An Incremental Learning Method to Support the Annotation of Workflows with Data-to-Data Relations

Workflow formalisations are often focused on the representation of a process with the primary objective to support execution. However, there are scenarios where what needs to be represented is the effect of the process on the data artefacts involved, for example when reasoning over the corresponding data policies. This can be achieved by annotating the workflow with the semantic relations that occur between these data artefacts. However, manually producing such annotations is difficult and time consuming. In this paper we introduce a method based on recommendations to support users in this task. Our approach is centred on an incremental rule association mining technique that allows to compensate the cold start problem due to the lack of a training set of annotated workflows. We discuss the implementation of a tool relying on this approach and how its application on an existing repository of workflows effectively enable the generation of such annotations

Distributed Semantic Social Networks: Architecture, Protocols and Applications

Online social networking has become one of the most popular services on the Web. Especially Facebook with its 845Mio+ monthly active users and 100Mrd+ friendship relations creates a Web inside the Web. Drawing on the metaphor of islands, Facebook is becoming more like a continent. However, users are locked up on this continent with hardly any opportunity to communicate easily with users on other islands and continents or even to relocate trans-continentally. In addition to that, privacy, data ownership and freedom of communication issues are problematically in centralized environments. The idea of distributed social networking enables users to overcome the drawbacks of centralized social networks. The goal of this thesis is to provide an architecture for distributed social networking based on semantic technologies. This architecture consists of semantic artifacts, protocols and services which enable social network applications to work in a distributed environment and with semantic interoperability. Furthermore, this thesis presents applications for distributed semantic social networking and discusses user interfaces, architecture and communication strategies for this application category.Soziale Netzwerke gehören zu den beliebtesten Online Diensten im World Wide Web. Insbesondere Facebook mit seinen mehr als 845 Mio. aktiven Nutzern im Monat und mehr als 100 Mrd. Nutzer- Beziehungen erzeugt ein eigenständiges Web im Web. Den Nutzern dieser Sozialen Netzwerke ist es jedoch schwer möglich mit Nutzern in anderen Sozialen Netzwerken zu kommunizieren oder aber mit ihren Daten in ein anderes Netzwerk zu ziehen. Zusätzlich dazu werden u.a. Privatsphäre, Eigentumsrechte an den eigenen Daten und uneingeschränkte Freiheit in der Kommunikation als problematisch empfunden. Die Idee verteilter Soziale Netzwerke ermöglicht es, diese Probleme zentralisierter Sozialer Netzwerke zu überwinden. Das Ziel dieser Arbeit ist die Darstellung einer Architektur verteilter Soziale Netzwerke welche auf semantischen Technologien basiert. Diese Architektur besteht aus semantischen Artefakten, Protokollen und Diensten und ermöglicht die Kommunikation von Sozialen Anwendungen in einer verteilten Infrastruktur. Darüber hinaus präsentiert diese Arbeit mehrere Applikationen für verteilte semantische Soziale Netzwerke und diskutiert deren Nutzer-Schnittstellen, Architektur und Kommunikationsstrategien. 

Named Entity Resolution in Personal Knowledge Graphs

Entity Resolution (ER) is the problem of determining when two entities refer to the same underlying entity. The problem has been studied for over 50 years, and most recently, has taken on new importance in an era of large, heterogeneous 'knowledge graphs' published on the Web and used widely in domains as wide ranging as social media, e-commerce and search. This chapter will discuss the specific problem of named ER in the context of personal knowledge graphs (PKGs). We begin with a formal definition of the problem, and the components necessary for doing high-quality and efficient ER. We also discuss some challenges that are expected to arise for Web-scale data. Next, we provide a brief literature review, with a special focus on how existing techniques can potentially apply to PKGs. We conclude the chapter by covering some applications, as well as promising directions for future research.Comment: To appear as a book chapter by the same name in an upcoming (Oct. 2023) book `Personal Knowledge Graphs (PKGs): Methodology, tools and applications' edited by Tiwari et a

