposed as an enhancement to the Google Physical Web approach for the Internet of Things. It allows associating beacons to semantic annotations (instead of trivial identifiers), in order to enable more powerful expressiveness in human-things and things-things interactions. This paper presents a general frame-work for the Physical Semantic Web based on machine-understandable descriptions of physical resources and novel logic-guided resource discovery capabilities. A possible application scenario and early experiments are outlined to prove benefits of the induced enhancements and the effectiveness of theoretical solutions.

Cooperative semantic sensor networks for pervasive computing contexts

The Semantic Web of Things (SWoT) merges the Internet of Things with knowledge representation and reasoning techniques borrowed from the Semantic Web, in order to improve resource management and discovery. This paper proposes a SWoT framework in Wireless Sensor Networks (WSNs) enabling cooperative discovery of sensors and actuators. A backward-compatible extension of the Constrained Application Protocol (CoAP) makes possible to use semantic matchmaking via non-standard reasoning to better characterize the resource discovery. The framework also integrates nimble data stream mining to detect and annotate high-level events through raw data gathered from the environment. A cooperative environmental monitoring case study in Hybrid Sensor and Vehicular Networks (HSVN) is presented together with experiments on a real testbed to assess feasibility and benefits of proposal.

A CoAP-based framework for collaborative sensing in the Semantic Web of Things

Abstract: This paper proposes a novel Semantic Web of Things framework, enabling collaborative discovery of sensors and actuators in pervasive contexts. It is based on a backward-compatible extension of the Constrained Application Protocol (CoAP), supporting advanced semantic matchmaking via non-standard inference services. The framework also integrates efficient data stream mining to analyze raw data gathered from the environment and detect high-level events, annotating them with machine-understandable metadata. A case study about cooperative environmental risk monitoring and prevention in Hybrid Sensor and Vehicular Networks is presented and experimental performance results on a real testbed are provided.

A journey from the Physical Web to the Physical Semantic Web

ThePhysical Semantic Web (PSW) is a novel paradigm built upon the Google Physical Web (PW) approach and devoted to improve the quality of interactions in the Web of Things. Beacons expose semantic annotations instead of basic identifiers, ıe\ machine-understandable descriptions of physical resources. This enables novel ontology-based object advertisement and discovery and --in turn-- advanced user-to-thing and autonomous thing-to-thing interactions. The demo shows the evolution from the PW to the PSW in a discovery scenario set in a winery, where bottles are equipped with Bluetooth Low Energy beacons and a customer can discover them using her smartphone. The final goal is to prove benefits of PSW over basic PW, including: rich semantic-based object annotation; dynamic annotations exploiting on-board sensors; enhanced discovery and ranking of nearby objects through semantic matchmaking; availability of interactions even without working Internet infrastructure, by means of point-to-point data exchanges.