Ontology mapping for the laboratory analytics domain

The Pistoia Alliance Ontologies Mapping Project has applied the Ontology Matching algorithm, Paxo to the laboratory analytics domain. Nine ontologies relevant to laboratory analytics were selected. Among all possible pair combinations, thirteen of Paxo’s computed mapping sets were selected for evaluation through comparison with a silver standard alignment generated from the consensus votes of a panel of systems participating in the OAEI campaign

Map Conflation using Piecewise Linear Rubber-Sheeting Transformation between Layout and As-Built Plans in Kumasi Metropolis.

Context and backgroundAccurately integrating different geospatial data sets remain a challenging task because diverse geospatial data may have different accuracy levels and formats. Surveyors may typically create several arbitrary coordinate systems at local scales, which could lead to a variety of coordinate datasets causing such data to remain unconsolidated and in-homogeneous.Methodology:In this study, a piecewise rubber-sheeting conflation or geometric correction approach is used to accomplish transformations between such a pair of data for accurate data integration. Rubber-sheeting or piecewise linear homeomorphism is necessary because the different plans’ data would rarely match up correctly due to various reasons, such as the method of setting out from the design to the ground situation, and/or the non-accommodation of existing developments in the design.  Results:The conflation in ArcGIS using rubber sheet transformation achieved integration to a mean displacement error of 1.58 feet (0.48 meters.) from an initial mean displacement error of 71.46 feet (21.78 meters) an improvement of almost 98%. It is recommended that the rubber sheet technique gave a near exact point matching transformation and could be used to integrate zone plans with As-built surveys to address the challenges in correcting zonal plans in land records.  It is further recommended to investigate the incorporation of the use of textual information recognition and address geocoding to enable the use of on-site road names and plot numbers to detect points for matching

Ontology mapping for semantically enabled applications

In this review, we provide a summary of recent progress in ontology mapping (OM) at a crucial time when biomedical research is under a deluge of an increasing amount and variety of data. This is particularly important for realising the full potential of semantically enabled or enriched applications and for meaningful insights, such as drug discovery, using machine-learning technologies. We discuss challenges and solutions for better ontology mappings, as well as how to select ontologies before their application. In addition, we describe tools and algorithms for ontology mapping, including evaluation of tool capability and quality of mappings. Finally, we outline the requirements for an ontology mapping service (OMS) and the progress being made towards implementation of such sustainable services

Enhancing the FAIRness of Arctic Research Data Through Semantic Annotation

The National Science Foundation’s Arctic Data Center is the primary data repository for NSF-funded research conducted in the Arctic. There are major challenges in discovering and interpreting resources in a repository containing data as heterogeneous and interdisciplinary as those in the Arctic Data Center. This paper reports on advances in cyberinfrastructure at the Arctic Data Center that help address these issues by leveraging semantic technologies that enhance the repository’s adherence to the FAIR data principles and improve the Findability, Accessibility, Interoperability, and Reusability of digital resources in the repository. We describe the Arctic Data Center’s improvements. We use semantic annotation to bind metadata about Arctic data sets with concepts in web-accessible ontologies. The Arctic Data Center’s implementation of a semantic annotation mechanism is accompanied by the development of an extended search interface that increases the findability of data by allowing users to search for specific, broader, and narrower meanings of measurement descriptions, as well as through their potential synonyms. Based on research carried out by the DataONE project, we evaluated the potential impact of this approach, regarding the accessibility, interoperability, and reusability of measurement data. Arctic research often benefits from having additional data, typically from multiple, heterogeneous sources, that complement and extend the bases – spatially, temporally, or thematically – for understanding Arctic phenomena. These relevant data resources must be ‘found’, and ‘harmonized’ prior to integration and analysis. The findings of a case study indicated that the semantic annotation of measurement data enhances the capabilities of researchers to accomplish these tasks

