Owlifier: Creating OWL-DL ontologies from simple spreadsheet-based knowledge descriptions

Discovery and integration of data is important in many ecological studies, especially those that concern broad-scale ecological questions. Data discovery and integration are often difficult and time consuming tasks for researchers, which is due in part to the use of informal, ambiguous, and sometimes inconsistent terms for describing data content. Ontologies offer a solution to this problem by providing consistent definitions of ecological concepts that in turn can be used to annotate, relate, and search for data sets. However, unlike in molecular biology or biomedicine, few ontology development efforts exist within ecology. Ontology development often requires considerable expertise in ontology languages and development tools, which is often a barrier for ontology creation in ecology. In this paper we describe an approach for ontology creation that allows ecologists to use common spreadsheet tools to describe different aspects of an ontology. We present conventions for creating, relating, and constraining concepts through spreadsheets, and provide software tools for converting these ontologies into equivalent OWL-DL representations. We also consider inverse translations, i.e., to convert ontologies represented using OWL-DL into our spreadsheet format. Our approach allows large lists of terms to be easily related and organized into concept hierarchies, and generally provides a more intuitive and natural interface for ontology development by ecologists.     

Evaluating the Good Ontology Design Guideline (GoodOD) with the Ontology Quality Requirements and Evaluation Method and Metrics (OQuaRE)

Objective

To (1) evaluate the GoodOD guideline for ontology development by applying the OQuaRE evaluation method and metrics to the ontology artefacts that were produced by students in a randomized controlled trial, and (2) informally compare the OQuaRE evaluation method with gold standard and competency questions based evaluation methods, respectively.

Background

In the last decades many methods for ontology construction and ontology evaluation have been proposed. However, none of them has become a standard and there is no empirical evidence of comparative evaluation of such methods. This paper brings together GoodOD and OQuaRE. GoodOD is a guideline for developing robust ontologies. It was previously evaluated in a randomized controlled trial employing metrics based on gold standard ontologies and competency questions as outcome parameters. OQuaRE is a method for ontology quality evaluation which adapts the SQuaRE standard for software product quality to ontologies and has been successfully used for evaluating the quality of ontologies.

Methods

In this paper, we evaluate the effect of training in ontology construction based on the GoodOD guideline within the OQuaRE quality evaluation framework and compare the results with those obtained for the previous studies based on the same data.

Results

Our results show a significant effect of the GoodOD training over developed ontologies by topics: (a) a highly significant effect was detected in three topics from the analysis of the ontologies of untrained and trained students; (b) both positive and negative training effects with respect to the gold standard were found for five topics.

Conclusion

The GoodOD guideline had a significant effect over the quality of the ontologies developed. Our results show that GoodOD ontologies can be effectively evaluated using OQuaRE and that OQuaRE is able to provide additional useful information about the quality of the GoodOD ontologies.     

Text mining and ontologies in biomedicine: making sense of raw text

The volume of biomedical literature is increasing at such a rate that it is becoming difficult to locate, retrieve and manage the reported information without text mining, which aims to automatically distill information, extract facts, discover implicit links and generate hypotheses relevant to user needs. Ontologies, as conceptual models, provide the necessary framework for semantic representation of textual information. The principal link between text and an ontology is terminology, which maps terms to domain-specific concepts. This paper summarises different approaches in which ontologies have been used for text-mining applications in biomedicine.     

Benchmarking ontologies: bigger or better?

A scientific ontology is a formal representation of knowledge within a domain, typically including central concepts, their properties, and relations. With the rise of computers and high-throughput data collection, ontologies have become essential to data mining and sharing across communities in the biomedical sciences. Powerful approaches exist for testing the internal consistency of an ontology, but not for assessing the fidelity of its domain representation. We introduce a family of metrics that describe the breadth and depth with which an ontology represents its knowledge domain. We then test these metrics using (1) four of the most common medical ontologies with respect to a corpus of medical documents and (2) seven of the most popular English thesauri with respect to three corpora that sample language from medicine, news, and novels. Here we show that our approach captures the quality of ontological representation and guides efforts to narrow the breach between ontology and collective discourse within a domain. Our results also demonstrate key features of medical ontologies, English thesauri, and discourse from different domains. Medical ontologies have a small intersection, as do English thesauri. Moreover, dialects characteristic of distinct domains vary strikingly as many of the same words are used quite differently in medicine, news, and novels. As ontologies are intended to mirror the state of knowledge, our methods to tighten the fit between ontology and domain will increase their relevance for new areas of biomedical science and improve the accuracy and power of inferences computed across them.     

