Building a biomedical ontology recommender web service

<p>Abstract</p> <p>Background</p> <p>Researchers in biomedical informatics use ontologies and terminologies to annotate their data in order to facilitate data integration and translational discoveries. As the use of ontologies for annotation of biomedical datasets has risen, a common challenge is to identify ontologies that are best suited to annotating specific datasets. The number and variety of biomedical ontologies is large, and it is cumbersome for a researcher to figure out which ontology to use.</p> <p>Methods</p> <p>We present the <it>Biomedical Ontology Recommender web service</it>. The system uses textual metadata or a set of keywords describing a domain of interest and suggests appropriate ontologies for annotating or representing the data. The service makes a decision based on three criteria. The first one is <it>coverage</it>, or the ontologies that provide most terms covering the input text. The second is <it>connectivity</it>, or the ontologies that are most often mapped to by other ontologies. The final criterion is <it>size</it>, or the number of concepts in the ontologies. The service scores the ontologies as a function of scores of the annotations created using the National Center for Biomedical Ontology (NCBO) <it>Annotator web service</it>. We used all the ontologies from the UMLS Metathesaurus and the NCBO BioPortal.</p> <p>Results</p> <p>We compare and contrast our Recommender by an exhaustive functional comparison to previously published efforts. We evaluate and discuss the results of several recommendation heuristics in the context of three real world use cases. The best recommendations heuristics, rated ‘very relevant’ by expert evaluators, are the ones based on coverage and connectivity criteria. The Recommender service (alpha version) is available to the community and is embedded into BioPortal.</p

NCBO Ontology Recommender 2.0: An Enhanced Approach for Biomedical Ontology Recommendation

Biomedical researchers use ontologies to annotate their data with ontology terms, enabling better data integration and interoperability. However, the number, variety and complexity of current biomedical ontologies make it cumbersome for researchers to determine which ones to reuse for their specific needs. To overcome this problem, in 2010 the National Center for Biomedical Ontology (NCBO) released the Ontology Recommender, which is a service that receives a biomedical text corpus or a list of keywords and suggests ontologies appropriate for referencing the indicated terms. We developed a new version of the NCBO Ontology Recommender. Called Ontology Recommender 2.0, it uses a new recommendation approach that evaluates the relevance of an ontology to biomedical text data according to four criteria: (1) the extent to which the ontology covers the input data; (2) the acceptance of the ontology in the biomedical community; (3) the level of detail of the ontology classes that cover the input data; and (4) the specialization of the ontology to the domain of the input data. Our evaluation shows that the enhanced recommender provides higher quality suggestions than the original approach, providing better coverage of the input data, more detailed information about their concepts, increased specialization for the domain of the input data, and greater acceptance and use in the community. In addition, it provides users with more explanatory information, along with suggestions of not only individual ontologies but also groups of ontologies. It also can be customized to fit the needs of different scenarios. Ontology Recommender 2.0 combines the strengths of its predecessor with a range of adjustments and new features that improve its reliability and usefulness. Ontology Recommender 2.0 recommends over 500 biomedical ontologies from the NCBO BioPortal platform, where it is openly available.Comment: 29 pages, 8 figures, 11 table

BiOSS: A system for biomedical ontology selection

In biomedical informatics, ontologies are considered a key technology for annotating, retrieving and sharing the huge volume of publicly available data. Due to the increasing amount, complexity and variety of existing biomedical ontologies, choosing the ones to be used in a semantic annotation problem or to design a specific application is a difficult task. As a consequence, the design of approaches and tools addressed to facilitate the selection of biomedical ontologies is becoming a priority. In this paper we present BiOSS, a novel system for the selection of biomedical ontologies. BiOSS evaluates the adequacy of an ontology to a given domain according to three different criteria: (1) the extent to which the ontology covers the domain; (2) the semantic richness of the ontology in the domain; (3) the popularity of the ontology in the biomedical community. BiOSS has been applied to 5 representative problems of ontology selection. It also has been compared to existing methods and tools. Results are promising and show the usefulness of BiOSS to solve real-world ontology selection problems. BiOSS is openly available both as a web tool and a web service.Instituto de Salud Carlos III; FIS-PI10/02180Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; CN2012/217Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; CN2011/034Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; CN2012/211Programa Iberoamericano de Ciencia y Tecnología para el Desarrollo; ref. 209RT036

A methodology for biomedical ontology reuse

The abundance of biomedical ontologies is beneficial to the development of biomedical related systems. However, existing biomedical ontologies such as the National Cancer Institute Thesaurus (NCIT), Foundational Model of Anatomy (FMA) and Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT) are often too large to be implemented in a particular system and cause unnecessary high usage of memory and slow down the system’s processing time. Developing a new ontology from scratch just for the use of a particular system is deemed as inefficient since it requires additional time and causes redundancy. Thus, a potentially better method is by reusing existing ontologies. However, currently there are no specific methods or tools for reusing ontologies. This paper aims to provide readers with a step by step method in reusing ontologies together with the tools that can be used to ease the process

Expertise Profiling in Evolving Knowledgecuration Platforms

Expertise modeling has been the subject of extensiveresearch in two main disciplines: Information Retrieval (IR) andSocial Network Analysis (SNA). Both IR and SNA approachesbuild the expertise model through a document-centric approachproviding a macro-perspective on the knowledge emerging fromlarge corpus of static documents. With the emergence of the Webof Data there has been a significant shift from static to evolvingdocuments, through micro-contributions. Thus, the existingmacro-perspective is no longer sufficient to track the evolution ofboth knowledge and expertise. In this paper we present acomprehensive, domain-agnostic model for expertise profiling inthe context of dynamic, living documents and evolving knowledgebases. We showcase its application in the biomedical domain andanalyze its performance using two manually created datasets

The evaluation of ontologies: quality, reuse and social factors

Finding a “good” or the “right” ontology is a growing challenge in the ontology domain, where one of the main aims is to share and reuse existing semantics and knowledge. Before reusing an ontology, knowledge engineers not only have to find a set of appropriate ontologies for their search query, but they should also be able to evaluate those ontologies according to different internal and external criteria. Therefore, ontology evaluation is at the heart of ontology selection and has received a considerable amount of attention in the literature.Despite the importance of ontology evaluation and selection and the widespread research on these topics, there are still many unanswered questions and challenges when it comes to evaluating and selecting ontologies for reuse. Most of the evaluation metrics and frameworks in the literature are mainly based on a limited set of internal characteristics, e.g., content and structure of ontologies and ignore how they are used and evaluated by communities. This thesis aimed to investigate the notion of quality and reusability in the ontology domain and to explore and identify the set of metrics that can affect the process of ontology evaluation and selection for reuse. [Continues.

ONTOLOGY BASED TECHNICAL SKILL SIMILARITY

Online job boards have become a major platform for technical talent procurement and job search. These job portals have given rise to challenging matching and search problems. The core matching or search happens between technical skills of the job requirements and the candidate\u27s profile or keywords. The extensive list of technical skills and its polyonymous nature makes it less effective to perform a direct keyword matching. This results in substandard job matching or search results which misses out a closely matching candidate on account of it not having the exact skills. It is important to use a semantic similarity measure between skills to improve the relevance of the results. This paper proposes a semantic similarity measure between technical skills using a knowledge based approach. The approach builds an ontology using DBpedia and uses it to derive a similarity score. Feature based ontology similarity measures are used to derive a similarity score between two skills. The ontology also helps in resolving a base skill from its multiple representations. The paper discusses implementation of custom ontology, similarity measuring system and performance of the system in comparing technical skills. The proposed approach performs better than the Resumatcher system in finding the similarity between skills. Keywords