Pragmatic Ontology Evolution: Reconciling User Requirements and Application Performance

Increasingly, organizations are adopting ontologies to describe their large catalogues of items. These ontologies need to evolve regularly in response to changes in the domain and the emergence of new requirements. An important step of this process is the selection of candidate concepts to include in the new version of the ontology. This operation needs to take into account a variety of factors and in particular reconcile user requirements and application performance. Current ontology evolution methods focus either on ranking concepts according to their relevance or on preserving compatibility with existing applications. However, they do not take in consideration the impact of the ontology evolution process on the performance of computational tasks – e.g., in this work we focus on instance tagging, similarity computation, generation of recommendations, and data clustering. In this paper, we propose the Pragmatic Ontology Evolution (POE) framework, a novel approach for selecting from a group of candidates a set of concepts able to produce a new version of a given ontology that i) is consistent with the a set of user requirements (e.g., max number of concepts in the ontology), ii) is parametrised with respect to a number of dimensions (e.g., topological considerations), and iii) effectively supports relevant computational tasks. Our approach also supports users in navigating the space of possible solutions by showing how certain choices, such as limiting the number of concepts or privileging trendy concepts rather than historical ones, would reflect on the application performance. An evaluation of POE on the real-world scenario of the evolving Springer Nature taxonomy for editorial classification yielded excellent results, demonstrating a significant improvement over alternative approaches

Application of Semantics to Solve Problems in Life Sciences

Fecha de lectura de Tesis: 10 de diciembre de 2018La cantidad de información que se genera en la Web se ha incrementado en los últimos años. La mayor parte de esta información se encuentra accesible en texto, siendo el ser humano el principal usuario de la Web. Sin embargo, a pesar de todos los avances producidos en el área del procesamiento del lenguaje natural, los ordenadores tienen problemas para procesar esta información textual. En este cotexto, existen dominios de aplicación en los que se están publicando grandes cantidades de información disponible como datos estructurados como en el área de las Ciencias de la Vida. El análisis de estos datos es de vital importancia no sólo para el avance de la ciencia, sino para producir avances en el ámbito de la salud. Sin embargo, estos datos están localizados en diferentes repositorios y almacenados en diferentes formatos que hacen difícil su integración. En este contexto, el paradigma de los Datos Vinculados como una tecnología que incluye la aplicación de algunos estándares propuestos por la comunidad W3C tales como HTTP URIs, los estándares RDF y OWL. Haciendo uso de esta tecnología, se ha desarrollado esta tesis doctoral basada en cubrir los siguientes objetivos principales: 1) promover el uso de los datos vinculados por parte de la comunidad de usuarios del ámbito de las Ciencias de la Vida 2) facilitar el diseño de consultas SPARQL mediante el descubrimiento del modelo subyacente en los repositorios RDF 3) crear un entorno colaborativo que facilite el consumo de Datos Vinculados por usuarios finales, 4) desarrollar un algoritmo que, de forma automática, permita descubrir el modelo semántico en OWL de un repositorio RDF, 5) desarrollar una representación en OWL de ICD-10-CM llamada Dione que ofrezca una metodología automática para la clasificación de enfermedades de pacientes y su posterior validación haciendo uso de un razonador OWL

An Evolution-based Approach for Assessing Ontology Mappings - A Case Study in the Life Sciences

Ontology matching has been widely studied. However, the resulting on-tology mappings can be rather unstable when the participating ontologies or util-ized secondary sources (e.g., instance sources, thesauri) evolve. We propose an evolution-based approach for assessing ontology mappings by annotating their cor-respondences 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. The approach is generic in that it can be applied independently from the utilized match technique. We define different stability measures and show results of a first evaluation for the life science domain

GOMMA: a component-based infrastructure for managing and analyzing life science ontologies and their evolution

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>We present GOMMA, a generic infrastructure for managing and analyzing life science ontologies and their evolution. GOMMA utilizes a generic repository to uniformly and efficiently manage ontology versions and different kinds of mappings. Furthermore, it provides components for ontology matching, and determining evolutionary ontology changes. These components are used by analysis tools, such as the Ontology Evolution Explorer (OnEX) and the detection of unstable ontology regions. We introduce the component-based infrastructure and show analysis results for selected components and life science applications. GOMMA is available at <url>http://dbs.uni-leipzig.de/GOMMA</url>.</p> <p>Conclusions</p> <p>GOMMA provides a comprehensive and scalable infrastructure to manage large life science ontologies and analyze their evolution. Key functions include a generic storage of ontology versions and mappings, support for ontology matching and determining ontology changes. The supported features for analyzing ontology changes are helpful to assess their impact on ontology-dependent applications such as for term enrichment. GOMMA complements OnEX by providing functionalities to manage various versions of mappings between two ontologies and allows combining different match approaches.</p

