Tracing the triumphs and challenges of two decades of Semantic Web research and applications

https://dl.acm.org/doi/pdf/10.1145/3397512

A review of the semantic web field

Tabular data to Knowledge Graph matching is the process of assigning semantic tags from knowledge graphs (e.g., Wikidata or DBpedia) to the elements of a table. This task is a challenging problem for various reasons, including the lack of metadata (e.g., table and column names), the noisiness, heterogeneity, incompleteness and ambiguity in the data. The results of this task provide significant insights about potentially highly valuable tabular data, as recent works have shown, enabling a new family of data analytics and data science applications. Despite significant amount of work on various flavors of this problem, there is a lack of a common framework to conduct a systematic evaluation of state-of-the-art systems. The creation of the Semantic Web Challenge on Tabular Data to Knowledge Graph Matching (SemTab) aims at filling this gap. In this paper, we report about the datasets, infrastructure and lessons learned from the first edition of the SemTab challenge.

/pdf/semtab-2019-resources-to-benchmark-tabular-data-to-knowledge-2r3azgwpw2.pdf

SemTab 2019: Resources to Benchmark Tabular Data to Knowledge Graph Matching Systems

Simple ontology alignments, largely studied in the literature, link a single entity of a source ontology to a single entity of a target ontology. One of the limitations of these alignments is, however, their lack of expressiveness which can be overcome by complex alignments. While diverse state-of-the-art surveys mainly review the matching approaches in general, to the best of our knowledge, there is no study about the specificities of the complex matching problem. In this paper, an overview of the different complex matching approaches is provided. It proposes a classification of the complex matching approaches based on their specificities (i.e., type of correspondences, guiding structure). The evaluation aspects and the limitations of these approaches are also discussed. Insights for future work in the field are provided.

Survey on complex ontology matching

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 (from simple thesauri to expressive OWL ontologies) and use different evaluation modalities (e.g., blind evaluation, open evaluation, or consensus). The OAEI 2019 campaign offered 11 tracks with 29 test cases, and was attended by 20 participants. This paper is an overall presentation of that campaign.

/pdf/results-of-the-ontology-alignment-evaluation-initiative-2019-51aj5u5pwy.pdf

Results of the Ontology Alignment Evaluation Initiative 2019

Ontology mapping for semantically enabled applications

Simple ontology alignments, largely studied, link one entity of a source ontology to one entity of a target ontology. One of the limitations of these alignments is, however, their lack of expressiveness which can be overcome by complex alignments. Although different complex matching approaches have emerged in the literature, there is a lack of complex reference alignments on which these approaches can be systematically evaluated. This paper proposes two sets of complex alignments between 10 pairs of ontologies from the well-known OAEI conference simple alignment dataset.

/pdf/task-oriented-complex-ontology-alignment-two-alignment-1cv64pzxx2.pdf

Task-Oriented Complex Ontology Alignment: Two Alignment Evaluation Sets

We present the first version of the complex benchmark of the Ontology Alignment Evaluation Initiative campaigns. This benchmark is composed of four datasets from different domains (conference, hydrology, geoscience and agronomy) and covers different evaluation strategies.

/pdf/the-first-version-of-the-oaei-complex-alignment-benchmark-4t1ljui5ar.pdf

The First Version of the OAEI Complex Alignment Benchmark

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 (from simple thesauri to expressive OWL ontologies) and use different evaluation modalities (e.g., blind evaluation, open evaluation, or consensus). The OAEI 2018 campaign offered 12 tracks with 23 test cases, and was attended by 19 participants. This paper is an overall presentation of that campaign.

/pdf/results-of-the-ontology-alignment-evaluation-initiative-2018-2zt4qgg9b4.pdf

Elodie Thiéblin

Papers

Survey on complex ontology matching

Results of the Ontology Alignment Evaluation Initiative 2019

Task-Oriented Complex Ontology Alignment: Two Alignment Evaluation Sets

The First Version of the OAEI Complex Alignment Benchmark

Results of the Ontology Alignment Evaluation Initiative 2018