Ontology Alignment Architecture for Semantic Sensor Web Integration

Abstract

Sensor networks are a concept that has become very popular in data acquisition and processing for multiple applications in different fields such as industrial, medicine, home automation, environmental detection, etc. Today, with the proliferation of small communication devices with sensors that collect environmental data, semantic Web technologies are becoming closely related with sensor networks. The linking of elements from Semantic Web technologies with sensor networks has been called Semantic Sensor Web and has among its main features the use of ontologies. One of the key challenges of using ontologies in sensor networks is to provide mechanisms to integrate and exchange knowledge from heterogeneous sources (that is, dealing with semantic heterogeneity). Ontology alignment is the process of bringing ontologies into mutual agreement by the automatic discovery of mappings between related concepts. This paper presents a system for ontology alignment in the Semantic Sensor Web which uses fuzzy logic techniques to combine similarity measures between entities of different ontologies. The proposed approach focuses on two key elements: the terminological similarity, which takes into account the linguistic and semantic information of the context of the entity's names, and the structural similarity, based on both the internal and relational structure of the concepts. This work has been validated using sensor network ontologies and the Ontology Alignment Evaluation Initiative (OAEI) tests. The results show that the proposed techniques outperform previous approaches in terms of precision and recall.

OntoPolis©: A Semantic Participatory Platform for Performance Assessment and Augmentation of Urban Environments

“…These technologies assure the interconnection between concepts of the same or different domains, by discovering correspondences amongst related entities, in a process called ontology alignment [14]. Moreover, they enhance existing diverse source data with spatial, temporal and thematic (STT) semantic metadata to further enrich their meaning.…”

Optimizing Sensor Ontology Alignment through Compact co-Firefly Algorithm

“…First, we remove those correspondences with similarity value lower than 0.88 from the obtained alignment to ensure the precision of the final alignment. Here, we utilize the threshold 0.88 by referring to Fernandez et al [38]. Then, we sort the resting correspondences by descending similarity, and select them one by one into the final alignment as long as they do not conflict with previous selected ones.…”

“…In the experiments, the OAEI's Conference track with ra1 version [40] and three pairs of real sensor ontologies are used to test CcFA's performance. We compare CcFA with two SIA-based ontology matching techniques, that is, EA-based matcher [41], PSO-based matcher [42] and four state-of-the-art sensor ontology matching systems, that is, SOBOM [43], CODI [44], ASMOV [45] and FuzzyAlign [38], whose code and configuration parameters are available online. SOBOM works based on the syntax and structure based similarity measures, and it can obtain better results when the literal of concept and ontology hierarchy structure is complete.…”

The observation capability reason of optical satellite sensor for soil moisture monitoring

“…The accuracy of soil moisture depends on these sensors' observation capability. Different methods have been developed in order to assure the interoperability among reason of sensor observation [1][2][3]. They have simple inference rules to provide obvious results for users, which have not clear connection between the sensors and specific application.…”

“…Reasoing based on ontology methodology: In 2013, Fernandez et al [3] propose an ontology alignment architecture for Semantic Sensor Web which uses fuzzy logic techniques to combine similarity measures between entities of different ontologies.…”