Enabling Traceability in Agri-Food Supply Chains Using an Ontological Approach

Traceability of food products to their sources is critical for quick responses to a food emergency. US law now requires stakeholders in the agri-food supply chain to support traceability by tracking food materials they acquire and sell. However, having complete and consistent information needed to quickly investigate sources and identify affected material has proven difficult. There are multiple reasons that makes food traceability a challenging task including diversity of stakeholders and their lexicons, standards, tools and methods; unwillingness to expose information of internal operations; lack of a common understanding of steps in a supply chain; and incompleteness of data. Ontologies can address the traceability challenge by creating a shared understanding of the traceability model across stakeholders in a food supply chain. They can also support semantic mediation, data integration, and data exploration. This paper reports an on ongoing effort aimed at developing a formal ontology for supply chain traceability using use cases and data from partners in the bulk grain domain. The developed ontology was validated in VocBench environment through creating RDF triples from real datasets and executing SPARQL queries corresponding to predefined competency questions.

Semantic Health Mediation and Access Control Manager for Interoperability Among Healthcare Systems

Healthcare systems have evolved to become more patient-centric. Many efforts have been made to transform paper-based patient data to automated medical information by developing electronic healthcare records (EHRs). Several international EHRs standards have been enabling healthcare interoperability and communication among a wide variety of medical centres. It is a dual-model methodology which comprises a reference information model and an archetype model. The archetype is responsible for the definition of clinical concepts which has limitations in terms of supporting complex reasoning and knowledge discovery requirements. The objective of this article is to propose a semantic-mediation architecture to support semantic interoperability among healthcare organizations. It provides an intermediate semantic layer to exploit clinical information based on richer ontological representations to create a “model of meaning” for enabling semantic mediation. The proposed model also provides secure mechanisms to allow interoperable sharing of patient data between healthcare organizations.

Semantic Mediation Model to Promote Improved Data Sharing Using Representation Learning in Heterogeneous Healthcare Service Environments

Interoperability has become a major challenge for the development of integrated healthcare applications. This is mainly because of the reason that data is collected, processed, and managed using heterogeneous protocols, different data formats, and diverse technologies, respectively. Moreover, interoperability among healthcare applications has been limited because of the lack of mutually agreed standards. This article proposes a semantic mediation model for the interoperability provision in heterogeneous healthcare service environments. To enhance semantic mediation, the Web of Objects (WoO) framework has been used to support abstraction and aggregation of healthcare concepts using virtual objects and composite virtual objects with ontologies. Besides, semantic annotation of healthcare data has been achieved with a simplified annotation algorithm. The alignment of diverse data models has been supported with the deep representation learning method. Semantic annotation and alignment provide a common understanding of data and cohesive integration, respectively. The semantic mediation model is backed with a target ontology catalog and standard vocabulary. Healthcare data is modeled using the standard Resource Description Framework (RDF), which provides triples structure to describe the healthcare concepts in a unified way. We demonstrate the semantic mediation process with the experimental settings and provide details on the utilization of the proposed model.

Semantic Health Mediation and Access Control Manager for Interoperability Among Healthcare Systems

Healthcare systems have evolved to become more patient-centric. Many efforts have been made to transform paper-based patient data to automated medical information by developing electronic healthcare records (EHRs). Several international EHRs standards have been enabling healthcare interoperability and communication among a wide variety of medical centres. It is a dual-model methodology which comprises a reference information model and an archetype model. The archetype is responsible for the definition of clinical concepts which has limitations in terms of supporting complex reasoning and knowledge discovery requirements. The objective of this article is to propose a semantic-mediation architecture to support semantic interoperability among healthcare organizations. It provides an intermediate semantic layer to exploit clinical information based on richer ontological representations to create a “model of meaning” for enabling semantic mediation. The proposed model also provides secure mechanisms to allow interoperable sharing of patient data between healthcare organizations.

Semantic Mediation in MedPeer

Peer-to-Peer (P2P) infrastructure is an emerging paradigm that offers new opportunities for the development of large-scale distributed systems. This architecture combined with the new techniques introduced by semantic web as ontologies encouraged the emergence of new multi-source data integration possibilities for sharing information. A challenging problem in such systems is to find correspondences between concepts of their different ontologies. This is a necessary step before locating peers that are relevant with respect to a given query. In this paper, the authors propose a new ontology alignment method which deals with both linguistic and semantic characteristics of concepts and uses graph structure to explore multiple depth levels of neighborhood in calculation of semantic similarity which is the most important part of their global similarity measure. This function is implemented into their new P2P heterogeneous and distributed data integration system MedPeer.

Using Category Theory to Facilitate Multiple Manufacturing Service Database Integration

The goal of this paper is to illustrate the use of category theory (CT) as a basis for the integration of manufacturing service databases. In this paper, we use as our reference prior work by Kulvatunyou et al. (2013, “An Analysis of OWL-Based Semantic Mediation Approaches to Enhance Manufacturing Service Capability Models,” Int. J. Comput. Integr. Manuf., 27(9), pp. 803–823) on the use of web ontology language (OWL)-based semantic web tools to study the integration of different manufacturing service capability (MSC) databases using a generic-model approach that they propose in their paper. We approach the same task using a different set of tools, specifically CT and FQL, a functorial query language based on categorical mathematics. This work demonstrates the potential utility of category-theoretic information management tools and illustrates some advantages of categorical techniques for the integration and evolution of databases. We conclude by making the case that a category-theoretic approach can provide a more flexible and robust approach to integration of existing and evolving information.