A semantic and agent-based approach to support information retrieval, interoperability and multi-lateral viewpoints for heterogeneous environmental databases

PhDData stored in individual autonomous databases often needs to be combined and interrelated. For example, in the Inland Water (IW) environment monitoring domain, the spatial and temporal variation of measurements of different water quality indicators stored in different databases are of interest. Data from multiple data sources is more complex to combine when there is a lack of metadata in a computation forin and when the syntax and semantics of the stored data models are heterogeneous. The main types of information retrieval (IR) requirements are query transparency and data harmonisation for data interoperability and support for multiple user views. A combined Semantic Web based and Agent based distributed system framework has been developed to support the above IR requirements. It has been implemented using the Jena ontology and JADE agent toolkits. The semantic part supports the interoperability of autonomous data sources by merging their intensional data, using a Global-As-View or GAV approach, into a global semantic model, represented in DAML+OIL and in OWL. This is used to mediate between different local database views. The agent part provides the semantic services to import, align and parse semantic metadata instances, to support data mediation and to reason about data mappings during alignment. The framework has applied to support information retrieval, interoperability and multi-lateral viewpoints for four European environmental agency databases. An extended GAV approach has been developed and applied to handle queries that can be reformulated over multiple user views of the stored data. This allows users to retrieve data in a conceptualisation that is better suited to them rather than to have to understand the entire detailed global view conceptualisation. User viewpoints are derived from the global ontology or existing viewpoints of it. This has the advantage that it reduces the number of potential conceptualisations and their associated mappings to be more computationally manageable. Whereas an ad hoc framework based upon conventional distributed programming language and a rule framework could be used to support user views and adaptation to user views, a more formal framework has the benefit in that it can support reasoning about the consistency, equivalence, containment and conflict resolution when traversing data models. A preliminary formulation of the formal model has been undertaken and is based upon extending a Datalog type algebra with hierarchical, attribute and instance value operators. These operators can be applied to support compositional mapping and consistency checking of data views. The multiple viewpoint system was implemented as a Java-based application consisting of two sub-systems, one for viewpoint adaptation and management, the other for query processing and query result adjustment

Applications of flexible querying to graph data

Graph data models provide flexibility and extensibility that makes them well-suited to modelling data that may be irregular, complex, and evolving in structure and content. However, a consequence of this is that users may not be familiar with the full structure of the data, which itself may be changing over time, making it hard for users to formulate queries that precisely match the data graph and meet their information seeking requirements. There is a need therefore for flexible querying systems over graph data that can automatically make changes to the user's query so as to find additional or different answers, and so help the user to retrieve information of relevance to them. This chapter describes recent work in this area, looking at a variety of graph query languages, applications, flexible querying techniques and implementations

