Structural auditing methodologies for controlled terminologies

Several auditing methodologies for large controlled terminologies are developed. These are applied to the Unified Medical Language System XXXX and the National Cancer Institute Thesaurus (NCIT). Structural auditing methodologies are based on the structural aspects such as IS-A hierarchy relationships groups of concepts assigned to semantic types and groups of relationships defined for concepts. Structurally uniform groups of concepts tend to be semantically uniform. Structural auditing methodologies focus on concepts with unlikely or rare configuration. These concepts have a high likelihood for errors. One of the methodologies is based on comparing hierarchical relationships between the META and SN, two major knowledge sources of the UMLS. In general, a correspondence between them is expected since the SN hierarchical relationships should abstract the META hierarchical relationships. It may indicate an error when a mismatch occurs. The UMLS SN has 135 categories called semantic types. However, in spite of its medium size, the SN has limited use for comprehension purposes because it cannot be easily represented in a pictorial form, it has many (about 7,000) relationships. Therefore, a higher-level abstraction for the SN called a metaschema, is constructed. Its nodes are meta-semantic types, each representing a connected group of semantic types of the SN. One of the auditing methodologies is based on a kind of metaschema called a cohesive metaschema. The focus is placed on concepts of intersections of meta-semantic types. As is shown, such concepts have high likelihood for errors. Another auditing methodology is based on dividing the NCIT into areas according to the roles of its concepts. Moreover, each multi-rooted area is further divided into pareas that are singly rooted. Each p-area contains a group of structurally and semantically uniform concepts. These groups, as well as two derived abstraction networks called taxonomies, help in focusing on concepts with potential errors. With genomic research being at the forefront of bioscience, this auditing methodology is applied to the Gene hierarchy as well as the Biological Process hierarchy of the NCIT, since processes are very important for gene information. The results support the hypothesis that the occurrence of errors is related to the size of p-areas. Errors are more frequent for small p-areas

Knowledge-driven entity recognition and disambiguation in biomedical text

Entity recognition and disambiguation (ERD) for the biomedical domain are notoriously difficult problems due to the variety of entities and their often long names in many variations. Existing works focus heavily on the molecular level in two ways. First, they target scientific literature as the input text genre. Second, they target single, highly specialized entity types such as chemicals, genes, and proteins. However, a wealth of biomedical information is also buried in the vast universe of Web content. In order to fully utilize all the information available, there is a need to tap into Web content as an additional input. Moreover, there is a need to cater for other entity types such as symptoms and risk factors since Web content focuses on consumer health. The goal of this thesis is to investigate ERD methods that are applicable to all entity types in scientific literature as well as Web content. In addition, we focus on under-explored aspects of the biomedical ERD problems -- scalability, long noun phrases, and out-of-knowledge base (OOKB) entities. This thesis makes four main contributions, all of which leverage knowledge in UMLS (Unified Medical Language System), the largest and most authoritative knowledge base (KB) of the biomedical domain. The first contribution is a fast dictionary lookup method for entity recognition that maximizes throughput while balancing the loss of precision and recall. The second contribution is a semantic type classification method targeting common words in long noun phrases. We develop a custom set of semantic types to capture word usages; besides biomedical usage, these types also cope with non-biomedical usage and the case of generic, non-informative usage. The third contribution is a fast heuristics method for entity disambiguation in MEDLINE abstracts, again maximizing throughput but this time maintaining accuracy. The fourth contribution is a corpus-driven entity disambiguation method that addresses OOKB entities. The method first captures the entities expressed in a corpus as latent representations that comprise in-KB and OOKB entities alike before performing entity disambiguation.Die Erkennung und Disambiguierung von Entitäten für den biomedizinischen Bereich stellen, wegen der vielfältigen Arten von biomedizinischen Entitäten sowie deren oft langen und variantenreichen Namen, große Herausforderungen dar. Vorhergehende Arbeiten konzentrieren sich in zweierlei Hinsicht fast ausschließlich auf molekulare Entitäten. Erstens fokussieren sie sich auf wissenschaftliche Publikationen als Genre der Eingabetexte. Zweitens fokussieren sie sich auf einzelne, sehr spezialisierte Entitätstypen wie Chemikalien, Gene und Proteine. Allerdings bietet das Internet neben diesen Quellen eine Vielzahl an Inhalten biomedizinischen Wissens, das vernachlässigt wird. Um alle verfügbaren Informationen auszunutzen besteht der Bedarf weitere Internet-Inhalte als zusätzliche Quellen zu erschließen. Außerdem ist es auch erforderlich andere Entitätstypen wie Symptome und Risikofaktoren in Betracht zu ziehen, da diese für zahlreiche Inhalte im Internet, wie zum Beispiel Verbraucherinformationen im Gesundheitssektor, relevant sind. Das Ziel dieser Dissertation ist es, Methoden zur Erkennung und Disambiguierung von Entitäten zu erforschen, die alle Entitätstypen in Betracht ziehen und sowohl auf wissenschaftliche Publikationen als auch auf andere Internet-Inhalte anwendbar sind. Darüber hinaus setzen wir Schwerpunkte auf oft vernachlässigte Aspekte der biomedizinischen Erkennung und Disambiguierung von Entitäten, nämlich Skalierbarkeit, lange Nominalphrasen und fehlende Entitäten in einer Wissensbank. In dieser Hinsicht leistet diese Dissertation vier Hauptbeiträge, denen allen das Wissen von UMLS (Unified Medical Language System), der größten und wichtigsten Wissensbank im biomedizinischen Bereich, zu Grunde liegt. Der erste Beitrag ist eine schnelle Methode zur Erkennung von Entitäten mittels Lexikonabgleich, welche den Durchsatz maximiert und gleichzeitig den Verlust in Genauigkeit und Trefferquote (precision and recall) balanciert. Der zweite Beitrag ist eine Methode zur Klassifizierung der semantischen Typen von Nomen, die sich auf gebräuchliche Nomen von langen Nominalphrasen richtet und auf einer selbstentwickelten Sammlung von semantischen Typen beruht, die die Verwendung der Nomen erfasst. Neben biomedizinischen können diese Typen auch nicht-biomedizinische und allgemeine, informationsarme Verwendungen behandeln. Der dritte Beitrag ist eine schnelle Heuristikmethode zur Disambiguierung von Entitäten in MEDLINE Kurzfassungen, welche den Durchsatz maximiert, aber auch die Genauigkeit erhält. Der vierte Beitrag ist eine korpusgetriebene Methode zur Disambiguierung von Entitäten, die speziell fehlende Entitäten in einer Wissensbank behandelt. Die Methode wandelt erst die Entitäten, die in einem Textkorpus ausgedrückt aber nicht notwendigerweise in einer Wissensbank sind, in latente Darstellungen um und führt anschließend die Disambiguierung durch