XML Matchers: approaches and challenges

Schema Matching, i.e. the process of discovering semantic correspondences between concepts adopted in different data source schemas, has been a key topic in Database and Artificial Intelligence research areas for many years. In the past, it was largely investigated especially for classical database models (e.g., E/R schemas, relational databases, etc.). However, in the latest years, the widespread adoption of XML in the most disparate application fields pushed a growing number of researchers to design XML-specific Schema Matching approaches, called XML Matchers, aiming at finding semantic matchings between concepts defined in DTDs and XSDs. XML Matchers do not just take well-known techniques originally designed for other data models and apply them on DTDs/XSDs, but they exploit specific XML features (e.g., the hierarchical structure of a DTD/XSD) to improve the performance of the Schema Matching process. The design of XML Matchers is currently a well-established research area. The main goal of this paper is to provide a detailed description and classification of XML Matchers. We first describe to what extent the specificities of DTDs/XSDs impact on the Schema Matching task. Then we introduce a template, called XML Matcher Template, that describes the main components of an XML Matcher, their role and behavior. We illustrate how each of these components has been implemented in some popular XML Matchers. We consider our XML Matcher Template as the baseline for objectively comparing approaches that, at first glance, might appear as unrelated. The introduction of this template can be useful in the design of future XML Matchers. Finally, we analyze commercial tools implementing XML Matchers and introduce two challenging issues strictly related to this topic, namely XML source clustering and uncertainty management in XML Matchers.Comment: 34 pages, 8 tables, 7 figure

Surveying human habit modeling and mining techniques in smart spaces

A smart space is an environment, mainly equipped with Internet-of-Things (IoT) technologies, able to provide services to humans, helping them to perform daily tasks by monitoring the space and autonomously executing actions, giving suggestions and sending alarms. Approaches suggested in the literature may differ in terms of required facilities, possible applications, amount of human intervention required, ability to support multiple users at the same time adapting to changing needs. In this paper, we propose a Systematic Literature Review (SLR) that classifies most influential approaches in the area of smart spaces according to a set of dimensions identified by answering a set of research questions. These dimensions allow to choose a specific method or approach according to available sensors, amount of labeled data, need for visual analysis, requirements in terms of enactment and decision-making on the environment. Additionally, the paper identifies a set of challenges to be addressed by future research in the field

Visualization for biomedical ontologies alignment

Tese de mestrado, Bioinformática e Biologia Computacional (Bioinformática), Universidade de Lisboa, Faculdade de Ciências, 2016Desde o início do século, a investigação biomédica e a prática clínica levaram a uma acumulação de grandes quantidades de informação, por exemplo, os dados resultantes da sequenciação genómica ou os registos médicos. As ontologias fornecem um modelo estruturado com o intuito de representar o conhecimento e têm sido bem sucedidas no domínio biomédico na melhoria da interoperabilidade e partilha. O desenvolvimento desconectado das ontologias biomédicas levou à criação de modelos que apresentam domínios idênticos ou sobrepostos. As técnicas de emparelhamento de ontologias foram desenvolvidas afim de estabelecer ligações significativas entre as classes das ontologias, por outras palavras, para criar alinhamentos. Para alcançar um alinhamento ótimo é, não só importante melhorar as técnicas de emparelhamentos mas também criar as ferramentas necessárias para que possa existir intervenção humana, particularmente na visualização. Apesar da importância da intervenção de utilizadores e da visualização no emparelhamento de ontologias, poucos sistemas o suportam, sobretudo para grandes e complexas ontologias como as do domínio biomédico, concretamente no contexto da revisão de alinhamentos e interpretação de incoerências lógicas. O objetivo central desta tese consistiu na investigação dos principais paradigmas de visualização de ontologias, no contexto do alinhamento de ontologias biomédicas, e desenvolver abordagens de visualização e interação que vão de encontro a estes desafios. O trabalho desenvolvido levou, então, à criação de um novo módulo de visualização para um sistema de emparelhamento do state of the art que suporta a revisão de alinhamentos, e à construção de uma ferramenta online que visa ajudar o utilizador a compreender os conflitos encontrados nos alinhamentos, ambos baseados numa abordagem de visualização de subgrafos. Ambas as contribuições foram avaliadas em pequena escala, por testes a utilizadores que revelaram a relevância da visualização de subgrafos contra a visualização em árvore, mais comum no domínio biomédico.Since the begin of the century, biomedical research and clinical practice have resulted in the accumulation of very large amounts of information, e.g. data from genomic sequencing or medical records. Ontologies provide a structured model to represent knowledge and have been quite successful in the biomedical domain at improving interoperability and sharing. The disconnected development of biomedical ontologies has led to the creation of models that have overlapping or even equal domains. Ontology matching techniques were developed to establish meaningful connections between classes of the ontologies, in other words to create alignments. In order to achieve an optimal alignment, it is not only important to improve the matching techniques but also to create the necessary tools for human intervention, namely in visualization. Despite the importance of user intervention and visualization in ontology matching, few systems support these, especially for large and complex ontologies such as those in the biomedical domain, specifically in the context of the alignment revision and logical incoherence explanation. The central objective of this thesis was to investigate the main ontology visualization paradigms, in the context of biomedical ontology matching, and to develop visualization and interaction approaches addressing those challenges. The work developed lead to the creation of a new visualization module for a state of the art ontology matching system, that supports the alignment review, and to the construction of an online tool that aims to help the user understand the conflicts found in the alignments both based on a subgraph visualization approach. Both contributions were evaluated, in a small-scale, by user tests that revealed the relevance of subgraph visualization versus the more common tree visualization for the biomedical domain