Composição de serviços para aplicações biomédicas

Doutoramento em Engenharia InformáticaA exigente inovação na área das aplicações biomédicas tem guiado a evolução das tecnologias de informação nas últimas décadas. Os desafios associados a uma gestão, integração, análise e interpretação eficientes dos dados provenientes das mais modernas tecnologias de hardware e software requerem um esforço concertado. Desde hardware para sequenciação de genes a registos electrónicos de paciente, passando por pesquisa de fármacos, a possibilidade de explorar com precisão os dados destes ambientes é vital para a compreensão da saúde humana. Esta tese engloba a discussão e o desenvolvimento de melhores estratégias informáticas para ultrapassar estes desafios, principalmente no contexto da composição de serviços, incluindo técnicas flexíveis de integração de dados, como warehousing ou federação, e técnicas avançadas de interoperabilidade, como serviços web ou LinkedData. A composição de serviços é apresentada como um ideal genérico, direcionado para a integração de dados e para a interoperabilidade de software. Relativamente a esta última, esta investigação debruçou-se sobre o campo da farmacovigilância, no contexto do projeto Europeu EU-ADR. As contribuições para este projeto, um novo standard de interoperabilidade e um motor de execução de workflows, sustentam a sucesso da EU-ADR Web Platform, uma plataforma para realizar estudos avançados de farmacovigilância. No contexto do projeto Europeu GEN2PHEN, esta investigação visou ultrapassar os desafios associados à integração de dados distribuídos e heterogéneos no campo do varíoma humano. Foi criada uma nova solução, WAVe - Web Analyses of the Variome, que fornece uma coleção rica de dados de variação genética através de uma interface Web inovadora e de uma API avançada. O desenvolvimento destas estratégias evidenciou duas oportunidades claras na área de software biomédico: melhorar o processo de implementação de software através do recurso a técnicas de desenvolvimento rápidas e aperfeiçoar a qualidade e disponibilidade dos dados através da adopção do paradigma de web semântica. A plataforma COEUS atravessa as fronteiras de integração e interoperabilidade, fornecendo metodologias para a aquisição e tradução flexíveis de dados, bem como uma camada de serviços interoperáveis para explorar semanticamente os dados agregados. Combinando as técnicas de desenvolvimento rápidas com a riqueza da perspectiva "Semantic Web in a box", a plataforma COEUS é uma aproximação pioneira, permitindo o desenvolvimento da próxima geração de aplicações biomédicas.The demand for innovation in the biomedical software domain has been an information technologies evolution driver over the last decades. The challenges associated with the effective management, integration, analyses and interpretation of the wealth of life sciences information stemming from modern hardware and software technologies require concerted efforts. From gene sequencing hardware to pharmacology research up to patient electronic health records, the ability to accurately explore data from these environments is vital to further improve our understanding of human health. This thesis encloses the discussion on building better informatics strategies to address these challenges, primarily in the context of service composition, including warehousing and federation strategies for resource integration, as well as web services or LinkedData for software interoperability. Service composition is introduced as a general principle, geared towards data integration and software interoperability. Concerning the latter, this research covers the service composition requirements within the pharmacovigilance field, namely on the European EU-ADR project. The contributions to this area, the definition of a new interoperability standard and the creation of a new workflow-wrapping engine, are behind the successful construction of the EUADR Web Platform, a workspace for delivering advanced pharmacovigilance studies. In the context of the European GEN2PHEN project, this research tackles the challenges associated with the integration of heterogeneous and distributed data in the human variome field. For this matter, a new lightweight solution was created: WAVe, Web Analysis of the Variome, provides a rich collection of genetic variation data through an innovative portal and an advanced API. The development of the strategies underlying these products highlighted clear opportunities in the biomedical software field: enhancing the software implementation process with rapid application development approaches and improving the quality and availability of data with the adoption of the Semantic Web paradigm. COEUS crosses the boundaries of integration and interoperability as it provides a framework for the flexible acquisition and translation of data into a semantic knowledge base, as well as a comprehensive set of interoperability services, from REST to LinkedData, to fully exploit gathered data semantically. By combining the lightness of rapid application development strategies with the richness of its "Semantic Web in a box" approach, COEUS is a pioneering framework to enhance the development of the next generation of biomedical applications

Integrated software architecture to support modern experimental biology

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2004.Includes bibliographical references (p. 127-132).Over the past several years, the explosive growth of biological data generated by new high-throughput instruments has virtually begun to drown the biological community. There is no established infrastructure to deal with these data in a consistent and successful fashion. This thesis presents a new informatics platform capable of supporting a large subsection of the experimental methods found in modem biology. A consistent data definition strategy is outlined that can handle gel electrophoresis, microarray, fluorescence activated cell sorting, mass spectrometry, and microscopy within a single coherent set of information object definitions. A key issue for interoperability is that common attributes are made truly identical between the different methods. This dramatically decreases the overhead of separate and distinct classes for each method, and reserves the uniqueness for attributes that are different between the methods. Thus, at least one higher level of integration is obtained. The thesis shows that rich object-oriented modeling together with object-relational database features and the uniform treatment of data and metadata is an ideal candidate for complex experimental information integration tasks. This claim is substantiated by elaborating on the coherent set of information object definitions and testing the corresponded database using real experimental data. A first implementation of this work--ExperiBase--is an integrated software platform to store and query data generated by the leading experimental protocols used in biology within a single database. It provides: comprehensive database features for searching and classifying; web-based client interfaces; web services; data import and export capabilities to accommodate other data(cont.) repositories; and direct support for metadata produced by analysis programs. Using JDBC, Java Servlets and Java Server Pages, SOAP, XML, and IIOP/CORBA's technologies, the information architecture is portable and platform independent. The thesis develops an ExperiBase XML according to the single coherent set of information object definitions, and also presents a new way of database federation--translating heterogeneous database schemas into the common ExperiBase XML schema and then merging the output: XML messages to get data federated. ExperiBase has become a reference implementation of the I3C Life Science Object Ontologies group.by Shixin Zhang.Ph.D

