Initially designed for Knowledge Graphs (KG) containing plain (s, p, o) triples, research on link prediction has evolved to address more complex structures such as hypergraphs, hyper-relational graphs, and bi-level graphs. These advancements push link prediction to handle annotation patterns where a triple is annotated with another (t, p, t), or where a triple is annotated with qualifiers (t, p, o). However, each approach focuses on a single annotation pattern, and the case where a triple is annotated with a subject (s, p, t) has never been explored. In this paper, we propose LP4P, the first link prediction model capable of predicting entities in both plain triples and three annotation patterns. LP4P captures the information expressed in annotations through a four-pattern attention layer, and its loss function further leverages the ontological information of KGs. Moreover, we built WD4P, an RDF KG derived from baseline datasets, which includes the four patterns. Extensive empirical evaluations demonstrate that LP4P outperforms relevant state-of-the-art models on knowledge graphs with the four patterns, and achieves comparable performance on standard benchmarks.

Scientific workflows have established themselves as valuable tools for designing, automating, and sharing scientific experiments and analyses, with the aim of promoting reproducibility and reuse. However, early evidence from a decade-old experiment revealed that a significant portion of workflows suffer from decay, making them difficult to rerun, let alone repeat or reproduce successfully. In this paper, we report on the results of an empirical analysis that offers a fresh perspective on this issue by examining workflows specified using workflow languages that are currently popular, namely SnakeMake and Nextflow. Our analysis seeks to address several key questions: Is workflow decay still prevalent, and have its causes evolved from the previous generations? What mechanisms and techniques can mitigate it?

International audience

Today, large amounts of data are collected in various domains, presenting unprecedented economic and societal opportunities. Yet, at present, the exploitation of these data sets through data science methods is primarily dominated by AI-savvy users. From an inclusive perspective, there is a need for solutions that can democratise data science that can guide non-specialists intuitively to explore data collections and extract knowledge out of them. This paper introduces the vision of a new data science engine, called DS4ALL (Data Science for ALL), that empowers users who are neither computer nor AI experts to perform sophisticated data exploration and analysis tasks. Therefore, DS4ALL is based on a conversational and intuitive approach that insulates users from the complexity of AI algorithms. DS4ALL allows a dialogue-based approach that gives the user greater freedom of expression. It will enable them to communicate using natural language without requiring a high level of expertise on data-driven algorithms. User requests are interpreted and handled internally by the system in an automated manner, providing the user with the required output by masking the complexity of the data science workflow. The system can also collect feedback on the displayed results, leveraging these comments to address personalized data analysis sessions. The benefits of the envisioned system are discussed, and a use case is also presented to describe the innovative aspects.

International audience

International audience

Data exchange is one of the oldest database problems, being of both practical and theoretical interest. Given the pace at which heterogeneous data are published on the web, thanks to initiatives such as Linked Data and Open Science, scalability of data exchange becomes crucial. Pivotal to data exchange is the chase algorithm, which is a fixpoint algorithm to evaluate both source-to-target constraints and target constraints in the data exchange process. In this paper, we investigate how new programming models such as MapReduce can be used to implement the chase on large-scale data sources. To the best of our knowledge, how to exchange data at scale has not been investigated so far. We present an initial solution for chasing source-to-target tuple generating dependencies and target tuple-generating dependencies, and discuss open issues that need to be addressed to leverage MapReduce for the data exchange problem.

Technical developments, such as the web of data and web data extraction, combined with policy developments such as those relating to open government or open science, are leading to the availability of increasing numbers of data sources. Indeed, given these physical sources, it is then also possible to create further virtual sources that integrate, aggregate or summarise the data from the original sources. As a result, there is a plethora of data sources, from which a small subset may be able to provide the information required to support a task. The number and rate of change in the available sources is likely to make manual source selection and curation by experts impractical for many applications, leading to the need to pursue a pay-as-you-go approach, in which crowds or data consumers annotate results based on their correctness or suitability, with the resulting annotations used to inform, e.g., source selection algorithms. However, for pay-as-you-go feedback collection to be cost-effective, it may be necessary to select judiciously the data items on which feedback is to be obtained. This paper describes OLBP (Ordering and Labelling By Precision), a heuristics-based approach to the targeting of data items for feedback to support mapping and source selection tasks, where users express their preferences in terms of the trade-off between precision and recall. The proposed approach is then evaluated on two different scenarios, mapping selection with synthetic data, and source selection with real data produced by web data extraction. The results demonstrate a significant reduction in the amount of feedback required to reach user-provided objectives when using OLBP.

