Semantic Integration for Research Environments

2011 ◽  
pp. 514-530 ◽  
Author(s):  
Tomasz Gubala ◽  
Marian Bubak ◽  
Peter Sloot
Keyword(s):  
Data Integration ◽  
Domain Knowledge ◽  
Coupled System ◽  
Semantic Integration ◽  
Data Sources ◽  
Virtual Laboratory ◽  
Loosely Coupled ◽  
System Components ◽  
Research Environments ◽  
High Level

Research environments for modern, cross-disciplinary scientific endeavors have to unite multiple users, with varying levels of expertise and roles, along with multitudes of data sources and processing units. The high level of required integration contrasts with the loosely-coupled nature of environments which are appropriate for research. The problem is to support integration of dynamic service-based infrastructures with data sources, tools and users in a way that conserves ubiquity, extensibility and usability. This chapter presents a close examination of related achievements in the field and the description of proposed approach. It shows that integration of loosely-coupled system components with formallydefined vocabularies may fulfill the listed requirements. The authors demonstrate that combining formal representations of domain knowledge with techniques like data integration, semantic annotations and shared vocabularies, enables the development of systems for modern e-Science. For demonstration they present how several semantically-augmented experiments are modeled in the ViroLab virtual laboratory for virology.

scholarly journals Helping Novices Avoid the Hazards of Data: Leveraging Ontologies to Improve Model Generalization Automatically with Online Data Sources

AI Magazine ◽  
2016 ◽  
Vol 37 (2) ◽  
pp. 19-32 ◽  
Author(s):  
Sasin Janpuangtong ◽  
Dylan A. Shell
Keyword(s):  
Domain Knowledge ◽  
Large Scale ◽  
Model Building ◽  
Data Extraction ◽  
Data Sources ◽  
Online Data ◽  
Model Generalization ◽  
Improve Model ◽  
Building Process ◽  
High Level

The infrastructure and tools necessary for large-scale data analytics, formerly the exclusive purview of experts, are increasingly available. Whereas a knowledgeable data-miner or domain expert can rightly be expected to exercise caution when required (for example, around fallacious conclusions supposedly supported by the data), the nonexpert may benefit from some judicious assistance. This article describes an end-to-end learning framework that allows a novice to create models from data easily by helping structure the model building process and capturing extended aspects of domain knowledge. By treating the whole modeling process interactively and exploiting high-level knowledge in the form of an ontology, the framework is able to aid the user in a number of ways, including in helping to avoid pitfalls such as data dredging. Prudence must be exercised to avoid these hazards as certain conclusions may only be supported if, for example, there is extra knowledge which gives reason to trust a narrower set of hypotheses. This article adopts the solution of using higher-level knowledge to allow this sort of domain knowledge to be used automatically, selecting relevant input attributes, and thence constraining the hypothesis space. We describe how the framework automatically exploits structured knowledge in an ontology to identify relevant concepts, and how a data extraction component can make use of online data sources to find measurements of those concepts so that their relevance can be evaluated. To validate our approach, models of four different problem domains were built using our implementation of the framework. Prediction error on unseen examples of these models show that our framework, making use of the ontology, helps to improve model generalization.

scholarly journals An approach for semantic integration of heterogeneous data sources

2020 ◽  
Vol 6 ◽  
pp. e254
Author(s):  
Giuseppe Fusco ◽  
Lerina Aversano
Keyword(s):  
Data Integration ◽  
Heterogeneous Data ◽  
Semantic Integration ◽  
Data Sources ◽  
Complex Data ◽  
Semantic Heterogeneity ◽  
Heterogeneous Information ◽  
Heterogeneous Data Sources ◽  
Autonomous Data Sources ◽  
Unified View

