Semantic Interoperability Challenges Research Articles

An Intelligent System to Ensure Interoperability for the Dairy Farm Business Model

Picking reliable partners, negotiating synchronously with all partners, and managing similar proposals are challenging tasks for any manager. This challenge is even harder when it concerns small and medium enterprises (SMEs) who need to deal with short budgets and evident size limitations, often leading them to avoid handling very large contracts. This size problem can only be mitigated by collaboration efforts between multiple SMEs, but then again this brings back the initially stated issues. To address these problems, this paper proposes a collaborative negotiation system that automates the outsourcing part by assisting the manager throughout a negotiation. The described system provides a comprehensive view of all negotiations, facilitates simultaneous bilateral negotiations, and provides support for ensuring interoperability among multiple partners negotiating on a task described by multiple attributes. In addition, it relies on an ontology to cope with the challenges of semantic interoperability, it automates the selection of reliable partners by using a lattice-based approach, and it manages similar proposals by allowing domain experts to define a satisfaction degree for each SME. To showcase this method, this research focused on small and medium-size dairy farms (DFs) and describes a negotiation scenario in which a few DFs are able to assess and generate proposals.

Mapping clinical procedures to the ICD-10-PCS: The German operation and procedure classification system use case.

Mappings among terminologies to ensure homogeneous analysis among different data sources is one of the key challenges of semantic interoperability. Concretely, mappings to the International Classification of Diseases 10th Revision Procedure Classification System (ICD-10-PCS) are especially challenging due to its multiaxial structure and lack of terms used by physicians (many terminologies used in real world data (RWD) are initially intended for reimbursement, not for clinical purposes). In this work, we propose a new theoretical methodology for mapping healthcare data to the ICD-10-PCS by exploiting its multiaxial structure to reduce the search spaces within concepts and leveraging the dependencies between axes for inferring additional relevant information. We tested this methodology with a subset of the German Operation and Procedure Classification System (OPS), aiming to integrate heterogeneous data sources queried for clinical research.

The Challenges of Semantic Interoperability in the Era of eScience on the Web

This article is an exploratory study that intends to present semantic interoperability initiatives in the area of information science and computer science. It discusses these initiatives, focusing on open science and eScience, aiming at pointing out similarities and differences in the methodologies used for an intelligent retrieval of data in heterogeneous environments that do not benefit a universal language. We will illustrate seminal initiatives regarding the compatibility of languages in information science and the alignment and semantic mapping in the area of computer science, emphasizing what they have in common and their differences, showing that those seminal initiatives are still relevant nowadays. In this perspective, we want to bring up discussions about methodologies that supports a kind of “intermediate language” that allows the compatibility of research resources, semantically interconnecting their contents without forcing the use of a single language, but of a switching language, such as a metalanguage that guarantees to keep the differences of each community of speech.

A semantic interoperability framework for distributed electronic health record based on fuzzy ontology

To achieve an efficient healthcare process; the professionals and doctors need to access the complete data about their patients in the suitable time. In the face of that issue; healthcare semantic interoperability is still a huge problem without solving. In this paper, a unified semantic interoperability framework for distributed EHR based on fuzzy ontology is proposed. The lowest layer stores the EHRs heterogeneous data with different models. In the middle layer, the local ontologies are mapped to a global crisp one. The global reference ontology combines and integrates all local ontologies and therefore describes all data. In the user interface layer, any linguistic or semantic queries can be done by dealing with only the global reference fuzzy ontology. We expect that our framework will handle the current EHR semantic interoperability challenges, reduce the cost of the integration process, and get a higher acceptance and accuracy rate than previous studies.

Semantic interoperability challenges to process large amount of data perspectives in forensic and legal medicine

This article is a position paper dealing with semantic interoperability challenges. It addresses the Variety and Veracity dimensions when integrating, sharing and reusing large amount of heterogeneous data for data analysis and decision making applications in the healthcare domain. Many issues are raised by the necessity to conform Big Data to interoperability standards. We discuss how semantics can contribute to the improvement of information sharing and address the problem of data mediation with domain ontologies. We then introduce the main steps for building domain ontologies as they could be implemented in the context of Forensic and Legal medicine. We conclude with a particular emphasis on the current limitations in standardisation and the importance of knowledge formalization.