Ontologies for behavior

SUMMARY: Although dozens of biological ontologies have been created and deployed, relatively little attention has been given to using ontologies to represent behavior. Ontologies for two different behavior systems are described here. One ontology was a translation of a published ethogram, and the second was coded from video clips in a comparative study of jumping spider courtship. AVAILABILITY: http://mesquiteproject.org/ontology/.     

Ontology Alignment Repair through Modularization and Confidence-Based Heuristics

Ontology Matching aims at identifying a set of semantic correspondences, called an alignment, between related ontologies. In recent years, there has been a growing interest in efficient and effective matching methods for large ontologies. However, alignments produced for large ontologies are often logically incoherent. It was only recently that the use of repair techniques to improve the coherence of ontology alignments began to be explored. This paper presents a novel modularization technique for ontology alignment repair which extracts fragments of the input ontologies that only contain the necessary classes and relations to resolve all detectable incoherences. The paper presents also an alignment repair algorithm that uses a global repair strategy to minimize both the degree of incoherence and the number of mappings removed from the alignment, while overcoming the scalability problem by employing the proposed modularization technique. Our evaluation shows that our modularization technique produces significantly small fragments of the ontologies and that our repair algorithm produces more complete alignments than other current alignment repair systems, while obtaining an equivalent degree of incoherence. Additionally, we also present a variant of our repair algorithm that makes use of the confidence values of the mappings to improve alignment repair. Our repair algorithm was implemented as part of AgreementMakerLight, a free and open-source ontology matching system.     

Cells in experimental life sciences - challenges and solution to the rapid evolution of knowledge

Cell cultures used in biomedical experiments come in the form of both sample biopsy primary cells, and maintainable immortalised cell lineages. The rise of bioinformatics and high-throughput technologies has led us to the requirement of ontology representation of cell types and cell lines. The Cell Ontology (CL) and Cell Line Ontology (CLO) have long been established as reference ontologies in the OBO framework. We have compiled a series of the challenges and the proposals of solutions in this CELLS (Cells in ExperimentaL Life Sciences) thematic series that cover the grounds of standing issues and the directions, which were discussed in the First International Workshop on CELLS at the the International Conference on Biomedical Ontology (ICBO). This workshop focused on the extension of the current CL and CLO to cover a wider set of biological questions and challenges needing semantic infrastructure for information modeling. We discussed data-driven use cases that leverage linkage of CL, CLO and other bio-ontologies. This is an established approach in data-driven ontologies such as the Experimental Factor Ontology (EFO), and the Ontology for Biomedical Investigation (OBI). The First International Workshop on CELLS at the International Conference on Biomedical Ontology has brought together experimental biologists and biomedical ontologists to discuss solutions to organizing and representing the rapidly evolving knowledge gained from experimental cells. The workshop has successfully identified the areas of challenge, and the gap in connecting the two domains of knowledge. The outcome of this workshop yielded practical implementation plans to filled in this gap.This CELLS workshop also provided a venue for panel discussions of innovative solutions as well as challenges in the development and applications of biomedical ontologies to represent and analyze experimental cell data.     

The qualitative and time-dependent character of spatial relations in biomedical ontologies

MOTIVATION: The formal representation of mereological aspects of canonical anatomy (parthood relations) is relatively well understood. The formal representation of other aspects of canonical anatomy, such as connectedness and adjacency relations between anatomical parts, their shape and size as well as the spatial arrangement of anatomical parts within larger anatomical structures are, however, much less well understood and represented in existing computational anatomical and bio-medical ontologies only insufficiently. RESULTS: In this article, we provide a methodology of how to incorporate this kind of information into anatomical and bio-medical ontologies by applying techniques of representing qualitative spatial information from Artificial Intelligence. In particular, we focus on how to explicitly take into account the qualitative and time-dependent character of these relations. As a running example, we use the human temporomandibular joint (TMJ). AVAILABILITY: Using the presented methodology, a formal ontology was developed which is accessible on http://www.ifomis.org/bfo/fol. This ontology may help to improve the logical and ontological rigor of bio-medical ontologies such as the OBO relation ontology.     

