KGvec2go – Knowledge graph embeddings as a service

In this paper, we present KGvec2go, a Web API for accessing and consuming graph embeddings in a light-weight fashion in downstream applications. Currently, we serve pre-trained embeddings for four knowledge graphs. We introduce the service and its usage, and we show further that the trained models have semantic value by evaluating them on multiple semantic benchmarks. The evaluation also reveals that the combination of multiple models can lead to a better outcome than the best individual model.Comment: to be published in the Proceedings of the International Conference on Language Resources and Evaluation (LREC) 202

ALOD2Vec Matcher results for OAEI 2020

This paper presents the results of the ALOD2Vec Matcher in the Ontology Alignment Evaluation Initiative(OAEI) 2020. The matching system exploits a Web-scale dataset, i.e.WebIsALOD, as background knowledge source. In order to make use of the dataset, the RDF2Vec approach is applied to derive embeddings for each concept available in the dataset. ALOD2Vec Matcher participated in the OAEI 2018 campaign before. This is the system’s second participation. The matching system has been extended, improved, and achieves better results this year

ALOD2vec matcher results for OAEI 2021

This paper presents the results of the ALOD2vec Matcher in the Ontology Alignment Evaluation Initiative (OAEI) 2021. The matching system exploits a Web-scale dataset, i.e. WebIsALOD, as background knowledge source. In order to make use of the dataset, the RDF2vec approach is applied to derive embeddings for each concept available in the dataset. ALOD2vec Matcher participated in the OAEI 2018 and 2020 campaigns before. This is the system’s third participation

Entity Type Prediction Leveraging Graph Walks and Entity Descriptions

The entity type information in Knowledge Graphs (KGs) such as DBpedia, Freebase, etc. is often incomplete due to automated generation or human curation. Entity typing is the task of assigning or inferring the semantic type of an entity in a KG. This paper presents \textit{GRAND}, a novel approach for entity typing leveraging different graph walk strategies in RDF2vec together with textual entity descriptions. RDF2vec first generates graph walks and then uses a language model to obtain embeddings for each node in the graph. This study shows that the walk generation strategy and the embedding model have a significant effect on the performance of the entity typing task. The proposed approach outperforms the baseline approaches on the benchmark datasets DBpedia and FIGER for entity typing in KGs for both fine-grained and coarse-grained classes. The results show that the combination of order-aware RDF2vec variants together with the contextual embeddings of the textual entity descriptions achieve the best results

Exploiting general-purpose background knowledge for automated schema matching

The schema matching task is an integral part of the data integration process. It is usually the first step in integrating data. Schema matching is typically very complex and time-consuming. It is, therefore, to the largest part, carried out by humans. One reason for the low amount of automation is the fact that schemas are often defined with deep background knowledge that is not itself present within the schemas. Overcoming the problem of missing background knowledge is a core challenge in automating the data integration process. In this dissertation, the task of matching semantic models, so-called ontologies, with the help of external background knowledge is investigated in-depth in Part I. Throughout this thesis, the focus lies on large, general-purpose resources since domain-specific resources are rarely available for most domains. Besides new knowledge resources, this thesis also explores new strategies to exploit such resources. A technical base for the development and comparison of matching systems is presented in Part II. The framework introduced here allows for simple and modularized matcher development (with background knowledge sources) and for extensive evaluations of matching systems. One of the largest structured sources for general-purpose background knowledge are knowledge graphs which have grown significantly in size in recent years. However, exploiting such graphs is not trivial. In Part III, knowledge graph em- beddings are explored, analyzed, and compared. Multiple improvements to existing approaches are presented. In Part IV, numerous concrete matching systems which exploit general-purpose background knowledge are presented. Furthermore, exploitation strategies and resources are analyzed and compared. This dissertation closes with a perspective on real-world applications

More is not Always Better: The Negative Impact of A-box Materialization on RDF2vec Knowledge Graph Embeddings

RDF2vec is an embedding technique for representing knowledge graph entities in a continuous vector space. In this paper, we investigate the effect of materializing implicit A-box axioms induced by subproperties, as well as symmetric and transitive properties. While it might be a reasonable assumption that such a materialization before computing embeddings might lead to better embeddings, we conduct a set of experiments on DBpedia which demonstrate that the materialization actually has a negative effect on the performance of RDF2vec. In our analysis, we argue that despite the huge body of work devoted on completing missing information in knowledge graphs, such missing implicit information is actually a signal, not a defect, and we show examples illustrating that assumption.Comment: Accepted at the Workshop on Combining Symbolic and Sub-symbolic methods and their Applications (CSSA 2020

Neuro-symbolic learning for dealing with sparsity in cultural heritage image archives: an empirical journey

Deep Learning (DL) methods have proved to be very successful for many image classification tasks. In the SPICE project, we are researching on an intelligent system that classifies artworks to support several tasks such as metadata curation and linking across image collections. However, applying DL methods to real-world cultural heritage collections for the task of artwork subject classification is problematic. Objects in this domain are characterised by different levels of heterogeneity: of media and techniques, of categories, of time-periods, just to mention a few. This heterogeneity makes the related training features sparsely distributed. In this paper, we report on an empirical investigation where we apply neuro-symbolic, Deep Learning techniques to a paradigmatic case of cultural heritage archive: the Tate Gallery collection open data. We pose the question of what type of feature engineering could help in reducing the impact of data sparsity in this domain. Crucially, we explore how neuro-symbolic learning, combining image features, textual metadata, and Knowledge Graph embeddings, could help in mitigating the problems derived from data sparsity in cultural heritage image archives