Investigating semantic similarity for biomedical ontology alignment

Tese de mestrado, Bioinformática e Biologia Computacional (Bioinformática) Universidade de Lisboa, Faculdade de Ciências, 2017A heterogeneidade dos dados biomédicos e o crescimento exponencial da informação dentro desse domínio tem levado à utilização de ontologias, que codificam o conhecimento de forma computacionalmente tratável. O desenvolvimento de uma ontologia decorre, em geral, com base nos requisitos da equipa que a desenvolve, podendo levar à criação de ontologias diferentes e potencialmente incompatíveis por várias equipas de investigação. Isto implica que as várias ontologias existentes para codificar conhecimento biomédico possam, entre elas, sofrer de heterogeneidade: mesmo quando o domínio por elas codificado é idêntico, os conceitos podem ser representados de formas diferentes, com diferente especificidade e/ou granularidade. Para minimizar estas diferenças e criar representações mais standard e aceites pela comunidade, foram desenvolvidos algoritmos (matchers) que encontrassem pontes de conhecimento (mappings) entre as ontologias de forma a alinharem-nas. O tipo de algoritmos mais utilizados no Alinhamento de Ontologias (AO) são os que utilizam a informação léxica (isto é, os nomes, sinónimos e descrições dos conceitos) para calcular as semelhanças entre os conceitos a serem mapeados. Uma abordagem complementar a esses algoritmos é a utilização de Background Knowledge (BK) como forma de aumentar o número de sinónimos usados e assim aumentar a cobertura do alinhamento produzido. Uma alternativa aos algoritmos léxicos são os algoritmos estruturais que partem do pressuposto que as ontologias foram desenvolvidas com pontos de vista semelhantes – realidade pouco comum. Surge então o tema desta dissertação onde toma-se partido da Semelhança Semântica (SS) para o desenvolvimento de novos algoritmos de AO. É de salientar que até ao momento a utilização de SS no Alinhamento de Ontologias é cingida à verificação de mappings e não à sua procura. Esta dissertação apresenta o desenvolvimento, implementação e avaliação de dois algoritmos que utilizam SS, ambos usados como forma de estender alinhamentos produzidos previamente, um para encontrar mappings de equivalências e o outro de subsunção (onde um conceito de uma ontologia é mapeado como sendo descendente do conceito proveniente de outra ontologia). Os algoritmos propostos foram implementados no AML que é um sistema topo de gama em Alinhamento de Ontologias. O algoritmo de equivalência demonstrou uma melhoria de até 0.2% em termos de F-measure em comparação com o alinhamento âncora utilizado; e um aumento de até 11.3% quando comparado a outro sistema topo de gama (LogMapLt) que não utiliza BK. É importante referir que, dentro do espaço de procura do algoritmo o Recall variou entre 66.7% e 100%. Já o algoritmo de subsunção apresentou precisão entre 75.9% e 95% (avaliado manualmente).The heterogeneity of biomedical data and the exponential growth of the information within this domain has led to the usage of ontologies, which encode knowledge in a computationally tractable way. Usually, the ontology’s development is based on the requirements of the research team, which means that ontologies of the same domain can be different and potentially incompatible among several research teams. This fact implies that the various existing ontologies encoding biomedical knowledge can, among them, suffer from heterogeneity: even when the encoded domain is identical, the concepts may be represented in different ways, with different specificity and/or granularity. To minimize these differences and to create representations that are more standard and accepted by the community, algorithms (known as matchers) were developed to search for bridges of knowledge (known as mappings) between the ontologies, in order to align them. The most commonly used type of matchers in Ontology Matching (OM) are the ones taking advantage of the lexical information (names, synonyms and textual description of the concepts) to calculate the similarities between the concepts to be mapped. A complementary approach to those algorithms is the usage of Background Knowledge (BK) as a way to increase the number of synonyms used, and further increase of the coverage of the produced alignment. An alternative to lexical algorithms are the structural ones which assume that the ontologies were developed with similar points of view - an unusual reality. The theme of this dissertation is to take advantage of Semantic Similarity (SS) for the development of new OM algorithms. It is important to emphasize that the use of SS in Ontology Alignment has, until now, been limited to the verification of mappings and not to its search. This dissertation presents the development, implementation, and evaluation of two algorithms that use SS. Both algorithms were used to extend previously produced alignments, one to search for equivalence and the other for subsumption mappings (where a concept of an ontology is mapped as descendant from a concept from another ontology). The proposed algorithms were implemented in AML, which is a top performing system in Ontology Matching. The equivalence algorithm showed an improvement in F-measure up to 0.2% when compared to the anchor alignment; and an increase of up to 11.3% when compared to another high-end system (LogMapLt) which lacks the usage of BK. It is important to note that, within the search space of the algorithm, the Recall ranged from 66.7% to 100%. On the other hand, the subsumption algorithm presented an accuracy between 75.9% and 95% (manually evaluated)

