Declarative RDF graph generation from heterogeneous (semi-)structured data: A systematic literature review

Assche, Dylan Van; Delva, Thomas; Haesendonck, Gerald; Heyvaert, Pieter; Meester, Ben De; Dimou, Anastasia

doi:10.1016/j.websem.2022.100753

Cited by 22 publications

(22 citation statements)

References 93 publications

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“…In a recent review of declarative RDF generation tools, Van Assche et al find that existing mapping tools execute the mapping process atomically from source to RDF [11]. This is for instance the case for the above mapping tools.…”

Section: Existing Solutionsmentioning

confidence: 99%

“…Van Assche et al find that many mapping tools have support for data transformations. These are functions that are applied to input values to transform them before they are encoded in triples [11]. The authors cite the need to extract a year from a date when the target schema requires birth year, while the input contains the birth date.…”

Section: Existing Solutionsmentioning

confidence: 99%

“…The review of mapping tools by Van Assche et al [11] found at the time that only Morph-CSV was based on Python, but it has CLI-based integration. Some of the tools described by Van Assche et al do have library-based integration, but these are written in Java, and have no Python library with bindings.…”

Section: Existing Solutionsmentioning

confidence: 99%

“…This lacking integration is likely due to the use of end-to-end declarative mappings using RML, which is a mapping language that defines end-toend transformations from sources such as databases or files to knowledge graphs [10]. RML implementations typically allow no inspection and no interactivity with intermediary results [11]. RML also does not support templates, which are important in industrial settings with a large degree of modularity [12].…”

Section: Introductionmentioning

confidence: 99%

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maplib: Interactive, Literal RDF Model Mapping for Industry

Bakken

2023

IEEE Access

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Knowledge graphs are important for industrial digitalization. Industrial knowledge graphs are often mapped from multiple existing large data sources, and creating a mapping requires the time of scarce subject matter experts (SME). Interactive, literal programming for large scale mapping would allow mapping engineers to make good use of SME time, and document their work. Currently, there are no open source tools supporting such a process. To solve this problem, we implement maplib, which leverages existing tooling from data science. In data science, there is widespread use of literate programming using frameworks such as Jupyter notebooks to interactively prepare data and create analyses using in-memory tables called DataFrames. Maplib is implemented in Rust using Polars DataFrames and has Python bindings, allowing us to leverage tooling used in data science. Maplib implements the OTTR mapping language, which is highly suited for industrial use cases. Maplib features a SPARQL engine defined directly on DataFrames, making querying possible immediately after mapping. We evaluate our approach by comparing mapping and querying performance with Morph-KGC and SPARQL Anything on the GTFS Madrid benchmark. Our approach materializes the graph and is ready to query 47x-182x faster, and scales to models that are over twice as large. Morph-KGC and SPARQL Anything perform better for most, but not all of the queries once the graph has been constructed. On the end-to-end task of mapping and querying however, which is very important for interactive mapping, maplib performs better for most queries.

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Section: Existing Solutionsmentioning

confidence: 99%

Section: Existing Solutionsmentioning

confidence: 99%

Section: Existing Solutionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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maplib: Interactive, Literal RDF Model Mapping for Industry

Bakken

2023

IEEE Access

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“…Furthermore, ref. [30] surveys techniques and systems for querying RDF datasets, by mainly focusing on storage, indexing and query processing techniques for evaluating SPARQL queries, while [31] surveys RDF graph generation approaches from heterogeneous data, by focusing on existing mapping languages for schema and data transformations. Moreover, ref.…”

Section: Related Work: Past Surveysmentioning

confidence: 99%

A Brief Survey of Methods for Analytics over RDF Knowledge Graphs

Papadaki

Tzitzikas²,

Mountantonakis³

2023

Analytics

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There are several Knowledge Graphs expressed in RDF (Resource Description Framework) that aggregate/integrate data from various sources for providing unified access services and enabling insightful analytics. We observe this trend in almost every domain of our life. However, the provision of effective, efficient, and user-friendly analytic services and systems is quite challenging. In this paper we survey the approaches, systems and tools that enable the formulation of analytic queries over KGs expressed in RDF. We identify the main challenges, we distinguish two main categories of analytic queries (domain specific and quality-related), and five kinds of approaches for analytics over RDF. Then, we describe in brief the works of each category and related aspects, like efficiency and visualization. We hope this collection to be useful for researchers and engineers for advancing the capabilities and user-friendliness of methods for analytics over knowledge graphs.

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