Structuring research methods and data with the research object model:genomics workflows as a case study

Background: One of the main challenges for biomedical research lies in the computer-assisted integrative study of large and increasingly complex combinations of data in order to understand molecular mechanisms. The preservation of the materials and methods of such computational experiments with clear annotations is essential for understanding an experiment, and this is increasingly recognized in the bioinformatics community. Our assumption is that offering means of digital, structured aggregation and annotation of the objects of an experiment will provide necessary meta-data for a scientist to understand and recreate the results of an experiment. To support this we explored a model for the semantic description of a workflow-centric Research Object (RO), where an RO is defined as a resource that aggregates other resources, e. g., datasets, software, spreadsheets, text, etc. We applied this model to a case study where we analysed human metabolite variation by workflows. Results: We present the application of the workflow-centric RO model for our bioinformatics case study. Three workflows were produced following recently defined Best Practices for workflow design. By modelling the experiment as an RO, we were able to automatically query the experiment and answer questions such as "which particular data was input to a particular workflow to test a particular hypothesis?", and "which particular conclusions were drawn from a particular workflow?". Conclusions: Applying a workflow-centric RO model to aggregate and annotate the resources used in a bioinformatics experiment, allowed us to retrieve the conclusions of the experiment in the context of the driving hypothesis, the executed workflows and their input data. The RO model is an extendable reference model that can be used by other systems as well. Availability: The Research Object is available at http://www.myexperiment.org/packs/428 The Wf4Ever Research Object Model is available at http://wf4ever.github.io/r

Knowledge Graphs for the Life Sciences: Recent Developments, Challenges and Opportunities

The term life sciences refers to the disciplines that study living organisms and life processes, and include chemistry, biology, medicine, and a range of other related disciplines. Research efforts in life sciences are heavily data-driven, as they produce and consume vast amounts of scientific data, much of which is intrinsically relational and graphstructured. The volume of data and the complexity of scientific concepts and relations referred to therein promote the application of advanced knowledgedriven technologies for managing and interpreting data, with the ultimate aim to advance scientific discovery. In this survey and position paper, we discuss recent developments and advances in the use of graph-based technologies in life sciences and set out a vision for how these technologies will impact these fields into the future. We focus on three broad topics: the construction and management of Knowledge Graphs (KGs), the use of KGs and associated technologies in the discovery of new knowledge, and the use of KGs in artificial intelligence applications to support explanations (explainable AI). We select a few exemplary use cases for each topic, discuss the challenges and open research questions within these topics, and conclude with a perspective and outlook that summarizes the overarching challenges and their potential solutions as a guide for future research

An empirically-based framework for ontology modularization

Modularity is being increasingly used as an approach to solve for the information overload problem in ontologies. It eases cognitive complexity for humans, and computational complexity for machines. The current literature for modularity focuses mainly on techniques, tools, and on evaluation metrics. However, ontology developers still face difficulty in selecting the correct technique for specific applications and the current tools for modularity are not sufficient. These issues stem from a lack of theory about the modularisation process. To solve this problem, several researchers propose a framework for modularity, but alas, this has not been realised, up until now. In this article, we survey the existing literature to identify and populate dimensions of modules, experimentally evaluate and characterise 189 existing modules, and create a framework for modularity based on these results. The framework guides the ontology developer throughout the modularisation process. We evaluate the framework with a use-case for the Symptom ontology

Organização e Representação do Conhecimento Científico em Ambiente Web: do formato textual linear aos artigos semânticos

Artigos científicos são ainda hoje publicados eletronicamente segundo o formato textual-linear, cópia digital do formato impresso. Este formato impede que programas possam ser usados para o processamento “semântico” desses conteúdos. O trabalho discute a emergência dos artigos científicas semânticos, utilizando tecnologias da Web Semântica, suas motivações, potencialidades e conseqüências para gestão do conhecimento científico. São levantados requisitos funcionais para artigos semânticos. É apresentado um modelo que serve para ilustrar o atendimento a estes requisitos e suas potencialidades