Enhancing systems biology models through semantic data integration

Studying and modelling biology at a systems level requires a large amount of data of different experimental types. Historically, each of these types is stored in its own distinct format, with its own internal structure for holding the data produced by those experiments. While the use of community data standards can reduce the need for specialised, independent formats by providing a common syntax, standards uptake is not universal and a single standard cannot yet describe all biological data. In the work described in this thesis, a variety of integrative methods have been developed to reuse and restructure already extant systems biology data. SyMBA is a simple Web interface which stores experimental metadata in a published, common format. The creation of accurate quantitative SBML models is a time-intensive manual process. Modellers need to understand both the systems they are modelling and the intricacies of the SBML format. However, the amount of relevant data for even a relatively small and well-scoped model can be overwhelming. Saint is a Web application which accesses a number of external Web services and which provides suggested annotation for SBML and CellML models. MFO was developed to formalise all of the knowledge within the multiple SBML specification documents in a manner which is both human and computationally accessible. Rule-based mediation, a form of semantic data integration, is a useful way of reusing and re-purposing heterogeneous datasets which cannot, or are not, structured according to a common standard. This method of ontology-based integration is generic and can be used in any context, but has been implemented specifically to integrate systems biology data and to enrich systems biology models through the creation of new biological annotations. The work described in this thesis is one step towards the formalisation of biological knowledge useful to systems biology. Experimental metadata has been transformed into common structures, a Web application has been created for the retrieval of data appropriate to the annotation of systems biology models and multiple data models have been formalised and made accessible to semantic integration techniques.EThOS - Electronic Theses Online ServiceBBSRCEPSRCGBUnited Kingdo

Graph Processing in Main-Memory Column Stores

Evermore, novel and traditional business applications leverage the advantages of a graph data model, such as the offered schema flexibility and an explicit representation of relationships between entities. As a consequence, companies are confronted with the challenge of storing, manipulating, and querying terabytes of graph data for enterprise-critical applications. Although these business applications operate on graph-structured data, they still require direct access to the relational data and typically rely on an RDBMS to keep a single source of truth and access. Existing solutions performing graph operations on business-critical data either use a combination of SQL and application logic or employ a graph data management system. For the first approach, relying solely on SQL results in poor execution performance caused by the functional mismatch between typical graph operations and the relational algebra. To the worse, graph algorithms expose a tremendous variety in structure and functionality caused by their often domain-specific implementations and therefore can be hardly integrated into a database management system other than with custom coding. Since the majority of these enterprise-critical applications exclusively run on relational DBMSs, employing a specialized system for storing and processing graph data is typically not sensible. Besides the maintenance overhead for keeping the systems in sync, combining graph and relational operations is hard to realize as it requires data transfer across system boundaries. A basic ingredient of graph queries and algorithms are traversal operations and are a fundamental component of any database management system that aims at storing, manipulating, and querying graph data. Well-established graph traversal algorithms are standalone implementations relying on optimized data structures. The integration of graph traversals as an operator into a database management system requires a tight integration into the existing database environment and a development of new components, such as a graph topology-aware optimizer and accompanying graph statistics, graph-specific secondary index structures to speedup traversals, and an accompanying graph query language. In this thesis, we introduce and describe GRAPHITE, a hybrid graph-relational data management system. GRAPHITE is a performance-oriented graph data management system as part of an RDBMS allowing to seamlessly combine processing of graph data with relational data in the same system. We propose a columnar storage representation for graph data to leverage the already existing and mature data management and query processing infrastructure of relational database management systems. At the core of GRAPHITE we propose an execution engine solely based on set operations and graph traversals. Our design is driven by the observation that different graph topologies expose different algorithmic requirements to the design of a graph traversal operator. We derive two graph traversal implementations targeting the most common graph topologies and demonstrate how graph-specific statistics can be leveraged to select the optimal physical traversal operator. To accelerate graph traversals, we devise a set of graph-specific, updateable secondary index structures to improve the performance of vertex neighborhood expansion. Finally, we introduce a domain-specific language with an intuitive programming model to extend graph traversals with custom application logic at runtime. We use the LLVM compiler framework to generate efficient code that tightly integrates the user-specified application logic with our highly optimized built-in graph traversal operators. Our experimental evaluation shows that GRAPHITE can outperform native graph management systems by several orders of magnitude while providing all the features of an RDBMS, such as transaction support, backup and recovery, security and user management, effectively providing a promising alternative to specialized graph management systems that lack many of these features and require expensive data replication and maintenance processes