Local matching learning of large scale biomedical ontologies

Les larges ontologies biomédicales décrivent généralement le même domaine d'intérêt, mais en utilisant des modèles de modélisation et des vocabulaires différents. Aligner ces ontologies qui sont complexes et hétérogènes est une tâche fastidieuse. Les systèmes de matching doivent fournir des résultats de haute qualité en tenant compte de la grande taille de ces ressources. Les systèmes de matching d'ontologies doivent résoudre deux problèmes: (i) intégrer la grande taille d'ontologies, (ii) automatiser le processus d'alignement. Le matching d'ontologies est une tâche difficile en raison de la large taille des ontologies. Les systèmes de matching d'ontologies combinent différents types de matcher pour résoudre ces problèmes. Les principaux problèmes de l'alignement de larges ontologies biomédicales sont: l'hétérogénéité conceptuelle, l'espace de recherche élevé et la qualité réduite des alignements résultants. Les systèmes d'alignement d'ontologies combinent différents matchers afin de réduire l'hétérogénéité. Cette combinaison devrait définir le choix des matchers à combiner et le poids. Différents matchers traitent différents types d'hétérogénéité. Par conséquent, le paramétrage d'un matcher devrait être automatisé par les systèmes d'alignement d'ontologies afin d'obtenir une bonne qualité de correspondance. Nous avons proposé une approche appele "local matching learning" pour faire face à la fois à la grande taille des ontologies et au problème de l'automatisation. Nous divisons un gros problème d'alignement en un ensemble de problèmes d'alignement locaux plus petits. Chaque problème d'alignement local est indépendamment aligné par une approche d'apprentissage automatique. Nous réduisons l'énorme espace de recherche en un ensemble de taches de recherche de corresondances locales plus petites. Nous pouvons aligner efficacement chaque tache de recherche de corresondances locale pour obtenir une meilleure qualité de correspondance. Notre approche de partitionnement se base sur une nouvelle stratégie à découpes multiples générant des partitions non volumineuses et non isolées. Par conséquence, nous pouvons surmonter le problème de l'hétérogénéité conceptuelle. Le nouvel algorithme de partitionnement est basé sur le clustering hiérarchique par agglomération (CHA). Cette approche génère un ensemble de tâches de correspondance locale avec un taux de couverture suffisant avec aucune partition isolée. Chaque tâche d'alignement local est automatiquement alignée en se basant sur les techniques d'apprentissage automatique. Un classificateur local aligne une seule tâche d'alignement local. Les classificateurs locaux sont basés sur des features élémentaires et structurelles. L'attribut class de chaque set de donne d'apprentissage " training set" est automatiquement étiqueté à l'aide d'une base de connaissances externe. Nous avons appliqué une technique de sélection de features pour chaque classificateur local afin de sélectionner les matchers appropriés pour chaque tâche d'alignement local. Cette approche réduit la complexité d'alignement et augmente la précision globale par rapport aux méthodes d'apprentissage traditionnelles. Nous avons prouvé que l'approche de partitionnement est meilleure que les approches actuelles en terme de précision, de taux de couverture et d'absence de partitions isolées. Nous avons évalué l'approche d'apprentissage d'alignement local à l'aide de diverses expériences basées sur des jeux de données d'OAEI 2018. Nous avons déduit qu'il est avantageux de diviser une grande tâche d'alignement d'ontologies en un ensemble de tâches d'alignement locaux. L'espace de recherche est réduit, ce qui réduit le nombre de faux négatifs et de faux positifs. L'application de techniques de sélection de caractéristiques à chaque classificateur local augmente la valeur de rappel pour chaque tâche d'alignement local.Although a considerable body of research work has addressed the problem of ontology matching, few studies have tackled the large ontologies used in the biomedical domain. We introduce a fully automated local matching learning approach that breaks down a large ontology matching task into a set of independent local sub-matching tasks. This approach integrates a novel partitioning algorithm as well as a set of matching learning techniques. The partitioning method is based on hierarchical clustering and does not generate isolated partitions. The matching learning approach employs different techniques: (i) local matching tasks are independently and automatically aligned using their local classifiers, which are based on local training sets built from element level and structure level features, (ii) resampling techniques are used to balance each local training set, and (iii) feature selection techniques are used to automatically select the appropriate tuning parameters for each local matching context. Our local matching learning approach generates a set of combined alignments from each local matching task, and experiments show that a multiple local classifier approach outperforms conventional, state-of-the-art approaches: these use a single classifier for the whole ontology matching task. In addition, focusing on context-aware local training sets based on local feature selection and resampling techniques significantly enhances the obtained results

