Information extraction and integration from heterogeneous, distributed, autonomous information sources - a federated ontology-driven query-centric approach

Castillo, Juan Alberto; Silvescu, Adrian; Caragea, Doina; Pathak, Jyotishman; Honavar, Vasant

doi:10.1109/iri.2003.1251412

Cited by 26 publications

(9 citation statements)

References 18 publications

(6 reference statements)

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“…temporally); to display a data schema or to inform navigation through the data [22]. In particular cases, ontology-based visualization has been used to support queries based on temporal abstractions [23]; to enrich maps with additional geographic information [24]; to reveal multiple levels of abstraction in decision-tree generation [25] and to assist in information mining [26]; very popularly, to map social networks and communities of common interest [27], [28], [29]. Ontologies have also been used for knowledge discovery without visualization, especially in the integration of heterogeneous scientific repositories ( [30]).…”

Section: Related Approaches In Knowledge Visualizationmentioning

confidence: 99%

An Architecture for Semantic Navigation and Reasoning with Patient Data - Experiences of the Health-e-Child Project

Hauer

Rogulin

Zillner

et al. 2008

Lecture Notes in Computer Science

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Abstract. Medical ontologies have become the standard means of recording and accessing conceptualized biological and medical knowledge. The expressivity of these ontologies goes from simple concept lists through taxonomies to formal logical theories. In the context of patient information, their application is primarily annotation of medical (instance) data. To exploit higher expressivity, we propose an architecture which allows for reasoning on patient data using OWL DL ontologies. The implementation is carried out as part of the Health-e-Child platform prototype. We discuss the use case where ontologies establish a hierarchical classification of patients which in turn is used to aid the visualization of patient data. We briefly discuss the treemap-based patient viewer which has been evaluated in the Health-e-Child project.

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Section: Related Approaches In Knowledge Visualizationmentioning

confidence: 99%

An Architecture for Semantic Navigation and Reasoning with Patient Data - Experiences of the Health-e-Child Project

Hauer

Rogulin

Zillner

et al. 2008

Lecture Notes in Computer Science

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“…They propose that meta-models of distributed data sources and classification schemes can only be solved successfully through the thorough development of a distributed application ontology. In Reinoso Castillo, et al, 6 an Intelligent Data Understanding System (INDUS) is described that is based on a federated architecture of distributed data sources. The INDUS project does not specifically call out autonomous agent technology as a key component, although their instantiator concept is similar in function to an agent.…”

Section: B "What-if" and Predictive Questionsmentioning

confidence: 99%

Exploring Diverse Data Sets and Developing New Theories and Ideas with Project Integration Architecture

Benyo

Jones

2004

AIAA 1st Intelligent Systems Technical Conference

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“…In order to have the capacity to do so, data integration system must be able to do the following process: communication and interaction with data sources, unifying different queries in requester specifying vocabulary (ontology) across multiple autonomous, distributed and heterogeneous data sources, mapping techniques between requester ontology and the data source ontology, extracting information from the query with respect to the target data sources, and finally translating the query results to the requester vocabulary [4].…”

Section: Introductionmentioning

confidence: 99%

ROHDIP: Resource Oriented Heterogeneous Data Integration Platform

Shehab¹,

El-Gokhy²,

Sallam³

2016

ijacsa

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Abstract-During the last few years, the revolution of social networks such as Facebook, Twitter, and Instagram led to a daily increasing of data that are heterogeneous in their sources, data models, and platforms. Heterogeneous data sources have many forms such as the www, deep web, relational databases systems, No-SQL database systems, hierarchal data systems, semistructured files, in which data are usually allocated on different machines (distributed) and have different data models (heterogeneous).Large-scale data integration efforts demonstrate that their most valuable contribution is implementing a data integration platform that provides a uniform access to the heterogeneous data sources, as well as the different versions of data reported by the same data source over time. Furthermore, the platform must be able to integrate data from a broad range of data authoring devices and database management systems. It also should be accessible by almost types of data querying devices to ensure globally querying the integration platform from any place on earth anytime and receiving the query result in any data format.In this paper, we create a resource oriented heterogeneous data integration platform (ROHDIP) that facilitates the data integration process and implements the objectives discussed above. We use the resource oriented architecture ROA to support the uniform access by most types of data querying devices from anywhere and to improve the query response time.

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Information extraction and integration from heterogeneous, distributed, autonomous information sources - a federated ontology-driven query-centric approach

Cited by 26 publications

References 18 publications

An Architecture for Semantic Navigation and Reasoning with Patient Data - Experiences of the Health-e-Child Project

An Architecture for Semantic Navigation and Reasoning with Patient Data - Experiences of the Health-e-Child Project

Exploring Diverse Data Sets and Developing New Theories and Ideas with Project Integration Architecture

ROHDIP: Resource Oriented Heterogeneous Data Integration Platform

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