An ontology-based comparative anatomy information system

Formal Ontologies in Information Systems Development: A Systematic Review

Computational ontologies are machine-processable structures which represent particular domains of interest. They integrate knowledge which can be used by humans or machines for decision making and problem solving. The main aim of this systematic review is to investigate the role of formal ontologies in information systems development, i.e., how these graphs-based structures can be beneficial during the analysis and design of the information systems. Specific online databases were used to identify studies focused on the interconnections between ontologies and systems engineering. One-hundred eighty-seven studies were found during the first phase of the investigation. Twenty-seven studies were examined after the elimination of duplicate and irrelevant documents. Mind mapping was substantially helpful in organising the basic ideas and in identifying five thematic groups that show the main roles of formal ontologies in information systems development. Formal ontologies are mainly used in the interoperability of information systems, human resource management, domain knowledge representation, the involvement of semantics in unified modelling language (UML)-based modelling, and the management of programming code and documentation. We explain the main ideas in the reviewed studies and suggest possible extensions to this research.

Integrating terminologies into standard SQL: a new approach for research on routine data

Background

Most electronic medical records still contain large amounts of free-text data. Semantic evaluation of such data requires the data to be encoded with sufficient classifications or transformed into a knowledge-based database.

Methods

We present an approach that allows databases accessible via SQL (Structured Query Language) to be searched directly through semantic queries without the need for further transformations. Therefore, we developed I) an extension to SQL named Ontology-SQL (O-SQL) that allows to use semantic expressions, II) a framework that uses a standard terminology server to annotate free-text containing database tables and III) a parser that rewrites O-SQL to SQL, so that such queries can be passed to the database server.

Results

I) We compared several semantic queries published to date and were able to reproduce them in a reduced, highly condensed form. II) The quality of the annotation process was measured against manual annotation, and we found a sensitivity of 97.62% and a specificity of 100.00%. III) Different semantic queries were analyzed, and measured with F-scores between 0.91 and 0.98.

Conclusions

We showed that systematic analysis of free-text-containing medical records is possible with standard tools. The seamless connection of ontologies and standard technologies from the database field represents an important constituent of unstructured data analysis. The developed technology can be readily applied to relationally organized data and supports the increasingly important field of translational research.

Electronic supplementary material

The online version of this article (10.1186/s13326-019-0199-z) contains supplementary material, which is available to authorized users.

Recognizing lexical and semantic change patterns in evolving life science ontologies to inform mapping adaptation

BACKGROUND

Mappings established between life science ontologies require significant efforts to maintain them up to date due to the size and frequent evolution of these ontologies. In consequence, automatic methods for applying modifications on mappings are highly demanded. The accuracy of such methods relies on the available description about the evolution of ontologies, especially regarding concepts involved in mappings. However, from one ontology version to another, a further understanding of ontology changes relevant for supporting mapping adaptation is typically lacking.

METHODS

This research work defines a set of change patterns at the level of concept attributes, and proposes original methods to automatically recognize instances of these patterns based on the similarity between attributes denoting the evolving concepts. This investigation evaluates the benefits of the proposed methods and the influence of the recognized change patterns to select the strategies for mapping adaptation.

RESULTS

The summary of the findings is as follows: (1) the Precision (>60%) and Recall (>35%) achieved by comparing manually identified change patterns with the automatic ones; (2) a set of potential impact of recognized change patterns on the way mappings is adapted. We found that the detected correlations cover ∼66% of the mapping adaptation actions with a positive impact; and (3) the influence of the similarity coefficient calculated between concept attributes on the performance of the recognition algorithms.

CONCLUSIONS

The experimental evaluations conducted with real life science ontologies showed the effectiveness of our approach to accurately characterize ontology evolution at the level of concept attributes. This investigation confirmed the relevance of the proposed change patterns to support decisions on mapping adaptation.

The ontology of craniofacial development and malformation for translational craniofacial research

We introduce the Ontology of Craniofacial Development and Malformation (OCDM) as a mechanism for representing knowledge about craniofacial development and malformation, and for using that knowledge to facilitate integrating craniofacial data obtained via multiple techniques from multiple labs and at multiple levels of granularity. The OCDM is a project of the NIDCR-sponsored FaceBase Consortium, whose goal is to promote and enable research into the genetic and epigenetic causes of specific craniofacial abnormalities through the provision of publicly accessible, integrated craniofacial data. However, the OCDM should be usable for integrating any web-accessible craniofacial data, not just those data available through FaceBase. The OCDM is based on the Foundational Model of Anatomy (FMA), our comprehensive ontology of canonical human adult anatomy, and includes modules to represent adult and developmental craniofacial anatomy in both human and mouse, mappings between homologous structures in human and mouse, and associated malformations. We describe these modules, as well as prototype uses of the OCDM for integrating craniofacial data. By using the terms from the OCDM to annotate data, and by combining queries over the ontology with those over annotated data, it becomes possible to create "intelligent" queries that can, for example, find gene expression data obtained from mouse structures that are precursors to homologous human structures involved in malformations such as cleft lip. We suggest that the OCDM can be useful not only for integrating craniofacial data, but also for expressing new knowledge gained from analyzing the integrated data.

Uberon, an integrative multi-species anatomy ontology

We present Uberon, an integrated cross-species ontology consisting of over 6,500 classes representing a variety of anatomical entities, organized according to traditional anatomical classification criteria. The ontology represents structures in a species-neutral way and includes extensive associations to existing species-centric anatomical ontologies, allowing integration of model organism and human data. Uberon provides a necessary bridge between anatomical structures in different taxa for cross-species inference. It uses novel methods for representing taxonomic variation, and has proved to be essential for translational phenotype analyses. Uberon is available at http://uberon.org

Collaboration-based medical knowledge recommendation

PURPOSE

Clinicians rely on a large amount of medical knowledge when performing clinical work. In clinical environment, clinical organizations must exploit effective methods of seeking and recommending appropriate medical knowledge in order to help clinicians perform their work.

METHOD

Aiming at supporting medical knowledge search more accurately and realistically, this paper proposes a collaboration-based medical knowledge recommendation approach. In particular, the proposed approach generates clinician trust profile based on the measure of trust factors implicitly from clinicians' past rating behaviors on knowledge items. And then the generated clinician trust profile is incorporated into collaborative filtering techniques to improve the quality of medical knowledge recommendation, to solve the information-overload problem by suggesting knowledge items of interest to clinicians.

RESULTS

Two case studies are conducted at Zhejiang Huzhou Central Hospital of China. One case study is about the drug recommendation hold in the endocrinology department of the hospital. The experimental dataset records 16 clinicians' drug prescribing tracks in six months. This case study shows a proof-of-concept of the proposed approach. The other case study addresses the problem of radiological computed tomography (CT)-scan report recommendation. In particular, 30 pieces of CT-scan examinational reports about cerebral hemorrhage patients are collected from electronic medical record systems of the hospital, and are evaluated and rated by 19 radiologists of the radiology department and 7 clinicians of the neurology department, respectively. This case study provides some confidence the proposed approach will scale up.

CONCLUSION

The experimental results show that the proposed approach performs well in recommending medical knowledge items of interest to clinicians, which indicates that the proposed approach is feasible in clinical practice.