Ontology assisted query reformulation using the semantic and assertion capabilities of OWL-DL ontologies

Munir, Kamran; Odeh, Mohammed; McClatchey, Richard

doi:10.1145/1451940.1451953

Cited by 10 publications

(14 citation statements)

References 22 publications

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“…Despite the variety of approaches presented so far, three major concerns can be raised when we request information extraction from available data: (1) what type of requests can a specific system handle? (2) how can visual interfaces be provided to generate data requests?…”

Section: The Problem In Generalmentioning

confidence: 99%

“…In relation to the above problem, this research aims: (1) to assist end users in formulating relational database queries without requiring a complete knowledge of the information structure and access mechanisms to the underlying data sources; and (2) to enable the developed query formulation methods to : (a) be flexible in terms of accommodating changes in the underlying database schema; and (b) provide access to existing relational database without manipulating or replicating the transactional data. These research aims have been achieved by building an ontology to be the repository for end user queries.…”

Section: Research Aimsmentioning

confidence: 99%

“…To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. IDEAS09 2009, September 16-18, Cetraro, Calabria [Italy] Editor: Bipin C. DESAI Copyright ©2009 ACM 978-1-60558-402-7/09/09 $5.00" several heuristic based ontology to relational query translation algorithms have already been reported in [1] and [3]. This paper reports on further developments in this area and particularly focuses on (1) domain metadata representation from a relational model to an ontology model to enable ontology based query formulation; (2) storage and retrieval of ontology-database mappings to translate ontology statements into relational query statements as per the underlying schema structure; and (3) an ontology assisted query formulation case study in relation to the Health-e-Child [4] project.…”

Section: Research Aimsmentioning

confidence: 99%

“…In order to test our query formulation system and the developed methodologies, a detailed case study has been implemented (reported in Section 7) using a large subset of the Health-e-Child (HeC) patients' medical data collected during the requirements gathering and data-collection phases of the HeC project. Figure 1 shows the Ontology Assisted Relational Query Formulation (OntoQF) architectural framework as reported in [1].…”

Section: Semantic Query Formulation In Health-e-childmentioning

confidence: 99%

See 3 more Smart Citations

Managing the mappings between domain ontologies and database schemas when formulating relational queries

Munir

Odeh

McClatchey

2009

Proceedings of the 2009 International Database Engineering &Amp; Applications Symposium on - IDEAS '09

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Section: The Problem In Generalmentioning

confidence: 99%

Section: Research Aimsmentioning

confidence: 99%

Section: Research Aimsmentioning

confidence: 99%

Section: Semantic Query Formulation In Health-e-childmentioning

confidence: 99%

See 2 more Smart Citations

Managing the mappings between domain ontologies and database schemas when formulating relational queries

Munir

Odeh

McClatchey

2009

Proceedings of the 2009 International Database Engineering &Amp; Applications Symposium on - IDEAS '09

Self Cite

View full text Add to dashboard Cite

“…Knowledge integration enhances query formulation when the information structure and data sources are not known, as highlighted in [76], and is becoming increasingly important in data matching processes as various data structures conceptualise the same concept in different ways, with resulting inconsistencies and overlapping material. Integration can be based on extensions or concepts, and is aimed at indemnifying inconsistencies and mismatches in the concepts.…”

Section: Knowledge Integrationmentioning

confidence: 99%

Data linkage for querying heterogeneous databases

Gollapalli¹

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Data Linkage is an important step that can provide valuable insights for evidence-based decision making, especially for crucial events. Performing sensible queries across heterogeneous databases containing millions of records is a complex task that requires a complete understanding of each contributing database's schema to define the structure of its information. The key aim is to approximate the structure and content of the induced data into a concise synopsis in order to extract and link meaningful facts. Current techniques primarily focus on performing pair-wise attribute matching and pay little attention in discovering direct and weighted cluster correlations for linking semantic equivalent datasets. We identify such problems as four major research issues in Data Linkage: associated costs in pair-wise matching, record matching overheads, semantic flow of information restrictions, and single order classification limitations.In this doctorial dissertation, we introduce a new multi-faceted classification technique for performing structural analysis on knowledge domain clusters, using a novel Ontology Guided Data Linkage (OGDL) framework. In order to support self-organization of contributing databases through the discovery of structural dependencies, we introduce a series of algorithms for performing multi-level exploitation of ontological domain knowledge relating to tables, attributes and tuples. These techniques are of great help for automating the discovery of schema structures across multiple databases, based on the use of direct and weighted correlations between different ontological concepts, using a novel h-gram (hash gram) record matching technique for concept clustering and cluster mapping. Moreover, through a set of accuracy, performance and scalability experimental tests run on real-world datasets, we demonstrate the feasibility of our OGDL algorithms and show that our framework runs in polynomial time and performs well in practice.Data Linkage is an important enabling technology in eHealth as linked data is a cost effective approach towards advancing research outcomes into health policies, detect any adverse drug reactions, reduce costs, and uncover any non-practices within the health system. Hence, to illustrate the efficiency and effectiveness of OGDL in real-world applications, we comprehensively used clinical risk management domain as our practical example. For this reason, we further extended our OGDL framework and introduced a composite clinical risk management success indicator data linkage, which consists of clinical risk factors combined with clinical resource and intervention factors that have shown to be asii sociated with good and safe patient outcomes and with quality health care. The aim is to introduce a novel primitive upper ontology for semantic interoperability of health data and subsequent clinical risk management, and use it to map patient case data to reason about problems and solutions. Our experiments are performed on the Australian emergency medicine clinical trial da...

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