Schema Evolution publication categorizer

Semantic annotations are often used to enrich documents as clinical trials and
electronic health records. However, the usability of these annotations tends to decrease over
time due to the evolution of the domain ontologies. The maintenance of these annotations is
critical for tools that exploit them (eg, search engines and decision support systems) in order
to assure an acceptable level of performance. Despite the recent advances in ontology
evolution systems, the maintenance of semantic annotations remains an open problem. In

Background
Ontologies are increasingly used to structure and semantically describe entities of domains, such as genes and proteins in life sciences. Their increasing size and the high frequency of updates resulting in a large set of ontology versions necessitates efficient management and analysis of this data.

Results

There is an increasing need to interconnect biomedical ontologies. We investigate a simple but promising approach to generate mappings between ontologies by reusing and composing existing mappings across intermediate ontologies. Such an approach is especially promising for highly interconnected ontologies such as in the life science domain. There may be many ontologies that can be used for composition so that the problem arises to find the most suitable ones providing the best results.

Mappings between related ontologies are increasingly used to support data integration and analysis tasks. Changes in the ontologies also require the adaptation of ontology mappings. So far the evolution of ontology mappings has received little attention albeit ontologies change continuously especially in the life sciences. We therefore analyze how mappings between popular life science ontologies evolve for different match algorithms. We also evaluate which semantic ontology changes primarily affect the mappings.

In this paper we survey the most interesting and novel approaches for the representation, integration and management of different kinds of biological data by exploiting XML and the related recommendations and approaches. Moreover, we present new and interesting cutting edge approaches for the appropriate management of heterogeneous biological data represented through XML.

There is an increasing need to interrelate different life science ontologies in order to facilitate data integration or semantic data analysis. Ontology matching aims at a largely automatic generation of mappings between ontologies mostly by calculating the linguistic and structural similarity of their concepts. In this paper we investigate an indirect computation of ontology mappings that composes and thus reuses previously determined ontology mappings that involve intermediate ontologies. The composition approach promises a fast computation of new mappings with reduced manual effort.

Matching life science ontologies to determine ontology mappings has recently become an active field of research. The large size of existing ontologies and the application of complex match strategies for obtaining high quality mappings makes ontology matching a resource- and time-intensive process. To improve performance we investigate different approaches for parallel matching on multiple compute nodes. In particular, we consider inter-matcher and intra-matcher parallelism as well as the parallel execution of element- and structure-level matching.

In recent years, ontologies have become a mainstream topic in biomedical research. When biological entities are described using a common schema, such as an ontology, they can be compared by means of their annotations. This type of comparison is called semantic similarity, since it assesses the degree of relatedness between two entities by the similarity in meaning of their annotations. The application of semantic similarity to biomedical ontologies is recent; nevertheless, several studies have been published in the last few years describing and evaluating diverse approaches.

NCI Thesaurus entries reference corresponding nodes in the UMLS Semantic Network (SN). Adapting a process previously used to refine relationship definitions in the UMLS Metathesaurus, we used these Thesaurus-to-Network references to analyze alignment of the Thesaurus with the OBO Relations Ontology and at the same time validate and improve Thesaurus structure. Given this experience, we offer suggestions for enhancement of the UMLS SN so that it can be even more useful in the future.

For many years, the Unified Medical Language System (UMLS) semantic network (SN) has been used as an upper-level semantic framework for the categorization of terms from terminological resources in biomedicine. BioTop has recently been developed as an upper-level ontology for the biomedical domain. In contrast to the SN, it is founded upon strict ontological principles, using OWL DL as a formal representation language, which has become standard in the semantic Web.

This paper presents the LSLink (or Life Science Link) methodology that provides users with a set of tools to explore the rich Web of interconnected and annotated objects in multiple repositories, and to identify meaningful associations. Consider a physical link between objects in two repositories, where each of the objects is annotated with controlled vocabulary (CV) terms from two ontologies. Using a set of LSLink instances generated from a background dataset of knowledge we identify associations between pairs of CV terms that are potentially significant and may lead to new knowledge.

The field of biomedicine has embraced the Semantic Web probably
more than any other field. As a result, there is a large number of biomedical ontologies
covering overlapping areas of the field. We have developed BioPortal—
an open community-based repository of biomedical ontologies. We analyzed ontologies
and terminologies in BioPortal and the Unified Medical Language System
(UMLS), creating more than 4 million mappings between concepts in these
ontologies and terminologies based on the lexical similarity of concept names

Creating mappings between concepts in different ontologies is a critical step in facilitating data integration. In recent years, researchers have developed many elaborate algorithms that use graph structure, background knowledge, machine learning and other techniques to generate mappings between ontologies. We compared the performance of these advanced algorithms on creating mappings for biomedical ontologies with the performance of a simple mapping algorithm that relies on lexical matching.