Results of the Ontology Alignment Evaluation Initiative 2015

cheatham2016aInternational audienceOntology matching consists of finding correspondences between semantically related entities of two ontologies. OAEI campaigns aim at comparing ontology matching systems on precisely defined test cases. These test cases can use ontologies of different nature (from simple thesauri to expressive OWL ontologies) and use different modalities, e.g., blind evaluation, open evaluation and consensus. OAEI 2015 offered 8 tracks with 15 test cases followed by 22 participants. Since 2011, the campaign has been using a new evaluation modality which provides more automation to the evaluation. This paper is an overall presentation of the OAEI 2015 campaign

Uncertainty in Automated Ontology Matching: Lessons Learned from an Empirical Experimentation

Data integration is considered a classic research field and a pressing need within the information science community. Ontologies play a critical role in such a process by providing well-consolidated support to link and semantically integrate datasets via interoperability. This paper approaches data integration from an application perspective, looking at techniques based on ontology matching. An ontology-based process may only be considered adequate by assuming manual matching of different sources of information. However, since the approach becomes unrealistic once the system scales up, automation of the matching process becomes a compelling need. Therefore, we have conducted experiments on actual data with the support of existing tools for automatic ontology matching from the scientific community. Even considering a relatively simple case study (i.e., the spatio-temporal alignment of global indicators), outcomes clearly show significant uncertainty resulting from errors and inaccuracies along the automated matching process. More concretely, this paper aims to test on real-world data a bottom-up knowledge-building approach, discuss the lessons learned from the experimental results of the case study, and draw conclusions about uncertainty and uncertainty management in an automated ontology matching process. While the most common evaluation metrics clearly demonstrate the unreliability of fully automated matching solutions, properly designed semi-supervised approaches seem to be mature for a more generalized application