Systematic Analysis of the Factors Contributing to the Variation and Change of the Microbiome

abstract: Understanding changes and trends in biomedical knowledge is crucial for individuals, groups, and institutions as biomedicine improves people’s lives, supports national economies, and facilitates innovation. However, as knowledge changes what evidence illustrates knowledge changes? In the case of microbiome, a multi-dimensional concept from biomedicine, there are significant increases in publications, citations, funding, collaborations, and other explanatory variables or contextual factors. What is observed in the microbiome, or any historical evolution of a scientific field or scientific knowledge, is that these changes are related to changes in knowledge, but what is not understood is how to measure and track changes in knowledge. This investigation highlights how contextual factors from the language and social context of the microbiome are related to changes in the usage, meaning, and scientific knowledge on the microbiome. Two interconnected studies integrating qualitative and quantitative evidence examine the variation and change of the microbiome evidence are presented. First, the concepts microbiome, metagenome, and metabolome are compared to determine the boundaries of the microbiome concept in relation to other concepts where the conceptual boundaries have been cited as overlapping. A collection of publications for each concept or corpus is presented, with a focus on how to create, collect, curate, and analyze large data collections. This study concludes with suggestions on how to analyze biomedical concepts using a hybrid approach that combines results from the larger language context and individual words. Second, the results of a systematic review that describes the variation and change of microbiome research, funding, and knowledge are examined. A corpus of approximately 28,000 articles on the microbiome are characterized, and a spectrum of microbiome interpretations are suggested based on differences related to context. The collective results suggest the microbiome is a separate concept from the metagenome and metabolome, and the variation and change to the microbiome concept was influenced by contextual factors. These results provide insight into how concepts with extensive resources behave within biomedicine and suggest the microbiome is possibly representative of conceptual change or a preview of new dynamics within science that are expected in the future.Dissertation/ThesisDoctoral Dissertation Biology 201

Tackling the challenges of matching biomedical ontologies

Abstract Background Biomedical ontologies pose several challenges to ontology matching due both to the complexity of the biomedical domain and to the characteristics of the ontologies themselves. The biomedical tracks in the Ontology Matching Evaluation Initiative (OAEI) have spurred the development of matching systems able to tackle these challenges, and benchmarked their general performance. In this study, we dissect the strategies employed by matching systems to tackle the challenges of matching biomedical ontologies and gauge the impact of the challenges themselves on matching performance, using the AgreementMakerLight (AML) system as the platform for this study. Results We demonstrate that the linear complexity of the hash-based searching strategy implemented by most state-of-the-art ontology matching systems is essential for matching large biomedical ontologies efficiently. We show that accounting for all lexical annotations (e.g., labels and synonyms) in biomedical ontologies leads to a substantial improvement in F-measure over using only the primary name, and that accounting for the reliability of different types of annotations generally also leads to a marked improvement. Finally, we show that cross-references are a reliable source of information and that, when using biomedical ontologies as background knowledge, it is generally more reliable to use them as mediators than to perform lexical expansion. Conclusions We anticipate that translating traditional matching algorithms to the hash-based searching paradigm will be a critical direction for the future development of the field. Improving the evaluation carried out in the biomedical tracks of the OAEI will also be important, as without proper reference alignments there is only so much that can be ascertained about matching systems or strategies. Nevertheless, it is clear that, to tackle the various challenges posed by biomedical ontologies, ontology matching systems must be able to efficiently combine multiple strategies into a mature matching approach

Additional file 1 of Tackling the challenges of matching biomedical ontologies

Manual evaluation of the HP-MP mappings found through logical definitions Tab-separated text file listing each mapping (URI and label of the source and target classes) and its classification. (TSV 13 kb