Integration of disparate biomedical terminologies is becoming increasingly important as links between biological science and clinical medicine grow. Mapping concepts in the Gene OntologyTM(GO) to the UMLS may help further this integration and allow for more efficient information exchange among researchers. Using a gold standard of GO term – UMLS concept mappings provided by the NCI, we examined the performance of various published and combined mapping techniques, in order to maximize precision and recall.

Several ontology repositories provide access to the growing collection of ontologies on the Semantic Web. Some repositories collect ontologies automatically by crawling the Web; in other repositories, users submit ontologies themselves. In addition to providing search across multiple ontologies, the added value of ontology repositories lies in the metadata that they may contain.

Ontology matching has been widely studied. However, the resulting ontology mappings can be rather unstable when the participating ontologies or utilized secondary sources (e.g., instance sources, thesauri) evolve. We propose an evolution-based approach for assessing ontology mappings by annotating their correspondences by information about similarity values for past ontology versions. These annotations allow us to assess the stability of correspondences over time and they can thus be used to determine better and more robust ontology mappings.

Ontology-based annotations associate objects, such as genes and proteins, with well-defined ontology concepts to semantically and uniformly describe object properties. Such annotation mappings are utilized in different applications and analysis studies whose results strongly depend on the quality of the used annotations. To study the quality of annotations we propose a generic evaluation approach considering the annotation generation methods (provenance) as well as the evolution of ontologies, object sources, and annotations.

We have recently mapped the Gene Ontology (GO), developed by the Gene Ontology Consortium, into the National Library of Medicine’s Unified Medical Language System (UMLS). GO has been developed for the purpose of annotating gene products in genome databases, and the UMLS has been developed as a framework for integrating large numbers of disparate terminologies, primarily for the purpose of providing better access to biomedical information sources. The mapping of GO to UMLS highlighted issues in both terminology systems.

The entities described in the Gene Ontology, (i.e., molecular functions, cellular components and biological processes), often make reference (in their names) to other entities, either from GO or from other ontologies, such as ontologies of chemical entities, cell types and organisms. We developed a method for mapping terms from the Open Biomedical Ontology (OBO) family to GO. We show that 55% of the 17,250GO terms include in their names the name of some chemicalentity (ChEBI). Our findings are consistent with that of other studies.

Research on ontologies is becoming widespread in the biomedical informatics community. At the same time, it has become apparent that the challenges of properly constructing and maintaining ontologies have proven more difficult than many workers in the field initially expected. Discovering general, feasible methods has thus become a central activity for many of those hoping to reap the benefits of ontologies. This paper reviews current methods in the construction, maintenance, alignment, and evaluation of ontologies.

This paper describes how we used generic schema matching algorithms to align the Foundational Model of Anatomy (FMA)and the GALEN Common Reference Model (CRM), two largemodels of human anatomy. We summarize the generic schema
matching algorithms we used to identify correspondences. We present sample results that highlight the similarities and differences between the FMA and the CRM. We also identify uses of aggregation, transitivity, and reification, for which generic schema matching fails to produce an accurate mapping and
present manually constructed solutions for them.

Objective. The objective of this experiment is to develop methods for aligning two representations of anatomy (the Foundational Model of Anatomy and GALEN) at the lexical and structural level.
Methods. The alignment consists of the following four steps: 1) acquiring terms, 2) identifying anchors (i.e., shared concepts) lexically, 3) acquiring explicit and implicit semantic relations, and 4) identifying anchors structurally.

In recent years many biomedical ontologies have been developed
and many of these ontologies contain overlapping information.
To be able to use multiple ontologies they have to be aligned. In this
paper we propose strategies for aligning ontologies based on life science
literature. We propose a basic algorithm as well as extensions that take
the structure of the ontologies into account. We evaluate the strategies
and compare them with strategies implemented in the alignment system
SAMBO. We also evaluate the combination of the proposed strategies
and the SAMBO strategies.

More and more ontologies are emerging across bioinformatics domains to represent and define domain knowledge, such as gene ontology, anatomy ontology and disease ontology. To integrate these heterogeneous ontologies is becoming critically important for applications utilizing multiple ontologies. Because the entities described in the ontology often overlap with other entities in other ontologies, a mapping between two corresponding terms is required and becoming the key to the integration of heterogeneous ontologies.