Understanding and using the meaning of statements in a bio-ontology: recasting the Gene Ontology in OWL

   

The bio-ontology community falls into two camps: first we have biology domain experts, who actually hold the knowledge we wish to capture in ontologies; second, we have ontology specialists, who hold knowledge about techniques and best practice on ontology development. In the bio-ontology domain, these two camps have often come into conflict, especially where pragmatism comes into conflict with perceived best practice. One of these areas is the insistence of computer scientists on a well-defined semantic basis for the Knowledge Representation language being used. In this article, we will first describe why this community is so insistent. Second, we will illustrate this by examining the semantics of the Web Ontology Language and the semantics placed on the Directed Acyclic Graph as used by the Gene Ontology. Finally we will reconcile the two representations, including the broader Open Biomedical Ontologies format. The ability to exchange between the two representations means that we can capitalise on the features of both languages. Such utility can only arise by the understanding of the semantics of the languages being used. By this illustration of the usefulness of a clear, well-defined language semantics, we wish to promote a wider understanding of the computer science perspective amongst potential users within the biological community.     

OnEX: Exploring changes in life science ontologies

Background  

Numerous ontologies have recently been developed in life sciences to support a consistent annotation of biological objects, such as genes or proteins. These ontologies underlie continuous changes which can impact existing annotations. Therefore, it is valuable for users of ontologies to study the stability of ontologies and to see how many and what kind of ontology changes occurred.     

Semantic Similarity in Biomedical Ontologies

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. Semantic similarity has become a valuable tool for validating the results drawn from biomedical studies such as gene clustering, gene expression data analysis, prediction and validation of molecular interactions, and disease gene prioritization.     

Representing default knowledge in biomedical ontologies: application to the integration of anatomy and phenotype ontologies

Background  

Current efforts within the biomedical ontology community focus on achieving interoperability between various biomedical ontologies that cover a range of diverse domains. Achieving this interoperability will contribute to the creation of a rich knowledge base that can be used for querying, as well as generating and testing novel hypotheses. The OBO Foundry principles, as applied to a number of biomedical ontologies, are designed to facilitate this interoperability. However, semantic extensions are required to meet the OBO Foundry interoperability goals. Inconsistencies may arise when ontologies of properties – mostly phenotype ontologies – are combined with ontologies taking a canonical view of a domain – such as many anatomical ontologies. Currently, there is no support for a correct and consistent integration of such ontologies.     

The semantic metadatabase (SEMEDA): ontology based integration of federated molecular biological data sources

A system for "intelligent" semantic integration and querying of federated databases is being implemented by using three main components: A component which enables SQL access to integrated databases by database federation (MARGBench), an ontology based semantic metadatabase (SEMEDA) and an ontology based query interface (SEMEDA-query). In this publication we explain and demonstrate the principles, architecture and the use of SEMEDA. Since SEMEDA is implemented as 3 tiered web application database providers can enter all relevant semantic and technical information about their databases by themselves via a web browser. SEMEDA' s collaborative ontology editing feature is not restricted to database integration, and might also be useful for ongoing ontology developments, such as the "Gene Ontology" [2]. SEMEDA can be found at http://www-bm.cs.uni-magdeburg.de/semeda/. We explain how this ontologically structured information can be used for semantic database integration. In addition, requirements to ontologies for molecular biological database integration are discussed and relevant existing ontologies are evaluated. We further discuss how ontologies and structured knowledge sources can be used in SEMEDA and whether they can be merged supplemented or updated to meet the requirements for semantic database integration.     