Context-Based Entity Matching for Big Data

In the Big Data era, where variety is the most dominant dimension, the RDF data model enables the creation and integration of actionable knowledge from heterogeneous data sources. However, the RDF data model allows for describing entities under various contexts, e.g., people can be described from its demographic context, but as well from their professional contexts. Context-aware description poses challenges during entity matching of RDF datasets—the match might not be valid in every context. To perform a contextually relevant entity matching, the specific context under which a data-driven task, e.g., data integration is performed, must be taken into account. However, existing approaches only consider inter-schema and properties mapping of different data sources and prevent users from selecting contexts and conditions during a data integration process. We devise COMET, an entity matching technique that relies on both the knowledge stated in RDF vocabularies and a context-based similarity metric to map contextually equivalent RDF graphs. COMET follows a two-fold approach to solve the problem of entity matching in RDF graphs in a context-aware manner. In the first step, COMET computes the similarity measures across RDF entities and resorts to the Formal Concept Analysis algorithm to map contextually equivalent RDF entities. Finally, COMET combines the results of the first step and executes a 1-1 perfect matching algorithm for matching RDF entities based on the combined scores. We empirically evaluate the performance of COMET on testbed from DBpedia. The experimental results suggest that COMET accurately matches equivalent RDF graphs in a context-dependent manner

Application of information extraction techniques to pharmacological domain : extracting drug-drug interactions

Una interacción farmacológica ocurre cuando los efectos de un fármaco se modifican por la presencia de otro. Las consecuencias pueden ser perjudiciales si la interacción causa un aumento de la toxicidad del fármaco o la disminución de su efecto, pudiendo provocar incluso la muerte del paciente en los peores casos. Las interacciones farmacológicas no sólo suponen un grave problema para la seguridad del paciente, sino que además también conllevan un importante incremento en el gasto médico. En la actualidad, el personal sanitario tiene a su disposición diversas bases de datos sobre interacciones que permiten evitar posibles interacciones a la hora de prescribir un determinado tratamiento, sin embargo, estas bases de datos no están completas. Por este motivo, médicos y farmacéuticos se ven obligados a revisar una gran cantidad de artículos científicos e informes sobre seguridad de medicamentos para estar al día de todo lo publicado en relación al tema. Desgraciadamente, el gran volumen de información al respecto hace que estos profesionales estén desbordados ante tal avalancha. El desarrollo de métodos automáticos que permitan recopilar, mantener e interpretar toda esta información es crucial a la hora de conseguir una mejora real en la detección temprana de las interacciones entre fármacos. Por tanto, la extracción de información podría reducir el tiempo empleado por el personal médico en la revisión de la literatura médica. Sin embargo, la extracción de interacciones farmacológicas a partir textos biomédicos no ha sido dirigida hasta el momento. Motivados por estos aspectos, en esta tesis hemos realizado un estudio detallado sobre diversas técnicas de extracción de información aplicadas al dominio farmacológico. Basándonos en este estudio, hemos propuesto dos aproximaciones distintas para la extracción de interacciones farmacológicas de los textos. Nuestra primera aproximación propone un enfoque híbrido, que combina análisis sintáctico superficial y la aplicación de patrones léxicos definidos por un farmacéutico. La segunda aproximación se aborda mediante aprendizaje supervisado, concretamente, el uso de métodos kernels. Además, se han desarrollado las siguientes tareas auxiliares: (1) el análisis de los textos utilizando la herramienta UMLS MetaMap Transfer (MMTx), que proporciona información sintáctica y semántica, (2) un proceso para identificar y clasificar los nombres de fármacos que ocurren en los textos, y (3) un proceso para reconoger las expresiones anafóricas que se refieren a fármacos. Un prototipo ha sido desarrollado para integrar y combinar las distintas técnicas propuestas en esta tesis. Para la evaluación de las dos propuestas, con la ayuda de un farmacéutico desarrollamos y anotamos un corpus con interacciones farmacológicas. El corpus DrugDDI es una de las principales aportaciones de la tesis, ya que es el primer corpus en el dominio biomédico anotado con este tipo de información y porque creemos que puede alentar la investigación sobre extracción de información en el dominio farmacológico. Los experimentos realizados demuestran que el enfoque basado en kernels consigue mejores resultados que los reportados por el enfoque que utiliza información sintáctica y patrones léxicos. Además, los kernels consiguen resultados comparables a los obtenidos en dominios similares como son las interacciones entre proteínas. Esta tesis se ha llevado a cabo en el marco del consorcio de investigación MAVIRCM (Mejorando el acceso y visibilidad de la información multilingüe en red para la Comunidad de Madrid, www.mavir.net) dentro del Programa de Actividades de I+D en Tecnologías 2005-2008 de la Comunidad de Madrid (S-0505/TIC-0267) así como en el proyecto de investigación BRAVO: ”Búsqueda de Respuestas Avanzada Multimodal y Multilingüe” (TIN2007-67407-C03-01).----------------------------------------------------------------------------------------A drug-drug interaction occurs when one drug influences the level or activity of another drug. The detection of drug interactions is an important research area in patient safety since these interactions can become very dangerous and increase health care costs. Although there are different databases supporting health care professionals in the detection of drug interactions, this kind of resource is rarely complete. Drug interactions are frequently reported in journals of clinical pharmacology, making medical literature the most effective source for the detection of drug interactions. However, the increasing volume of the literature overwhelms health care professionals trying to keep an up-to-date collection of all reported drug-drug interactions. The development of automatic methods for collecting, maintaining and interpreting this information is crucial for achieving a real improvement in their early detection. Information Extraction (IE) techniques can provide an interesting way of reducing the time spent by health care professionals on reviewing the literature. Nevertheless, no approach has been carried out to extract drug-drug interactions from biomedical texts. In this thesis, we have conducted a detailed study on various IE techniques applied to biomedical domain. Based on this study, we have proposed two different approximations for the extraction of drug-drug interactions from texts. The first approximation proposes a hybrid approach, which combines shallow parsing and pattern matching to extract relations between drugs from biomedical texts. The second approximation is based on a supervised machine learning approach, in particular, kernel methods. In addition, we have created and annotated the first corpus, DrugDDI, annotated with drug-drug interactions, which allow us to evaluate and compare both approximations. To the best of our knowledge, the DrugDDI corpus is the only available corpus annotated for drug-drug interactions and this thesis is the first work which addresses the problem of extracting drug-drug interactions from biomedical texts. We believe the DrugDDI corpus is an important contribution because it could encourage other research groups to research into this problem. We have also defined three auxiliary processes to provide crucial information, which will be used by the aforementioned approximations. These auxiliary tasks are as follows: (1) a process for text analysis based on the UMLS MetaMap Transfer tool (MMTx) to provide shallow syntactic and semantic information from texts, (2) a process for drug name recognition and classification, and (3) a process for drug anaphora resolution. Finally, we have developed a pipeline prototype which integrates the different auxiliary processes. The pipeline architecture allows us to easily integrate these modules with each of the approaches proposed in this thesis: pattern-matching or kernels. Several experiments were performed on the DrugDDI corpus. They show that while the first approximation based on pattern matching achieves low performance, the approach based on kernel-methods achieves a performance comparable to those obtained by approaches which carry out a similar task such as the extraction of protein-protein interactions. This work has been partially supported by the Spanish research projects: MAVIR consortium (S-0505/TIC-0267, www.mavir.net), a network of excellence funded by the Madrid Regional Government and TIN2007-67407-C03-01 (BRAVO: Advanced Multimodal and Multilingual Question Answering)