Text mining for metabolic reaction extraction from scientific literature

Science relies on data in all its different forms. In molecular biology and bioinformatics in particular large scale data generation has taken centre stage in the form of high-throughput experiments. In line with this exponential increase of experimental data has been the near exponential growth of scientific publications. Yet where classical data mining techniques are still capable of coping with this deluge in structured data (Chapter 2), access of information found in scientific literature is still limited to search engines allowing searches on the level keywords, titles and abstracts. However, large amounts of knowledge about biological entities and their relations are held within the body of articles. When extracted, this data can be used as evidence for existing knowledge or hypothesis generation making scientific literature a valuable scientific resource. To unlock the information inside the articles requires a dedicated set of techniques and approaches tailored to the unstructured nature of free text. Analogous to the field of data mining for the analysis of structured data, the field of text mining has emerged for unstructured text and a number of applications has been developed in that field. This thesis is about text mining in the field of metabolomics. The work focusses on strategies for accessing large collections of scientific text and on the text mining steps required to extract metabolic reactions and their constituents, enzymes and metabolites, from scientific text. Metabolic reactions are important for our understanding of metabolic processes within cells and that information provides an important link between genotype phenotype. Furthermore information about metabolic reactions stored in databases is far from complete making it an excellent target for our text mining application. In order to access the scientific publications for further analysis they can be used as flat text or loaded into database systems. In Chapter 2we assessed and discussed the capabilities and performance of XML-type database systems to store and access very large collections of XML-type documents in the form of the Medline corpus, a collection of more than 20 million of scientific abstracts. XML data formats are common in the field of bioinformatics and are also at the core of most web services. With the increasing amount of data stored in XML comes the need for storing and accessing the data. The database systems were evaluated on a number of aspects broadly ranging from technical requirements to ease-of-use and performance. The performance of the different XML-type database systems was measured Medline abstract collections of increasing size and with a number of different queries. One of the queries assessed the capabilities of each database system to search the full-text of each abstract, which would allow access to the information within the text without further text analysis. The results show that all database systems cope well with the small and medium dataset, but that the full dataset remains a challenge. Also the query possibilities varied greatly across all studied databases. This led us to conclude that the performances and possibilities of the different database types vary greatly, also depending on the type of research question. There is no single system that outperforms the others; instead different circumstances can lead to a different optimal solution. Some of these scenarios are presented in the chapter. Among the conclusions of Chapter 2is that conventional data mining techniques do not work for the natural language part of a publication beyond simple retrieval queries based on pattern matching. The natural language used in written text is too unstructured for that purpose and requires dedicated text mining approaches, the main research topic of this thesis. Two major tasks of text mining are named entity recognition, the identification of relevant entities in the text, and relation extraction, the identification of relations between those named entities. For both text mining tasks many different techniques and approaches have been developed. For the named entity recognition of enzymes and metabolites we used a dictionary-based approach (Chapter 3) and for metabolic reaction extraction a full grammar approach (Chapter 4). In Chapter 3we describe the creation of two thesauri, one for enzymes and one for metabolites with the specific goal of allowing named entity identification, the mapping of identified synonyms to a common identifier, for metabolic reaction extraction. In the case of the enzyme thesaurus these identifiers are Enzyme Nomenclature numbers (EC number), in the case of the metabolite thesaurus KEGG metabolite identifiers. These thesauri are applied to the identification of enzymes and metabolites in the text mining approach of Chapter 4. Both were created from existing data sources by a series of automated steps followed by manual curation. Compared to a previously