José Manuél Gómez-Pérez

Why workflows break — Understanding and combating decay in Taverna workflows

Workflows provide a popular means for preserving scientific methods by explicitly encoding their process. However, some of them are subject to a decay in their ability to be re-executed or reproduce the same results over time, largely due to the volatility of the resources required for workflow executions. This paper provides an analysis of the root causes of workflow decay based on an empirical study of a collection of Taverna workflows from the myExperiment repository. Although our analysis was based on a specific type of workflow, the outcomes and methodology should be applicable to workflows from other systems, at least those whose executions also rely largely on accessing third-party resources. Based on our understanding about decay we recommend a minimal set of auxiliary resources to be preserved together with the workflows as an aggregation object and provide a software tool for end-users to create such aggregations and to assess their completeness.

A novel approach to visualizing and navigating ontologies

There is empirical evidence that the user interaction metaphors used in ontology engineering toolkits are largely inadequate and that novel interactive frameworks for human ontology interaction are needed. Here we present a novel tool for visualizing and navigating ontologies, called KC Viz, which exploits an innovative ontology summarization method to support a ’middleout ontology browsing’ approach, where it becomes possible to navigate ontologies starting from the most information-rich nodes (i.e., key concepts). This approach is similar to map-based visualization and navigation in Geographical Information Systems, where, e.g., major cities are displayed more prominently than others, depending on the current level of granularity.

Using a suite of ontologies for preserving workflow-centric research objects

Scientific workflows are a popular mechanism for specifying and automating data-driven in silico experiments. A significant aspect of their value lies in their potential to be reused. Once shared, workflows become useful building blocks that can be combined or modified for developing new experiments. However, previous studies have shown that storing workflow specifications alone is not sufficient to ensure that they can be successfully reused, without being able to understand what the workflows aim to achieve or to re-enact them. To gain an understanding of the workflow, and how it may be used and repurposed for their needs, scientists require access to additional resources such as annotations describing the workflow, datasets used and produced by the workflow, and provenance traces recording workflow executions.In this article, we present a novel approach to the preservation of scientific workflows through the application of research objects-aggregations of data and metadata that enrich the workflow specifications. Our approach is realised as a suite of ontologies that support the creation of workflow-centric research objects. Their design was guided by requirements elicited from previous empirical analyses of workflow decay and repair. The ontologies developed make use of and extend existing well known ontologies, namely the Object Reuse and Exchange (ORE) vocabulary, the Annotation Ontology (AO) and the W3C PROV ontology (PROVO). We illustrate the application of the ontologies for building Workflow Research Objects with a case-study that investigates Huntingtons disease, performed in collaboration with a team from the Leiden University Medial Centre (HG-LUMC). Finally we present a number of tools developed for creating and managing workflow-centric research objects.

Using task context to achieve effective information delivery

The use of task context to guide the delivery of information to knowledge workers is valuable for improving their efficiency and effectiveness. Moreover, the sharing of context between individuals can aid the sharing of knowledge. This paper describes research in the ACTIVE project which uses context to support information delivery and sharing. Machine intelligence techniques are used to learn the association between information objects and context; and to learn how to partition a users information objects into a set of contexts. Informal processes are also important to knowledge workers, and another research challenge is to understand how context influences the choice of steps in a process. Other research questions relate to the user interface for context-driven information delivery. Chief amongst these questions is whether the user wishes or needs to be aware of the concepts of context and process; or whether only the system should be aware.

Solving collaborative fuzzy agents problems with CLP( FD )

Truth values associated to fuzzy variables can be represented in an ordeal of different flavors, such as real numbers, percentiles, intervals, unions of intervals, and continuous or discrete functions on different domains. Many of the most interesting fuzzy problems deal with a discrete range of truth values. In this work we represent these ranges using Constraint Logic Programming over Finite Domains (CLP(

ROHub — A Digital Library of Research Objects Supporting Scientists Towards Reproducible Science

\mathcal{FD}

miKrow: semantic intra-enterprise micro-knowledge management system

)). This allows to produce finite enumerations of constructive answers instead of complicated, hardly self-explanatory, constraints expressions. Another advantage of representing fuzzy models through finite domains is that some of the existing techniques and algorithms of the field of distributed constraint programming can be borrowed. In this paper we exploit these considerations in order to create a new generation of collaborative fuzzy agents in a distributed environment.

Ontological Engineering and the Semantic Web

Research Objects (ROs) are semantic aggregations of related scientific resources, their annotations and research context. They are meant to help scientists to refer to all the materials supporting their investigation. ROHub is a digital library system for ROs that supports their storage, lifecycle management and preservation. It provides a Web interface and a set of RESTful APIs enabling the sharing of scientific findings via ROs. Additionally, ROHub includes different features that help scientists throughout the research lifecycle to create and maintain high-quality ROs that can be interpreted and reproduced in the future. For instance, scientists can assess the conformance of an RO to a set of predefined requirements and create RO Snapshots, at any moment, to share, cite or submit to review the current state of research outcomes. ROHub can also generate nested ROs for workflow runs, exposing their content and annotations, and includes monitoring features that generate notifications when changes are detected.

Applying problem solving methods for process knowledge acquisition, representation, and reasoning

Knowledge Management systems are one of the key strategies that allow companies to fully tap into their collective knowledge. However, two main entry barriers currently limit the potential of this approach: i) the hurdles employees encounter discouraging them from a strong and active participation (knowledge providing) and ii) the lack of truly evolved intelligent technologies that allow those employees to easily benefiting from the global knowledge provided by them and other users (knowledge consuming). Both needs can sometimes require opposite approaches, tending the current solutions to be not user friendly enough for user participation to be strong or not intelligent enough for them to be useful. In this paper, a lightweight framework for Knowledge Management is proposed based on the combination of two layers that cater to each need: a microblogging layer that simplifies how users interact with the whole system and a semantic powered engine that performs all the intelligent heavy lifting by combining semantic indexing and search of messages and users. Different mechanisms are also presented as extensions that can be plugged-in on demand and help expanding the capabilities of the whole system.

Exploiting Linked Data and Knowledge Graphs in Large Organisations

This chapter focuses on ontologies as means of representing knowledge and reason across the various domains. In this chapter, the state of the art in ontology engineering from a number of different perspectives is reviewed ranging from methodologies to manage the ontology lifecycle, from their inception to maintenance, to knowledge representing languages, reasoning methods and technology, and ontology development frameworks. The role of networked ontologies as a new stage in ontology engineering is described, where we transit from isolated information silos to networks of knowledge. Key issues of networked ontologies like modularization, customization and ontology alignment are approached. The different methods, technologies, and frameworks described herein with real life applications using ontologies are illustrated. This chapter is concluded with the introduction of key concepts for future work in ontology engineering and the Semantic Web.