Grid-based semantic integration of heterogeneous data resources: Implementation on a HealthGrid

This thesis was submitted for the degree of Doctor of Philosophy and was awarded by Brunel University.The semantic integration of geographically distributed and heterogeneous data resources still remains a key challenge in Grid infrastructures. Today's mainstream Grid technologies hold the promise to meet this challenge in a systematic manner, making data applications more scalable and manageable. The thesis conducts a thorough investigation of the problem, the state of the art, and the related technologies, and proposes an Architecture for Semantic Integration of Data Sources (ASIDS) addressing the semantic heterogeneity issue. It defines a simple mechanism for the interoperability of heterogeneous data sources in order to extract or discover information regardless of their different semantics. The constituent technologies of this architecture include Globus Toolkit (GT4) and OGSA-DAI (Open Grid Service Architecture Data Integration and Access) alongside other web services technologies such as XML (Extensive Markup Language). To show this, the ASIDS architecture was implemented and tested in a realistic setting by building an exemplar application prototype on a HealthGrid (pilot implementation). The study followed an empirical research methodology and was informed by extensive literature surveys and a critical analysis of the relevant technologies and their synergies. The two literature reviews, together with the analysis of the technology background, have provided a good overview of the current Grid and HealthGrid landscape, produced some valuable taxonomies, explored new paths by integrating technologies, and more importantly illuminated the problem and guided the research process towards a promising solution. Yet the primary contribution of this research is an approach that uses contemporary Grid technologies for integrating heterogeneous data resources that have semantically different. data fields (attributes). It has been practically demonstrated (using a prototype HealthGrid) that discovery in semantically integrated distributed data sources can be feasible by using mainstream Grid technologies, which have been shown to have some Significant advantages over non-Grid based approaches