Supervised ontology and instance matching with MELT

In this paper, we present MELT-ML, a machine learning extension to the Matching and EvaLuation Toolkit (MELT) which facilitates the application of supervised learning for ontology and instance matching. Our contributions are twofold: We present an open source machine learning extension to the matching toolkit as well as two supervised learning use cases demonstrating the capabilities of the new extension

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

DESKMatcher

This paper describes DESKMatcher, a label-based ontology matcher. It utilizes background knowledge from the financial services and enterprise domain to better find matches in these domains. The background knowledge utilized for the enterprise domain was in the form of documentation of terms used in SAP software (textual). Therefore, Word2Vec and GloVewere used for these corpora. The Financial Industries Business Ontology (FIBO) was used as more specific background knowledge for the financial services domain. Vector space embeddings for this corpus were trained using RDF2Vec and KGloVe. Individual matchers utilizing one set of embeddings (generated from a combination of method and corpus) are pipelined together after string-based matchers, searching only for matches between entities that have not been assignedto a match in a previous step. Results on theOAEI tracks are expectedto be sub-par, because low overlap between corpus and task vocabulary is expected

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

Wiktionary matcher results for OAEI 2020

This paper presents the results of the Wiktionary Matcher in the Ontology Alignment Evaluation Initiative(OAEI) 2020.Wiktionary Matcher is an ontology matching tool that exploits Wiktionary as external background knowledge source. Wiktionary is a large lexical knowledge resource that is collaboratively built online. Multiple current language versions of Wiktionary are merged and used for monolingual ontology matching by exploiting synonymy relations and for multilingual matching by exploiting the translations given in the resource. This is the second OAEI participation of the matching system. Wiktionary Matcher has been improved and is the best performing system on the knowledge graph track this year

Matching with transformers in MELT

One of the strongest signals for automated matching of ontologies and knowledge graphs are the textual descriptions of the concepts. The methods that are typically applied (such as character- or token-based comparisons) are relatively simple, and therefore do not capture the actual meaning of the texts. With the rise of transformer-based language models, text comparison based on meaning (rather than lexical features) is possible. In this paper, we model the ontology matching task as classification problem and present approaches based on transformer models. We further provide an easy to use implementation in the MELT framework which is suited for ontology and knowledge graph matching. We show that a transformer-based filter helps to choose the correct correspondences given a high-recall alignment and already achieves a good result with simple alignment post-processing methods

Results of the Ontology Alignment Evaluation Initiative 2023

The Ontology Alignment Evaluation Initiative (OAEI) aims at comparing ontology matching systems on precisely defined test cases. These test cases can be based on ontologies of different levels of complexity and use different evaluation modalities. The OAEI 2023 campaign offered 15 tracks and was attended by 16 participants. This paper is an overall presentation of that campaign

Results of the Ontology Alignment Evaluation Initiative 2022

The Ontology Alignment Evaluation Initiative (OAEI) aims at comparing ontology matching systems on precisely defined test cases. These test cases can be based on ontologies of different levels of complexity and use different evaluation modalities. The OAEI 2022 campaign offered 14 tracks and was attended by 18 participants. This paper is an overall presentation of that campaign