Process attributes in bio-ontologies

ABSTRACT: BACKGROUND: Biomedical processes can provide essential information about the (mal-) functioning of an organism and are thus frequently represented in biomedical terminologies and ontologies, including the GO Biological Process branch. These processes often need to be described and categorised in terms of their attributes, such as rates or regularities. The adequate representation of such process attributes has been a contentious issue in bio-ontologies recently; and domain ontologies have correspondingly developed ad hoc workarounds that compromise interoperability and logical consistency. RESULTS: We present a design pattern for the representation of process attributes that is compatible with upper ontology frameworks such as BFO and BioTop. Our solution rests on two key tenets: firstly, that many of the sorts of process attributes which are biomedically interesting can be characterised by the ways that repeated parts of such processes constitute, in combination, an overall process; secondly, that entities for which a full logical definition can be assigned do not need to be treated as primitive within a formal ontology framework. We apply this approach to the challenge of modelling and automatically classifying examples of normal and abnormal rates and patterns of heart beating processes, and discuss the expressivity required in the underlying ontology representation language. We provide full definitions for process attributes at increasing levels of domain complexity. CONCLUSIONS: We show that a logical definition of process attributes is feasible, though limited by the expressivity of DL languages so that the creation of primitives is still necessary. This finding may endorse current formal upper-ontology frameworks as a way of ensuring consistency, interoperability and clarity.     

OMIT: Dynamic,Semi-Automated Ontology Development for the microRNA Domain

As a special class of short non-coding RNAs, microRNAs (a.k.a. miRNAs or miRs) have been reported to perform important roles in various biological processes by regulating respective target genes. However, significant barriers exist during biologists'' conventional miR knowledge discovery. Emerging semantic technologies, which are based upon domain ontologies, can render critical assistance to this problem. Our previous research has investigated the construction of a miR ontology, named Ontology for MIcroRNA Target Prediction (OMIT), the very first of its kind that formally encodes miR domain knowledge. Although it is unavoidable to have a manual component contributed by domain experts when building ontologies, many challenges have been identified for a completely manual development process. The most significant issue is that a manual development process is very labor-intensive and thus extremely expensive. Therefore, we propose in this paper an innovative ontology development methodology. Our contributions can be summarized as: (i) We have continued the development and critical improvement of OMIT, solidly based on our previous research outcomes. (ii) We have explored effective and efficient algorithms with which the ontology development can be seamlessly combined with machine intelligence and be accomplished in a semi-automated manner, thus significantly reducing large amounts of human efforts. A set of experiments have been conducted to thoroughly evaluate our proposed methodology.     

Large-scale biomedical ontology matching with ServOMap

The proliferation of biomedical applications, which rely on different knowledge organization systems, such as ontologies and thesauri raises the issue of the automated identification of the correspondences between these models, in particular for the data integration need. A significant effort has been conducted for tackling this issue of ontology alignment. However, few systems are able to deal with ontologies containing tens of thousands of entities, as it may be the case in the biomedical domain where resources such as SNOMED-CT, the FMA or the NCI thesaurus are commonly used. We present in this paper ServOMap, an efficient system for large-scale ontology alignment. It relies on an Ontology Server (ServO) and uses Information Retrieval techniques for computing similarity between entities. The system participated with two configurations in the 2012 Ontology Alignment Evaluation Initiative campaign. We report the very promising results obtained by the system for large biomedical ontologies alignment. ServOMap is freely available for download at http://code.google.com/p/servo/.     

Use artificial neural network to align biological ontologies

Background

Being formal, declarative knowledge representation models, ontologies help to address the problem of imprecise terminologies in biological and biomedical research. However, ontologies constructed under the auspices of the Open Biomedical Ontologies (OBO) group have exhibited a great deal of variety, because different parties can design ontologies according to their own conceptual views of the world. It is therefore becoming critical to align ontologies from different parties. During automated/semi-automated alignment across biological ontologies, different semantic aspects, i.e., concept name, concept properties, and concept relationships, contribute in different degrees to alignment results. Therefore, a vector of weights must be assigned to these semantic aspects. It is not trivial to determine what those weights should be, and current methodologies depend a lot on human heuristics.

Results

In this paper, we take an artificial neural network approach to learn and adjust these weights, and thereby support a new ontology alignment algorithm, customized for biological ontologies, with the purpose of avoiding some disadvantages in both rule-based and learning-based aligning algorithms. This approach has been evaluated by aligning two real-world biological ontologies, whose features include huge file size, very few instances, concept names in numerical strings, and others.

Conclusion

The promising experiment results verify our proposed hypothesis, i.e., three weights for semantic aspects learned from a subset of concepts are representative of all concepts in the same ontology. Therefore, our method represents a large leap forward towards automating biological ontology alignment.