The amount of data being generated in recent years has increased drastically. Thus, a unified schema must be defined to bring multiple data sources into a single format. For that reason, the use of knowledge graphs has become much more commonplace. When creating a knowledge graph, different parameters affect the creation process, like the size and heterogeneity of the input data and the complexity of the input mapping. Multiple knowledge graph creation engines have been developed that handle these parameters differently. Therefore, a benchmark is needed to be defined to evaluate the performance of these engines. KGCW 2023 Challenge dataset presents a wide array of test cases to discover each engine's strengths and weaknesses and determine which engine is best suited for each case. This work reports the results of evaluating the performance of SDM-RDFizer while using this dataset.

Knowledge Graph Creation Challenge: Results for SDM-RDFizer

This is the report of our participation in the KGCW2023 Challenge @ ESWC 2023 with our RDFProcessing-
Toolkit/Sansa system which won the “fastest” tool award. The challenge was about the construction of
RDF knowledge graphs from RML specifications with varying complexity in regard to the mix of input
formats, characteristics of the data and the needed join operations. We detail how we integrated our
tool into the provided benchmark framework. Thereby we also report on the issues and shortcomings
we encountered as a base for future improvements. Furthermore, we provide an analysis of the data
measured with the benchmark framework.

KGCW2023 Challenge Report RDFProcessingToolkit / Sansa

Knowledge graph construction of heterogeneous data has seen a lot of uptake in the last decade from compliance to performance optimizations with respect to execution time. Besides execution time as metric for comparing knowledge graph construction, other metrics, e.g. CPU or memory usage, are often not considered. The Knowledge Graph Construction Workshop (KGCW) 2023 Challenge aims to be a competitive challenge for knowledge graph construction systems to encourage optimizations for execution time, CPU, and memory usage. We participated in this challenge with RMLStreamer, an RML mapping engine which processes all data in a streaming fashion. As the second part of the challenge is based on the Madrid-GTFS-Bench, which cannot be handled by RMLStreamer, we added RMLLooseGenerator as a first step for those experiments. RMLLooseGenerator is a proof-of-concept implementation that simulates the effect of using reference conditions in RML mapping rules. In previous work we showed that using reference conditions in the GTFS-Madrid-Bench mapping file results in exactly the same graph output. The challenge results show that RMLStreamer has a very scalable performance on execution time and CPU usage, while maintaining a constant memory usage. Therefore it received the Scalability Award in the KGCW 2023 Challenge. The challenge also highlighted some weaknesses of RMLStreamer: no support for relational databases, inefficient implementation of join operations, and longer execution time when handling nested sources such a JSON and XML files. After the challenge RMLStreamer has been expanded with support for relational databases. For future work we will research how to further optimize the handling of joins and of nested sources.

RMLStreamer with Reference Conditions in the KGCW Challenge 2023

Existing knowledge graph construction mapping languages have a legacy of mapping over relational databases. A such, current mapping language join constructs conflate securing referential integrity with relating concepts across data sources. This leads to a significant amount of operations (resulting in performance bottlenecks), and loss of additional context of potential linking triples that are not generated due to a lack of referential integrity. We propose a reference condition next to the traditional join condition, allowing to express a relation between sources without imposing any referential integrity.
In this short research paper, we describe the concept, its applicability, how it could be integrated in existing and future mapping languages, and a proof-of-concept implementation. Our evaluation based on GTFS-Madrid-Bench confirms the assumption that removing these integrity checks leads to much faster generation times, but we also find that using reference conditions results in exactly the same graph output, i.e. these alternative semantics do not influence generation results for cases where referential integrity is assumed within the source system. Adding the reference conditions keeps the best of all worlds: generation time is shortened where possible, you have more context in the resulting RDF graph, and the mapping file still supplies relevant metadata about relations between triples maps. For future work, we will further research relations between triples maps, and expand our implementation to more complex reference conditions. This will allow us to investigate similar performance gains with other benchmarks and other mapping engines.