Integrating data from multiple heterogeneous data sources entails dealing with data distributed among heterogeneous information sources, which can be structured, semi-structured or unstructured, and providing the user with a unified view of these data. Thus, in general, gathering information is challenging, and one of the main reasons is that data sources are designed to support specific applications. Very often their structure is unknown to the large part of users. Moreover, the stored data is often redundant, mixed with information only needed to support enterprise processes, and incomplete with respect to the business domain. Collecting, integrating, reconciling and efficiently extracting information from heterogeneous and autonomous data sources is regarded as a major challenge. In this paper, we present an approach for the semantic integration of heterogeneous data sources, DIF (Data Integration Framework), and a software prototype to support all aspects of a complex data integration process. The proposed approach is an ontology-based generalization of both Global-as-View and Local-as-View approaches. In particular, to overcome problems due to semantic heterogeneity and to support interoperability with external systems, ontologies are used as a conceptual schema to represent both data sources to be integrated and the global view.

Formalization and Semantic Integration of Heterogeneous Omics Annotations for Exploratory Searches

2020 ◽  
Vol 15 ◽  
Author(s):  
Omer Irshad ◽  
Muhammad Usman Ghani Khan
Keyword(s):  
Data Integration ◽  
Semantic Integration ◽  
Biological Data ◽  
Cellular System ◽  
Formal Specifications ◽  
Integration Model ◽  
Geographically Dispersed ◽  
Proposed Model ◽  
Data Heterogeneity ◽  
Biological Entities

Aim: To facilitate researchers and practitioners for unveiling the mysterious functional aspects of human cellular system through performing exploratory searching on semantically integrated heterogeneous and geographically dispersed omics annotations. Background: Improving health standards of life is one of the motives which continuously instigates researchers and practitioners to strive for uncovering the mysterious aspects of human cellular system. Inferring new knowledge from known facts always requires reasonably large amount of data in well-structured, integrated and unified form. Due to the advent of especially high throughput and sensor technologies, biological data is growing heterogeneously and geographically at astronomical rate. Several data integration systems have been deployed to cope with the issues of data heterogeneity and global dispersion. Systems based on semantic data integration models are more flexible and expandable than syntax-based ones but still lack aspect-based data integration, persistence and querying. Furthermore, these systems do not fully support to warehouse biological entities in the form of semantic associations as naturally possessed by the human cell. Objective: To develop aspect-oriented formal data integration model for semantically integrating heterogeneous and geographically dispersed omics annotations for providing exploratory querying on integrated data. Method: We propose an aspect-oriented formal data integration model which uses web semantics standards to formally specify its each construct. Proposed model supports aspect-oriented representation of biological entities while addressing the issues of data heterogeneity and global dispersion. It associates and warehouses biological entities in the way they relate with Result: To show the significance of proposed model, we developed a data warehouse and information retrieval system based on proposed model compliant multi-layered and multi-modular software architecture. Results show that our model supports well for gathering, associating, integrating, persisting and querying each entity with respect to its all possible aspects within or across the various associated omics layers. Conclusion: Formal specifications better facilitate for addressing data integration issues by providing formal means for understanding omics data based on meaning instead of syntax

Git4Voc: Collaborative Vocabulary Development Based on Git

2016 ◽  
Vol 10 (02) ◽  
pp. 167-191 ◽  
Author(s):  
Lavdim Halilaj ◽  
Irlán Grangel-González ◽  
Gökhan Coskun ◽  
Steffen Lohmann ◽  
Sören Auer
Keyword(s):  
Control System ◽  
Data Integration ◽  
Software Development ◽  
Domain Knowledge ◽  
Vocabulary Development ◽  
Version Control ◽  
Version Control System

Collaborative vocabulary development in the context of data integration is the process of finding consensus between experts with different backgrounds, system understanding and domain knowledge. The complexity of this process increases with the number of people involved, the variety of the systems to be integrated and the dynamics of their domain. In this paper, we advocate that the usage of a powerful version control system is one of the keys to address this problem. Driven by this idea and the success of the version control system Git in the context of software development, we investigate the applicability of Git for collaborative vocabulary development. Even though vocabulary development and software development have much more similarities than differences, there are still important challenges. These need to be considered in the development of a successful versioning and collaboration system for vocabulary development. Therefore, this paper starts by presenting the challenges we are faced with during the collaborative creation of vocabularies and discusses its distinction to software development. Drawing from these findings, we present Git4Voc which comprises guidelines on how Git can be adopted to vocabulary development. Finally, we demonstrate how Git hooks can be implemented to go beyond the plain functionality of Git by realizing vocabulary-specific features like syntactic validation and semantic diffs.