It has become widely recognized that user feedback can play a fundamental role in facilitating information integration tasks, e.g., the construction of integration schema and the specification of schema mappings. While promising, existing proposals make the assumption that the users providing feedback expect the same results from the integration system. In practice, however, different users may anticipate different results, due, e.g., to their preferences or application of interest, in which case the feedback they provide may be conflicting, thereby deteriorating the quality of the services provided by the integration system. In this paper, we present clustering strategies for grouping information integration users into groups of users with similar expectations as to the results delivered by the integration system. As well as grouping information integration users, we show that clustering results can be used as inputs to a wide range of functionalities that are relevant in the context of crowd-driven information integration. Specifically, we show that clustering can be used to identify feedback of relevance to a given user by exploiting the feedback provided by other users in the same cluster. We report on evaluation exercises that assess the effectiveness of the clustering strategies we propose, and showcase the benefits community- and crowd-driven information integration can derive from clustering.

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 Huntington’s 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.

A major issue that arises when designing scientific experiments(i.e., workflows) is that of identifying the modules (which are of-ten “black boxes”), that are suitable for performing the steps of theexperiment. To assist scientists in the task of identifying suitablemodules, semantic annotations have been proposed and used to de-scribe scientific modules. Different facets of the module can be de-scribed using semantic annotations. Our experience with scientistsfrom modern sciences such as bioinformatics, biodiversity and as-tronomy, however, suggests that most of semantic annotations thatare available are confined to the description of the domain of inputand output parameters of modules. Annotations specifying the be-havior of the modules, as to the tasks they play, are rarely specified.To address this issue, we argue in this paper that data examples arean intuitive and effective means for understanding the behavior ofscientific modules. We present a heuristic for automatically gener-ating data examples that annotate scientific modules without rely-ing on the existence of the module specifications, and show throughan empirical evaluation that uses real-world scientific modules theeffectiveness of the heuristic proposed.The data examples generated can be utilized in a range of scientificmodule management operations. To demonstrate this, we presentthe results of two real-world exercises that show that: (i) Data ex-amples are an intuitive means for human users to understand thebehavior of scientific modules, and that (ii) data examples are aneffective ingredient for matching scientific modules.

Provenance, a form of structured metadata designed to record the origin or source of information, can be instrumental in deciding whether information is to be trusted, how it can be integrated with other diverse information sources, and how to establish attribution of information to authors throughout its history. The PROV set of specifications, produced by the World Wide Web Consortium (W3C), is designed to promote the publication of provenance information on the Web, and offers a basis for interoperability across diverse provenance management systems. The PROV provenance model is deliberately generic and domain-agnostic, but extension mechanisms are available and can be exploited for modelling specific domains. This tutorial provides an account of these specifications. Starting from intuitive and informal examples that present idiomatic provenance patterns, it progressively introduces the relational model of provenance along with the constraints model for validation of provenance documents, and concludes with example applications that show the extension points in use.

The vision of dataspaces proposes an alternative to classical data integration approaches with reduced up-front costs followed by incremental improvement on a pay-as-you-go basis. In this paper, we demonstrate DSToolkit, a system that allows users to provide feedback on results of queries posed over an integration schema. Such feedback is then used to annotate the mappings with their respective precision and recall. The system then allows a user to state the expected levels of precision (or recall) that the query results should exhibit and, in order to produce those results, the system selects those mappings that are predicted to meet the stated constraints.