Reference conditions: relating mapping rules without joining

In a multi-stakeholder ecosystem, data exchange is not sufficient and semantic interoperability should be achieved to ensure different information systems can communicate without loss of meaning. A semantic conversion procedure relying on a reference ontology can enable an efficient conversion between data formats sharing common semantics, whilst generating an integrated and interoperable knowledge graph. The Chimera framework proposes a flexible and configurable solution to address different requirements for the integration of semantic converters across heterogeneous systems. Chimera minimises the effort required to specify custom semantic data transformation pipelines, offering different ready-to-use components to integrate heterogeneous data sources, manipulate knowledge graphs, and execute declarative mapping rules for their construction and exploitation. We present the Chimera framework as a resource for the Semantic Web community and we demonstrate its usage considering a challenging use case in the transportation domain.

Composable Semantic Data Transformation Pipelines with Chimera

Approaches for the construction of knowledge graphs from heterogeneous data sources range from
ad-hoc scripts to dedicated mapping languages. Two common foundations are thereby RML and SPARQL.
So far, both approaches are treated as different: On the one hand there are tools specifically for processing
RML whereas on the other hand there are tools that extend SPARQL in order to incorporate additional
data sources. In this work, we first show how this gap can be bridged by translating RML to a sequence
of SPARQL CONSTRUCT queries and introduce the necessary SPARQL extensions. In a subsequent step,
we employ techniques to optimize SPARQL query workloads as well as individual query execution times
in order to obtain an optimized sequence of queries w.r.t. order and uniqueness of the generated triples.
Finally, we present a corresponding SPARQL query execution engine based on the Apache Spark Big
Data framework. In our evaluation on benchmarks we show that our approach is capable of achieving
RML mapping execution performance that surpasses the current state of the art.

Scaling RML and SPARQL-based Knowledge Graph Construction with Apache Spark

Knowledge graphs are often constructed from heterogeneous data sources using declarative mapping languages. Mapping languages define rules that apply ontology terms to raw data to describe how a knowledge graph should be constructed from these raw data. While most mapping languages and systems support knowledge graph construction from different data formats, e.g., CSV, XML or JSON, and different types of data sources, e.g., files, Web APIs or databases, there is still no support for mapping in-memory data structures to knowledge graphs, i.e. data which is temporarily stored in RAM. Currently, this data must first be stored in HDD, locally or in the cloud, for RDF construction systems to access them and construct a knowledge graph. However, writing these data to HDD and reading from HDD is a computationally expensive and redundant task. In this paper, we propose a method to construct RDF graphs from data produced by a software process and stored in RAM. We introduce an extension of RML’s Logical Source to describe data structures produced by software, and exemplify our proposal with Python data structures. We extend a well-known RML system, Morph-KGC, to show the feasibility of our method and validate this extension with two use cases: OpenML, which translates machine learning executions into RDF, and SOMEF, which extracts software metadata from its documentation, converting them to triples. This proposal simplifies the construction of RDF graphs from in-memory data structures stored temporarily in RAM and enables the integration of data stored both in RAM and HDD.



Declarative RDF construction from in-memory data structures with RML

As the use of sensitive data becomes increasingly prevalent, it is essential to ensure that privacy preserving technologies are effectively utilized to protect such data. Relational databases are commonly used for data storage, but they may not provide sufficient insights for identifying privacy vulnerabilities. Moreover, due to the various legal and technical terms, as well as the various actors involved, it is difficult to decide the privacy-preserving technology and the type of the configuration needed for a specific dataset. This short paper presents work in progress towards adding a semantic layer on top of structured data sources for efficient and intelligent use of data in privacy-preserving scenarios. More specifically, we present key research directions for the development of SemCrypt, a novel framework for schema-enrichment through semantic annotations and mappings to Knowledge Bases and domain ontologies so as to: a) interlink and contextually enrich schemata and data in an interoperable manner; b) use the underlying semantics to assist data owners in assessing privacy preserving technologies depending on the sensitivity of data in different use cases, such as in health, finance and cyber threat intelligence.

Towards Semantic Interpretation of Structured Data Sources in Privacy-Preserving Environments

Knowledge graphs are frequently built using declarative rules to bridge diverse data sources to a desired ontology and materialise them as RDF. The materialisation of the full knowledge graph may be a complex task when these data sources are extensive, making it unsuitable for an "on-demand" materialisation. In this paper, we present a methodology on how to map Public Procurement Data from the Tenders Electronic Daily website of the European Union by using RML, based on a innovative idea of mapping partitions. We map the aforementioned data into the eProcurement Ontology, which is a popular ontology when it comes to representing public procurement data. We also provide a method of evaluating the quality of the mapped data by using a mechanism that produces SPARQL queries based on the conceptual mapping of the Tenders Electronic Daily website data into the eProcurement Ontology. We then give an empirical evaluation over the quality of the produced data, and provide a detailed discussion on what the method presented in this paper has to offer.


