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

Daga, Enrico; d’Aquin, Mathieu; Gangemi, Aldo; Motta, Enrico

doi:10.1007/978-3-319-49004-5_9

Cited by 3 publications

(5 citation statements)

References 19 publications

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“…Reasoning on policy propagation requires a formalisation of the data flow, and producing such representation can be time consuming. Recent work by the authors investigate how it is possible to support users in the formalisation of data flows derived from scientific workflows [11]. It would be of interest to explore methods for supporting and automating the generation of such data flows from other pre-existing artefacts (e.g., code bases and their documentation).…”

Section: Discussionmentioning

confidence: 99%

Reasoning with data flows and policy propagation rules

Daga

Gangemi

Motta

2018

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Data-oriented systems and applications are at the centre of current developments of the World Wide Web. In these scenarios, assessing what policies propagate from the licenses of data sources to the output of a given data-intensive system is an important problem. Both policies and data flows can be described with Semantic Web languages. Although it is possible to define Policy Propagation Rules (PPR) by associating policies to data flow steps, this activity results in a huge number of rules to be stored and managed. In a recent paper, we introduced strategies for reducing the size of a PPR knowledge base by using an ontology of the possible relations between data objects, the Datanode ontology, and applying the (A)AAAA methodology, a knowledge engineering approach that exploits Formal Concept Analysis (FCA). In this article, we investigate whether this reasoning is feasible and how it can be performed. For this purpose, we study the impact of compressing a rule base associated with an inference mechanism on the performance of the reasoning process. Moreover, we report on an extension of the (A)AAAA methodology that includes a coherency check algorithm, that makes this reasoning possible. We show how this compression, in addition to being beneficial to the management of the knowledge base, also has a positive impact on the performance and resource requirements of the reasoning process for policy propagation.

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Section: Discussionmentioning

confidence: 99%

Reasoning with data flows and policy propagation rules

Daga

Gangemi

Motta

2018

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“…Another strand of research has focused on capturing knowledge from scientific processes, in order to support design and management of workflows [26], identify common patterns and motifs in them [13,17], or recommend activities to tackle the cold start problem of experimental design [10]. These works demonstrated that research reproducibility and support to frame research hypotheses can be supported by semantically describing and mining workflow components.…”

Section: Methods and Applicationsmentioning

confidence: 99%

Fostering Scientific Meta-analyses with Knowledge Graphs: A Case-Study

2020

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A meta-analysis is a Science of Science method widely used in the medical and social sciences to review, aggregate and quantitatively synthesise a body of studies that address the same research question. With the volume of research growing exponentially every year, conducting meta-analyses can be costly and inefficient, as a significant amount of time and human efforts needs to be spent in finding studies meeting research criteria, annotating them, and properly performing the statistical analyses to summarise the findings. In this work, we show these issues can be tackled with semantic representations and technologies, using a social science scenario as case-study. We show how the domain-specific content of research outputs can be represented and used to facilitate their search, analysis and synthesis. We present the very first representation of the domain of human cooperation, and the application we built on top of this to help experts in performing meta-analyses semi-automatically. Using few application scenarios, we show how our approach supports the various phases meta-analyses, and more in general contributes towards research replication and automated hypotheses generation.

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“…Our work, in particular, builds upon these existing representations in order to provide a multilayered view of a data journey allowing different levels of abstraction to sit alongside one another. Specifically, we build on the notion of datanode as specified in the Datanode Ontology [15], developed to express complex data pipelines to reason upon the propagation of licences and terms and conditions in distributed applications [14].…”

Section: Provenance and Provenance Representationsmentioning

confidence: 99%

“…Our methodology is based on reusing successful, relevant models, completing them with concepts derived from the data used in this work. Specifically, we reuse concepts from three approaches: the W3C Provenance Ontology PROV-O [35], the Workflow Motifs Ontology [22] and the Datanode ontology [15]. Compared to the preexisting models, DJO provides a unified view of the data journey, linking the two layers (the data flow layer and the activity layer).…”

Section: Ontologymentioning

confidence: 99%

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Data journeys: Explaining AI workflows through abstraction

Daga

Groth

2024

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Artificial intelligence systems are not simply built on a single dataset or trained model. Instead, they are made by complex data science workflows involving multiple datasets, models, preparation scripts, and algorithms. Given this complexity, in order to understand these AI systems, we need to provide explanations of their functioning at higher levels of abstraction. To tackle this problem, we focus on the extraction and representation of data journeys from these workflows. A data journey is a multi-layered semantic representation of data processing activity linked to data science code and assets. We propose an ontology to capture the essential elements of a data journey and an approach to extract such data journeys. Using a corpus of Python notebooks from Kaggle, we show that we are able to capture high-level semantic data flow that is more compact than using the code structure itself. Furthermore, we show that introducing an intermediate knowledge graph representation outperforms models that rely only on the code itself. Finally, we report on a user survey to reflect on the challenges and opportunities presented by computational data journeys for explainable AI.

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

Cited by 3 publications

References 19 publications

Reasoning with data flows and policy propagation rules

Reasoning with data flows and policy propagation rules

Fostering Scientific Meta-analyses with Knowledge Graphs: A Case-Study

Data journeys: Explaining AI workflows through abstraction

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