Simulation and Relationship Strength: Characteristics of Knowledge Flows Among Subjects in a Regional Innovation System

2021 ◽  
pp. 097172182110204
Author(s):  
Yi Su ◽  
Xuesong Jiang ◽  
Zhouzhou Lin
Keyword(s):  
Innovation System ◽  
Small World ◽  
Coupled System ◽  
Mean Value ◽  
Knowledge Flows ◽  
Knowledge Flow ◽  
Regional Innovation System ◽  
Regional Innovation ◽  
Relationship Strength ◽  
Loosely Coupled

A small-world simulation model of a regional innovation system combining the strength of the intersubject relationship of the regional innovation system with the loosely coupled system is constructed. We use a simulation to observe knowledge flow within the regional innovation system under relationships of varying strength. The results show that when the relationship between the subjects of the regional innovation system reaches a certain strength, the system will exhibit high module independence and high network integrity, forming a loosely coupled system. The knowledge flow in the system exhibits the emergence of a fast flow rate, a high mean value and little variance. When relationship strength is at other levels, the emergence of knowledge cannot be identified.

scholarly journals The Association Between Dental Anxiety And Psychiatric Disorders And Symptoms: A Systematic Review

2018 ◽  
Vol 14 (1) ◽  
pp. 207-222 ◽  
Author(s):  
Harri Halonen ◽  
Jenna Nissinen ◽  
Heli Lehtiniemi ◽  
Tuula Salo ◽  
Pirkko Riipinen ◽  
...  
Keyword(s):  
Systematic Review ◽  
Psychiatric Disorders ◽  
Web Of Science ◽  
Dental Anxiety ◽  
Data Sources ◽  
Inclusion Criteria ◽  
Clinical Interviews ◽  
Specific Phobias ◽  
Axis I ◽  
High Level

Background:A growing amount of evidence suggests that dental anxiety is associated with other psychiatric disorders and symptoms. A systematic review was conducted to critically evaluate the studies of comorbidity of dental anxiety with other specific phobias and other Axis I psychiatric disorders.Objective:The aim of the review was to explore how dental anxiety is associated with other psychiatric disorders and to estimate the level of comorbid symptoms in dental anxiety patients.Methods:The review was conducted and reported in accordance with the MOOSE statement. Data sources included PubMed, PsycInfo, Web of Science and Scopus.Results:The search produced 631 hits, of which 16 unique records fulfilled the inclusion criteria. The number of eligible papers was low. Study populations were heterogeneous including 6,486 participants, and a total of 25 tests and in few cases clinical interviews were used in the evaluation processes. The results enhanced the idea about the comorbidity between dental anxiety and other psychiatric disorders. The effect was found strong in several studies.Conclusion:Patients with a high level of dental anxiety are more prone to have a high level of comorbid phobias, depression, mood disorders and other psychiatric disorders and symptoms.

scholarly journals Using ontology merging for the integration of information systems and the production capacity planning system

2019 ◽  
pp. 401-408
Author(s):  
N Yarushkina ◽  
A Romanov ◽  
A Filippov ◽  
A Dolganovskaya ◽  
M Grigoricheva
Keyword(s):  
Information System ◽  
Information Systems ◽  
Relational Database ◽  
Data Model ◽  
Capacity Planning ◽  
Production Capacity ◽  
Semantic Integration ◽  
Data Sources ◽  
Planning System ◽  
Ontology Merging

This article describes the method of integrating information systems of an aircraft factory with the production capacity planning system based on the ontology merging. The ontological representation is formed for each relational database (RDB) of integrated information systems. The ontological representation is formed in the process of analyzing the structure of the relational database of the information system (IS). Based on the ontological representations merging the integrating data model is formed. The integrating data model is a mechanism for semantic integration of data sources.

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