Towards a Mapping Framework for the Tenders Electronic Daily Standard Forms

We propose how to construct knowledge graphs using a method based on user requirements formulated as competency questions. It suggests to formalize the competency questions as SPARQL queries wrapped as SHACL constraints allowing them to be used as automated tests. The method defines a process to guide knowledge graph construction by feedback from tests. It aims to reduce the engineering effort required to construct a knowledge graph that meets the requirements, while assessing the quality of semantic artifacts produced in this effort. We intend the method to provide a solid engineering basis for knowledge graph construction that uses the lessons learnt from software development and is built on open standards. We demonstrate using the method to construct a knowledge graph about antigen covid tests and reflect on the proposed method.

Test-driven knowledge graph construction

To monitor complex systems, such as telecommunication and computer networks, interconnecting heterogeneous data with shared definitions is necessary for efficient interpretation of events and incidents. Semantic Web technologies are essential in this context, as they address the problems of data heterogeneity, knowledge sharing and logical/probabilistic reasoning. Well-established Network Monitoring Systems (NMSs) and Security Information and Event Management systems (SIEMs) do not explicitly use Semantic Web knowledge representation, however. To fill this gap, we propose an end-to-end data processing architecture that combines NMSs/SIEMs design patterns with Semantic Web tools. The platform features batch/stream processing, declarative data mapping with RML, data patching & reconciliation with SPARQL queries and SKOS, provenance auditability with centralized configuration and data management, and semantic data transfer with Kafka. The proposed architecture has been instantiated and tested in an industrial setting.

Designing NORIA: a Knowledge Graph-based Platform for Anomaly Detection and Incident Management in ICT Systems

Metadata is used to describe data. 
It includes information about the who, when, where, how, and why of data collection.
Ideally, it should be in a machine-understandable format like RDF. 
This enables data queries using structured query languages like SPARQL and empowers further data usage.
In this paper, we investigate metadata as a source for generating Knowledge Graphs (KGs). 
We introduce a semi-automated approach that transforms raw metadata files into a KG. 
We develop the Biodiversity Metadata Ontology (BMO) as an underlying schema for our technique. 
We auto-populate the constructed ontology with instances from several metadata files as a unified KG. 
Finally, we discuss the common obstacles that face such a transformation procedure. 
Our results show that metadata files are a promising source for KG construction. 
In addition, our resources and code are publicly available (https://github.com/fusion-jena/Meta2KG).

Meta2KG: An Embeddings-based Approach for Transforming Metadata to Knowledge Graphs

A significant proportion of Linked data (LD) is created through mapping of data from a variety of sources of data. LD has been described as highly dynamic in nature with source data being continuously changed, which could impact the quality of the LD data and related mapping artefacts. Changes which have occurred in the source data of the LD datasets should be propagated into the resulting dataset to provide an accurate representation of the underlying data sources. These changes can occur at an extremely fast rate which can results in difficulties propagating each change in a timely manner. Surprisingly, despite the growth of linked data publication on the web of data, there exists no standard to address the dynamics of the data. An approach which captures changes in the source data used by mapping artefacts to create linked data datasets will help to address the dynamics involved in the publication process. Furthermore, capturing changes in a machine-readable format will allow software agents to automatically process them and take appropriate actions to preserve the alignment between mapping artefacts and data sources used to create the LD dataset. Moreover, the ability to monitor the source data and detect changes regularly will support a mechanism to automatically send notifications of changes and potential alignment issues to data producers, therefore, providing necessary information to guide them in improving alignment. Evaluating an approach designed to address the dynamics of linked data is important to provide evidence of sufficient usability. This paper describes the evaluation of the Mapping Quality Improvement (MQI) Framework and focuses on change detection of source data used to create linked data and aims to support data producers in providing timely data to consumers and improving the quality, maintenance and reuse of related mapping artefacts. The evaluation of the MQI framework involved 55 participants with varying levels of background knowledge. 

ESWC 2023 Workshop KGCW Submissions