Using ontologies to improve semantic interoperability in health data

The present–day health data ecosystem comprises a wide array of complex heterogeneous data sources. A wide range of clinical, health care, social and other clinically relevant information are stored in...

Semantic interoperability of controlled vocabularies in medicine: A case study of the International Statistical Classification of Diseases ‘Tuberculosis’ subset

Controlled vocabularies have been promoted for the achievement of semantic interoperability in e-health. However, the current implementations of healthcare information systems struggle with the remaining encoded semantics inside each particular data model. Multilevel modelling has been proposed for overcoming the challenges of semantic interoperability in healthcare, with different approaches for binding domain models to standard vocabularies; however, proofs of concept are still needed. This paper presents the fundamentals of knowledge modelling with standard vocabularies using the Multilevel Healthcare Information Modelling (MLHIM) specifications. The implementation of one term subset (‘Tuberculosis’) of the 10th Revision of the International Classification of Diseases (ICD-10) in MLHIM domain models, the Concept Constraint Definitions (CCD), is described, using the Brazilian mortality and hospital information systems as use cases. Technical details of the semantic validation of data instances generated according to the XML instances that include this ICD-10 term set, as well as the correspondent migration of the original databases to MLHIM-compliant databases, are presented.

MAKOCI: A WEB PORTAL FOR INTEGRATING AND SHARING GEOGRAPHIC INFORMATION SERVICES

The lack of integration and communication of various geographic information services (GI services) has resulted in many duplication collection of earth observation data, and challenges of semantic interoperability. This paper proposes an ontology-based multi-agents platform, called MAKOCI (multi-agent knowledge oriented cyberinfrastructure), which acts as GI service one stop to manage, publish, share, and discover GI services semantically. By ontologies, formal meanings of concepts are defined to annotate and discover GI services on a conceptual level for semantic interoperability. With the assistance of multi-agents, the processes in MAKOCI can be divided into various modules and be communicated based on the same semantics in ontologies. A prototype was implemented to test MAKOCI. Finally, we conclude the advantages and disadvantages of MAKOCI and point out several future works.

A conceptual investigation of the ontological commensurability of spatial data infrastructures among different cultures

Humans think and communicate in very flexible and schematic ways, and a Spatial Data Infrastructure (SDI) for the Amazon and associated information system ontologies should reflect this flexibility and the adaptive nature of human cognition in order to achieve semantic interoperability. In this paper I offer a conceptual investigation of SDI and explore the nature of cultural schemas as expressions of indigenous ontologies and the challenges of semantic interoperability across cultures. Cultural schemas are, in essence, our ontologies, but they are markedly different than classical formal ontologies. They shape our ontological commitments to what exists in the world as well as the ways in which we approach and engage the world. And while they help structure our understanding of the world in which we are embedded, they are associative and flexible. They help to focus our attention to particular details of our experiences and give them salience, yet they cannot be simply reduced to a series of extracted features. They allow us to make meaning of the contextualized, cultural experience in which we are always immersed. An SDI is a shared social-technological-informational structure that, if it is to be useful and successful for sustainability in the Amazon, must incorporate and use indigenous cultural schemas. Indigenous communities must have the ability to contribute to the collection of geospatial data and their contributions recognized as legitimate forms of knowledge. In order for the SDI to work, it must recognize the larger cultural landscape to which cultural schemas can connect to the ready-to-hand elements of salient cultural experiences.

Knowledge framework for intelligent manufacturing systems

Nowadays, competition is experienced not only among companies but among global supply chains and business networks. There is a demand for intelligent world-class solutions capable of reinforcing partnerships and collaborations with an improved cross-cultural understanding. However due to the proliferation of terminology, organizations from similar business environments have trouble cooperating, and are experiencing difficulties exchanging electronically vital information, such as product and manufacturing data, even when using international standards. To address similar interoperability problems, the Intelligent manufacturing systems program ( http://www.ims.org/content/glossary ) is providing an opportunity to develop industry-led R&D initiatives, building common semantics and integrated solutions. The SMART-fm project was one of those initiatives. It led to the development of the international standard for product data representation and exchange in the furniture sector (ISO 10303-236) and identified the challenge of semantic interoperability which is today a major challenge in modern enterprise integration. This paper presents a knowledge framework to address that challenge and make interoperable intelligent manufacturing systems a reality. It proposes to use semantically enriched international product data standards, and knowledge representation elements as a basis for achieving seamless enterprise interoperability.