published chemical thesaurus, created entirely with automated steps, our much smaller metabolite thesaurus performed on the same level for F-measure with a slightly higher precision. The enzyme thesaurus produced results equal to our metabolite thesaurus. The compactness of our thesauri permits the manual curation step important in guaranteeing accuracy of the thesaurus contents, whereas creation from existing resources by automated means limits the effort required for creation. We concluded that our thesauri are compact and of high quality, and that this compactness does not greatly impact recall. In Chapter 4we studied the applicability and performance of a full parsing approach using the two thesauri described in Chapter 3 for the extraction of metabolic reactions from scientific full-text articles. For this we developed a text mining pipeline built around a modified dependency parser from the AGFL grammar lab using a pattern-based approach to extract metabolic reactions from the parsing output. Results of a comparison to a modified rule-based approach by Czarnecki et al.using three previously described metabolic pathways from the EcoCyc database show a slightly lower recall compared to the rule-based approach, but higher precision. We concluded that despite its current recall our full parsing approach to metabolic reaction extraction has high precision and potential to be used to (re-)construct metabolic pathways in an automated setting. Future improvements to the grammar and relation extraction rules should allow reactions to be extracted with even higher specificity. To identify potential improvements to the recall, the effect of a number of text pre-processing steps on the performance was tested in a number of experiments. The one experiment that had the most effect on performance was the conversion of schematic chemical formulas to syntactic complete sentences allowing them to be analysed by the parser. In addition to the improvements to the text mining approach described in Chapter 4I make suggestions in Chapter 5 for potential improvements and extensions to our full parsing approach for metabolic reaction extraction. Core focus here is the increase of recall by optimising each of the steps required for the final goal of extracting metabolic reactions from the text. Some of the discussed improvements are to increase the coverage of the used thesauri, possibly with specialist thesauri depending on the analysed literature. Another potential target is the grammar, where there is still room to increase parsing success by taking into account the characteristics of biomedical language. On a different level are suggestions to include some form of anaphora resolution and across sentence boundary search to increase the amount of information extracted from literature. In the second part of Chapter 5I make suggestions as to how to maximise the information gained from the text mining results. One of the first steps should be integration with other biomedical databases to allow integration with existing knowledge about metabolic reactions and other biological entities. Another aspect is some form of ranking or weighting of the results to be able to distinguish between high quality results useful for automated analyses and lower quality results still useful for manual approaches. Furthermore I provide a perspective on the necessity of computational literature analysis in the form of text mining. The main reasoning here is that human annotators cannot keep up with the amount of publications so that some form of automated analysis is unavoidable. Lastly I discuss the role of text mining in bioinformatics and with that also the accessibility of both text mining results and the literature resources necessary to create them. An important requirement for the future of text mining is that the barriers around high-throughput access to literature for text mining applications have to be removed. With regards to accessing text mining results, there is a long way to go for many applications, including ours, before they can be used directly by biologists. A major factor is that these applications rarely feature a suitable user interface and easy to use setup. To conclude, I see the main role of a text mining system like ours mainly in gathering evidence for existing knowledge and giving insights into the nuances of the research landscape of a given topic. When using the results of our reaction extraction system for the identification of ‘new’ reactions it is important to go back to the actual evidence presented for extra validations and to cross-validate the predictions with other resources or experiments. Ideally text mining will be used for generation of hypotheses, in which the researcher uses text mining findings to get ideas on, in our case, new connections between metabolites and enzymes; subsequently the researcher needs to go back to the original texts for further study. In this role text mining is an essential tool on the workbench of the molecular biologist.</p

