论文修改 Data mining and semantic web

3.3 The requirements for ontologies in medical data integration

Information technology today is widely adopted in modern medical practice, especially supporting digitized equipment, administrative tasks, and data management. But computational techniques doesn't use much of this medical information in research or practice because the laws of medicine are knowledge based disciplines and rely greatly on observed similarities rather than on the application of precise rules. In [5] the Health-e-Child (HeC) project is conducted to demonstrate that indeed integrating medical integration in novel ways yields immediate benefit for clinical research and practice. It aims to develop an integrated platform for European Paediatrics, providing seamless integration of traditional and emerging sources of biomedical information as part of a longer-term vision for large-scale information-based research and training, and informed policy making.

To have a vertical integration of data that is establishing a coherent view of the child's health to which information from each vertical level contributes, from molecular through cellular to individual, sharing data among spatially separated clinicians and information produced in different departments or multiple hospitals brings together for the purpose of creating statistically significant samples, studying population characteristics and sharing knowledge among clinicians. The emphasis of the Health-e-Child requirements process is therefore on "universality of information" and its corner stone is the integration of information across biomedical abstractions, whereby all layers of biomedical information are 'vertically integrated' to provide a unified view of a child's biomedical and clinical condition.

Ontology is a formal specification of a shared conceptualization. This means that ontology represents a shared, agreed and detailed model of a problem domain. One advantage for the use of ontologies is their ability to resolve any semantic heterogeneity that is present within the data. Ontologies define links between different types of semantic knowledge. The fact that ontologies are machine processable and human understandable is especially useful in this regard. There are many ontologies in existence today especially in the biomedical domain, however they are often limited to one level vertical integration and it would not be sensible to reuse these ontologies in their entirety; so to make an appropriate ontology for Hec available ontologies are integrated bye the extraction of the relevant parts and then the integration of these into a coherent whole, thereby capturing most of the HeC domain but the missing attributes of Hec modeled sepratly. Integration process involves identifying similarities between ontologies in order to determine which concepts and properties represent similar notions across heterogeneous data samples in a (semi-)automatic manner.

As mentioned above use of ontology and inference engine can aid in the area of query enhancement. It provides clinicians with more targeted information. Use of ontology enabled clinicians to take basic queries from users and translate them into more complex context aware searches and minimizes the load on the system as fewer searches are necessary. Query optimization also assists in this regard by using the HeC ontology to aid the creation of efficient data access paths by semantically altering the initial query to find a more efficient execution path within the database. Both query enhancement and optimization are crucial in delivery of intuitive data access for clinicians whilst at the same time ensuring the scalability and overall stability of the system.

4 Conclusion

This paper attempted to find application of semantic web and data mining in different fields. Observed application demonstrated that data mining methods can be very useful for ontology construction and the constructed ontology itself can be used for classification in data mining. Use of ontologies in healthcare has significant effect and cause having better standard of life.

References

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