The specification of schema mappings has proved to be time and resource consuming, and has been recognized as a critical bottleneck to the large scale deployment of data integration systems. In an attempt to address this issue, dataspaces have been proposed as a data management abstraction that aims to reduce the up-front cost required to setup a data integration system by gradually specifying schema mappings through interaction with end users in a pay-as-you-go fashion. As a step in this direction, we explore an approach for incrementally annotating schema mappings using feedback obtained from end users. In doing so, we do not expect users to examine mapping specifications; rather, they comment on results to queries evaluated using the mappings. Using annotations computed on the basis of user feedback, we present a method for selecting from the set of candidate mappings, those to be used for query evaluation considering user requirements in terms of precision and recall. In doing so, we cast mapping selection as an optimization problem. Mapping annotations may reveal that the quality of schema mappings is poor. We also show how feedback can be used to support the derivation of better quality mappings from existing mappings through refinement. An evolutionary algorithm is used to efficiently and effectively explore the large space of mappings that can be obtained through refinement. The results of evaluation exercises show the effectiveness of our solution for annotating, selecting and refining schema mappings.

The management and querying of workflow provenance data underpins a collection of activities, including the analysis of workflow results, and the debugging of workflows or services. Such activities require efficient evaluation of lineage queries over potentially complex and voluminous provenance logs. Näive implementations of lineage queries navigate provenance logs by joining tables that represent the flow of data between connected processors invoked from workflows. In this paper we provide an approach to provenance querying that: (i) avoids joins over provenance logs by using information about the workflow definition to inform the construction of queries that directly target relevant lineage results; (ii) provides fine grained provenance querying, even for workflows that create and consume collections; and (iii) scales effectively to address complex workflows, workflows with large intermediate data sets, and queries over multiple workflows.

Semantic annotations of web services can support the effective and efficient discovery of services, and guide their composition into workflows. At present, however, the practical utility of such annotations is limited by the small number of service annotations available for general use. Manual annotation of services is a time consuming and thus expensive task, so some means are required by which services can be automatically (or semi-automatically) annotated. In this paper, we show how information can be inferred about the semantics of operation parameters based on their connections to other (annotated) operation parameters within tried-and-tested workflows. Because the data links in the workflows do not necessarily contain every possible connection of compatible parameters, we can infer only constraints on the semantics of parameters. We show that despite their imprecise nature these so-called loose annotations are still of value in supporting the manual annotation task, inspecting workflows and discovering services. We also show that derived annotations for already annotated parameters are useful. By comparing existing and newly derived annotations of operation parameters, we can support the detection of errors in existing annotations, the ontology used for annotation and in workflows. The derivation mechanism has been implemented, and its practical applicability for inferring new annotations has been established through an experimental evaluation. The usefulness of the derived annotations is also demonstrated.

Service oriented computing has gained a considerable momentum as a new paradigm for building enterprise information systems. Notable efforts have been made recently from both researchers and industrials to support the construction of service-based applications, nevertheless several issues still need to be tackled including service definition and adaptation, and services orchestration. This work proposes an approach for building and finely orchestrating open and adaptable services. An open service is represented by a workflow that coordinates calls to service provider methods. Thereby component activities and the way they are synchronized are rendered visible. Service adaptability refers to the possibility to modify an open service. Through adaptation operations a service can be customized according to given user (application) requirements. In order to finely orchestrate services, they are associated with entry points. An entry point acts as a gateway for inserting and getting information about the progress of service execution. Defined services and orchestration are verified to ensure a correct behaviour of the resulting application. The paper details our approach for building and orchestrating services, and presents associated architectural choices.

This paper presents TERRA and ACQUA, two mechanisms that support adaptable definition and execution of workflows. TERRA generates and stores structures (objects, events, reactions) necessary for executing a workflow. AQUA executes workflows according to a parametric behaviour model that represents workflow execution policies in terms of dimensions associated to values. TERRA and ACQUA provide mechanisms for modifying the structure of a workflow and its associated behaviour.

This paper introduces definition and enactment models to characterize workflows. The proposed knowledge model provides concepts for modeling activities, data used by such activities, agents, and ordering operators and synchronization modes used for specifying activities dependencies. The behavior model provides dimensions that characterize workflow enactment. Both models are currently implemented in our workflow management system called AFLOWS. The originality of our approach is related to its flexibility as different enactment models can be associated to a workflow simply by giving values to dimensions according to application needs. Such an approach and system provide a suitable support for specifying and enacting workflow processes that may be useful today to describe and monitor process-oriented applications (i.e., large Web-based applications that run across wide area networks and multiple enterprises).