SeMi: A SEmantic Modeling machIne to build Knowledge Graphs with graph neural networks

Futia, Giuseppe; Vetrò, Antonio; Martin, Juan Carlos De

doi:10.1016/j.softx.2020.100516

Cited by 18 publications

(6 citation statements)

References 27 publications

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“…In order to test a semantic modeling approach on VC-SLAM, we used the approach by Futia et al [27] that is available online 11 . Since we only intended to test the semantic relation inference, we presumed existing labels.…”

Section: Vc-slam For Semantic Modelingmentioning

confidence: 99%

VC-SLAM—A Handcrafted Data Corpus for the Construction of Semantic Models

Burgdorf

Paulus

Pomp

et al. 2022

Data

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Ontology-based data management and knowledge graphs have emerged in recent years as efficient approaches for managing and utilizing diverse and large data sets. In this regard, research on algorithms for automatic semantic labeling and modeling as a prerequisite for both has made steady progress in the form of new approaches. The range of algorithms varies in the type of information used (data schema, values, or metadata), as well as in the underlying methodology (e.g., use of different machine learning methods or external knowledge bases). Approaches that have been established over the years, however, still come with various weaknesses. Most approaches are evaluated on few small data corpora specific to the approach. This reduces comparability and also limits statements for the general applicability and performance of those approaches. Other research areas, such as computer vision or natural language processing solve this problem by providing unified data corpora for the evaluation of specific algorithms and tasks. In this paper, we present and publish VC-SLAM to lay the necessary foundation for future research. This corpus allows the evaluation and comparison of semantic labeling and modeling approaches across different methodologies, and it is the first corpus that additionally allows to leverage textual data documentations for semantic labeling and modeling. Each of the contained 101 data sets consists of labels, data and metadata, as well as corresponding semantic labels and a semantic model that were manually created by human experts using an ontology that was explicitly built for the corpus. We provide statistical information about the corpus as well as a critical discussion of its strengths and shortcomings, and test the corpus with existing methods for labeling and modeling.

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Section: Vc-slam For Semantic Modelingmentioning

confidence: 99%

VC-SLAM—A Handcrafted Data Corpus for the Construction of Semantic Models

Burgdorf

Paulus

Pomp

et al. 2022

Data

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“…Their approach is more robust to noise than previous methods. Giuseppe et al proposed a semi-automatic approach for inferring semantic relations based on a graph neural network trained on a background knowledge graph [12]. In our article, we learned a semantic model for a new source by integrating the knowledge of known semantic mappings, knowledge graphs, and domain ontology.…”

Section: Related Workmentioning

confidence: 99%

“…Relationships among semantic models of a data source can be inferred by exploiting a knowledge graph as prior knowledge [11]. For example, Giuseppa et al developed a semi-automatic tool SeMi for constructing large-scale knowledge graphs from structured data sources by building the semantic models of data sources [12].…”

Section: Introductionmentioning

confidence: 99%

Automatic Semantic Modeling for Structural Data Source with the Prior Knowledge from Knowledge Base

Mayer

Zhang

et al. 2022

Mathematics

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A critical step in sharing semantic content online is to map the structural data source to a public domain ontology. This problem is denoted as the Relational-To-Ontology Mapping Problem (Rel2Onto). A huge effort and expertise are required for manually modeling the semantics of data. Therefore, an automatic approach for learning the semantics of a data source is desirable. Most of the existing work studies the semantic annotation of source attributes. However, although critical, the research for automatically inferring the relationships between attributes is very limited. In this paper, we propose a novel method for semantically annotating structured data sources using machine learning, graph matching and modified frequent subgraph mining to amend the candidate model. In our work, Knowledge graph is used as prior knowledge. Our evaluation shows that our approach outperforms two state-of-the-art solutions in tricky cases where only a few semantic models are known.

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“…Today, there are different automated and semi-automated approaches to support the creation of a semantic model. An example approach was presented by Futia et al [9], who utilized link prediction algorithms to improve the generation of semantic models by adjusting the underlying graph generation algorithm based on latent node features obtained from existing semantic models.…”

Section: Introductionmentioning

confidence: 99%

An Evaluation of Link Prediction Approaches in Few-Shot Scenarios

Braken,

Paulus,

Pomp

et al. 2023

Electronics

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Semantic models are utilized to add context information to datasets and make data accessible and understandable in applications such as dataspaces. Since the creation of such models is a time-consuming task that has to be performed by a human expert, different approaches to automate or support this process exist. A recurring problem is the task of link prediction, i.e., the automatic prediction of links between nodes in a graph, in this case semantic models, usually based on machine learning techniques. While, in general, semantic models are trained and evaluated on large reference datasets, these conditions often do not match the domain-specific real-world applications wherein only a small amount of existing data is available (the cold-start problem). In this study, we evaluated the performance of link prediction algorithms when datasets of a smaller size were used for training (few-shot scenarios). Based on the reported performance evaluation, we first selected algorithms for link prediction and then evaluated the performance of the selected subset using multiple reduced datasets. The results showed that two of the three selected algorithms were suitable for the task of link prediction in few-shot scenarios.

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SeMi: A SEmantic Modeling machIne to build Knowledge Graphs with graph neural networks

Cited by 18 publications

References 27 publications

VC-SLAM—A Handcrafted Data Corpus for the Construction of Semantic Models

VC-SLAM—A Handcrafted Data Corpus for the Construction of Semantic Models

Automatic Semantic Modeling for Structural Data Source with the Prior Knowledge from Knowledge Base

An Evaluation of Link Prediction Approaches in Few-Shot Scenarios

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