Enabling Complex Semantic Queries to Bioinformatics Databases through Intuitive Search Over Data

Data integration promises to be one of the main catalysts in enabling new insights to be drawn from the wealth of biological data already available publicly. However, the heterogene- ity of the existing data sources still poses significant challenges for achieving interoperability among biological databases. Furthermore, merely solving the technical challenges of data in- tegration, for example through the use of common data representation formats, leaves open the larger problem. Namely, the steep learning curve required for understanding the data models of each public source, as well as the technical language through which the sources can be queried and joined. As a consequence, most of the available biological data remain practically unexplored today. In this thesis, we address these problems jointly, by first introducing an ontology-based data integration solution in order to mitigate the data source heterogeneity problem. We illustrate through the concrete example of Bgee, a gene expression data source, how relational databases can be exposed as virtual Resource Description Framework (RDF) graphs, through relational-to-RDF mappings. This has the important advantage that the original data source can remain unmodified, while still becoming interoperable with external RDF sources. We complement our methods with applied case studies designed to guide domain experts in formulating expressive federated queries targeting the integrated data across the domains of evolutionary relationships and gene expression. More precisely, we introduce two com- parative analyses, first within the same domain (using orthology data from multiple, inter- operable, data sources) and second across domains, in order to study the relation between expression change and evolution rate following a duplication event. Finally, in order to bridge the semantic gap between users and data, we design and im- plement Bio-SODA, a question answering system over domain knowledge graphs, that does not require training data for translating user questions to SPARQL. Bio-SODA uses a novel ranking approach that combines syntactic and semantic similarity, while also incorporating node centrality metrics to rank candidate matches for a given user question. Our results in testing Bio-SODA across several real-world databases that span multiple domains (both within and outside bioinformatics) show that it can answer complex, multi-fact queries, be- yond the current state-of-the-art in the more well-studied open-domain question answering. -- L’intégration des données promet d’être l’un des principaux catalyseurs permettant d’extraire des nouveaux aperçus de la richesse des données biologiques déjà disponibles publiquement. Cependant, l’hétérogénéité des sources de données existantes pose encore des défis importants pour parvenir à l’interopérabilité des bases de données biologiques. De plus, en surmontant seulement les défis techniques de l’intégration des données, par exemple grâce à l’utilisation de formats standard de représentation de données, on laisse ouvert un problème encore plus grand. À savoir, la courbe d’apprentissage abrupte nécessaire pour comprendre la modéli- sation des données choisie par chaque source publique, ainsi que le langage technique par lequel les sources peuvent être interrogés et jointes. Par conséquent, la plupart des données biologiques publiquement disponibles restent pratiquement inexplorés aujourd’hui. Dans cette thèse, nous abordons l’ensemble des deux problèmes, en introduisant d’abord une solution d’intégration de données basée sur ontologies, afin d’atténuer le problème d’hété- rogénéité des sources de données. Nous montrons, à travers l’exemple de Bgee, une base de données d’expression de gènes, une approche permettant les bases de données relationnelles d’être publiés sous forme de graphes RDF (Resource Description Framework) virtuels, via des correspondances relationnel-vers-RDF (« relational-to-RDF mappings »). Cela présente l’important avantage que la source de données d’origine peut rester inchangé, tout en de- venant interopérable avec les sources RDF externes. Nous complétons nos méthodes avec des études de cas appliquées, conçues pour guider les experts du domaine dans la formulation de requêtes fédérées expressives, ciblant les don- nées intégrées dans les domaines des relations évolutionnaires et de l’expression des gènes. Plus précisément, nous introduisons deux analyses comparatives, d’abord dans le même do- maine (en utilisant des données d’orthologie provenant de plusieurs sources de données in- teropérables) et ensuite à travers des domaines interconnectés, afin d’étudier la relation entre le changement d’expression et le taux d’évolution suite à une duplication de gène. Enfin, afin de mitiger le décalage sémantique entre les utilisateurs et les données, nous concevons et implémentons Bio-SODA, un système de réponse aux questions sur des graphes de connaissances domaine-spécifique, qui ne nécessite pas de données de formation pour traduire les questions des utilisateurs vers SPARQL. Bio-SODA utilise une nouvelle ap- proche de classement qui combine la similarité syntactique et sémantique, tout en incorporant des métriques de centralité des nœuds, pour classer les possibles candidats en réponse à une question utilisateur donnée. Nos résultats suite aux tests effectués en utilisant Bio-SODA sur plusieurs bases de données à travers plusieurs domaines (tantôt liés à la bioinformatique qu’extérieurs) montrent que Bio-SODA réussit à répondre à des questions complexes, en- gendrant multiples entités, au-delà de l’état actuel de la technique en matière de systèmes de réponses aux questions sur les données structures, en particulier graphes de connaissances

