Parallel RDF generation from heterogeneous big data

Haesendonck, Gerald; Maroy, Wouter; Heyvaert, Pieter; Verborgh, Ruben; Dimou, Anastasia

doi:10.1145/3323878.3325802

Cited by 18 publications

(24 citation statements)

References 6 publications

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“…This is achieved by aligning the mapping rules with access-specific definitions for e.g., databases or files, to integrate data from heterogeneous formats, e.g., CSV, XML, JSON. However, we observe that: (i) data velocity is not well supported in mapping languages and corresponding processors, compared to data variety and volume [8,18,20]; and, (ii) the characteristics and destination of the generated knowledge graph remain unexplored.…”

Section: Introductionmentioning

confidence: 99%

Leveraging Web of Things W3C Recommendations for Knowledge Graphs Generation

Assche

Haesendonck

Mulder

et al. 2021

Lecture Notes in Computer Science

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Constructing a knowledge graph with mapping languages, such as RML or SPARQL-Generate, allows seamlessly integrating heterogeneous data by defining access-specific definitions for e.g., databases or files. However, such mapping languages have limited support for describing Web APIs and no support for describing data with varying velocities, as needed for e.g., streams, neither for the input data nor for the output RDF. This hampers the smooth and reproducible generation of knowledge graphs from heterogeneous data and their continuous integration for consumption since each implementation provides its own extensions. Recently, the Web of Things (WoT) Working Group released a set of recommendations to provide a machine-readable description of metadata and network-facing interfaces for Web APIs and streams. In this paper, we investigated (i) how mapping languages can be aligned with the newly specified recommendations to describe and handle heterogeneous data with varying velocities and Web APIs, and (ii) how such descriptions can be used to indicate how the generated knowledge graph should be exported. We extended RML's Logical Source to support WoT descriptions of Web APIs and streams, and introduced RML's Logical Target to describe the generated knowledge graph reusing the same descriptions. We implemented these extensions in the RMLMapper and RMLStreamer, and validated our approach in two use cases. Mapping languages are now able to use the same descriptions to define the input data but also the output RDF. This way, our work paves the way towards more reproducible workflows for knowledge graph generation.

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Section: Introductionmentioning

confidence: 99%

Leveraging Web of Things W3C Recommendations for Knowledge Graphs Generation

Assche

Haesendonck

Mulder

et al. 2021

Lecture Notes in Computer Science

Self Cite

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show abstract

“…Tools and mappings for different formats have been developed (e.g. [25,26,13]) and the mapping languages have been applied to convert data sources in different domains (e.g. [8,18]).…”

Section: Related Workmentioning

confidence: 99%

“…This method helps in correcting the tendency of decision trees to overfit their training set. We use the scikit-learn implementation 13 of the random forest classifier.…”

Section: Datatype Property Classification Modelmentioning

confidence: 99%

Extracting data models from background knowledge graphs

Oliveira

d’Aquin

2022

Knowledge-Based Systems

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Knowledge Graphs have emerged as a core technology to aggregate and publish knowledge on the Web. However, integrating knowledge from different sources, not specifically designed to be interoperable, is not a trivial task. Finding the right ontologies to model a dataset is a challenge since several valid data models exist and there is no clear agreement between them. In this paper, we propose to facilitate the selection of a data model with the RICDaM (Recommending Interoperable and Consistent Data Models) framework. RICDaM generates and ranks candidates that match entity types and properties in an input dataset. These candidates are obtained by aggregating freely available domain RDF datasets in a knowledge graph and then enriching the relationships between the graph's entities. The entity type and object property candidates are obtained by exploiting the instances and structure of this knowledge graph to compute a score that considers both the accuracy and interoperability of the candidates. Datatype properties are predicted with a random forest model, trained on the knowledge graph properties and their values, so to make predictions on candidate properties and rank them according to different measures. We present experiments using multiple datasets from the library domain as a use case and show that our methodology can produce meaningful candidate data models, adaptable to specific scenarios and needs.

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“…We selected our in-house RMLStreamer [45] for this component, which is a streaming implementation of the RMLMapper [46], a tool that executes RML (RDF Mapping Language [47]) mappings. Using RML, we can define a mapping from various common input formats, including JSON, XML, or CSV, to a semantic format.…”

Section: Semantic Conversion: Rmlmentioning

confidence: 99%

A Complete Software Stack for IoT Time-Series Analysis that Combines Semantics and Machine Learning—Lessons Learned from the Dyversify Project

et al. 2021

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Companies are increasingly gathering and analyzing time-series data, driven by the rising number of IoT devices. Many works in literature describe analysis systems built using either data-driven or semantic (knowledge-driven) techniques. However, little to no works describe hybrid combinations of these two. Dyversify, a collaborative project between industry and academia, investigated how event and anomaly detection can be performed on time-series data in such a hybrid setting. We built a proof-of-concept analysis platform, using a microservice architecture to ensure scalability and fault-tolerance. The platform comprises time-series ingestion, long term storage, data semantification, event detection using data-driven and semantic techniques, dynamic visualization, and user feedback. In this work, we describe the system architecture of this hybrid analysis platform and give an overview of the different components and their interactions. As such, the main contribution of this work is an experience report with challenges faced and lessons learned.

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Parallel RDF generation from heterogeneous big data

Cited by 18 publications

References 6 publications

Leveraging Web of Things W3C Recommendations for Knowledge Graphs Generation

Leveraging Web of Things W3C Recommendations for Knowledge Graphs Generation

Extracting data models from background knowledge graphs

A Complete Software Stack for IoT Time-Series Analysis that Combines Semantics and Machine Learning—Lessons Learned from the Dyversify Project

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