Advanced Methods for Entity Linking in the Life Sciences

The amount of knowledge increases rapidly due to the increasing number of available data sources. However, the autonomy of data sources and the resulting heterogeneity prevent comprehensive data analysis and applications. Data integration aims to overcome heterogeneity by unifying different data sources and enriching unstructured data. The enrichment of data consists of different subtasks, amongst other the annotation process. The annotation process links document phrases to terms of a standardized vocabulary. Annotated documents enable effective retrieval methods, comparability of different documents, and comprehensive data analysis, such as finding adversarial drug effects based on patient data. A vocabulary allows the comparability using standardized terms. An ontology can also represent a vocabulary, whereas concepts, relationships, and logical constraints additionally define an ontology. The annotation process is applicable in different domains. Nevertheless, there is a difference between generic and specialized domains according to the annotation process. This thesis emphasizes the differences between the domains and addresses the identified challenges. The majority of annotation approaches focuses on the evaluation of general domains, such as Wikipedia. This thesis evaluates the developed annotation approaches with case report forms that are medical documents for examining clinical trials. The natural language provides different challenges, such as similar meanings using different phrases. The proposed annotation method, AnnoMap, considers the fuzziness of natural language. A further challenge is the reuse of verified annotations. Existing annotations represent knowledge that can be reused for further annotation processes. AnnoMap consists of a reuse strategy that utilizes verified annotations to link new documents to appropriate concepts. Due to the broad spectrum of areas in the biomedical domain, different tools exist. The tools perform differently regarding a particular domain. This thesis proposes a combination approach to unify results from different tools. The method utilizes existing tool results to build a classification model that can classify new annotations as correct or incorrect. The results show that the reuse and the machine learning-based combination improve the annotation quality compared to existing approaches focussing on the biomedical domain. A further part of data integration is entity resolution to build unified knowledge bases from different data sources. A data source consists of a set of records characterized by attributes. The goal of entity resolution is to identify records representing the same real-world entity. Many methods focus on linking data sources consisting of records being characterized by attributes. Nevertheless, only a few methods can handle graph-structured knowledge bases or consider temporal aspects. The temporal aspects are essential to identify the same entities over different time intervals since these aspects underlie certain conditions. Moreover, records can be related to other records so that a small graph structure exists for each record. These small graphs can be linked to each other if they represent the same. This thesis proposes an entity resolution approach for census data consisting of person records for different time intervals. The approach also considers the graph structure of persons given by family relationships. For achieving qualitative results, current methods apply machine-learning techniques to classify record pairs as the same entity. The classification task used a model that is generated by training data. In this case, the training data is a set of record pairs that are labeled as a duplicate or not. Nevertheless, the generation of training data is a time-consuming task so that active learning techniques are relevant for reducing the number of training examples. The entity resolution method for temporal graph-structured data shows an improvement compared to previous collective entity resolution approaches. The developed active learning approach achieves comparable results to supervised learning methods and outperforms other limited budget active learning methods. Besides the entity resolution approach, the thesis introduces the concept of evolution operators for communities. These operators can express the dynamics of communities and individuals. For instance, we can formulate that two communities merged or split over time. Moreover, the operators allow observing the history of individuals. Overall, the presented annotation approaches generate qualitative annotations for medical forms. The annotations enable comprehensive analysis across different data sources as well as accurate queries. The proposed entity resolution approaches improve existing ones so that they contribute to the generation of qualitative knowledge graphs and data analysis tasks