Work flows in life science

The introduction of computer science technology in the life science domain has resulted in a new life science discipline called bioinformatics. Bioinformaticians are biologists who know how to apply computer science technology to perform computer based experiments, also known as in-silico or dry lab experiments. Various tools, such as databases, web applications and scripting languages, are used to design and run in-silico experiments. As the size and complexity of these experiments grow, new types of tools are required to design and execute the experiments and to analyse the results. Workflow systems promise to fulfill this role. The bioinformatician composes an experiment by using tools and web services as building blocks, and connecting them, often through a graphical user interface. Workflow systems, such as Taverna, provide access to up to a few thousand resources in a uniform way. Although workflow systems are intended to make the bioinformaticians' work easier, bioinformaticians experience difficulties in using them. This thesis is devoted to find out which problems bioinformaticians experience using workflow systems and to provide solutions for these problems.\u

Graph-Based Weakly-Supervised Methods for Information Extraction & Integration

The variety and complexity of potentially-related data resources available for querying --- webpages, databases, data warehouses --- has been growing ever more rapidly. There is a growing need to pose integrative queries across multiple such sources, exploiting foreign keys and other means of interlinking data to merge information from diverse sources. This has traditionally been the focus of research within Information Extraction (IE) and Information Integration (II) communities, with IE focusing on converting unstructured sources into structured sources, and II focusing on providing a unified view of diverse structured data sources. However, most of the current IE and II methods, which can potentially be applied to the pro blem of integration across sources, require large amounts of human supervision, often in the form of annotated data. This need for extensive supervision makes existing methods expensive to deploy and difficult to maintain. In this thesis, we develop techniques that generalize from limited human input, via weakly-supervised methods for IE and II. In particular, we argue that graph-based representation of data and learning over such graphs can result in effective and scalable methods for large-scale Information Extraction and Integration. Within IE, we focus on the problem of assigning semantic classes to entities. First we develop a context pattern induction method to extend small initial entity lists of various semantic classes. We also demonstrate that features derived from such extended entity lists can significantly improve performance of state-of-the-art discriminative taggers. The output of pattern-based class-instance extractors is often high-precision and low-recall in nature, which is inadequate for many real world applications. We use Adsorption, a graph based label propagation algorithm, to significantly increase recall of an initial high-precision, low-recall pattern-based extractor by combining evidences from unstructured and structured text corpora. Building on Adsorption, we propose a new label propagation algorithm, Modified Adsorption (MAD), and demonstrate its effectiveness on various real-world datasets. Additionally, we also show how class-instance acquisition performance in the graph-based SSL setting can be improved by incorporating additional semantic constraints available in independently developed knowledge bases. Within Information Integration, we develop a novel system, Q, which draws ideas from machine learning and databases to help a non-expert user construct data-integrating queries based on keywords (across databases) and interactive feedback on answers. We also present an information need-driven strategy for automatically incorporating new sources and their information in Q. We also demonstrate that Q\u27s learning strategy is highly effective in combining the outputs of ``black box\u27\u27 schema matchers and in re-weighting bad alignments. This removes the need to develop an expensive mediated schema which has been necessary for most previous systems