A hybrid approach for large knowledge graphs matching

Matching large and heterogeneous Knowledge Graphs (KGs) has been a challenge in the Semantic Web research community. This work highlights a number of limitations with current matching methods, such as: (1) they are highly dependent on string-based similarity measures, and (2) they are primarily built to handle well-formed ontologies. These features make them unsuitable for large, (semi-) automatically constructed KGs with hundreds of classes and millions of instances. Such KGs share a remarkable number of complementary facts, often described using different vocabulary. Inspired by the role of instances in large-scale KGs, we propose a hybrid matching approach. Our method composes an instance-based matcher that casts the schema matching process as a two-way text classification task by exploiting instances of KG classes, and a string-based matcher. Our method is domain-independent and is able to handle KG classes with unbalanced population. Our evaluation on a real-world KG dataset shows that our method obtains the highest recall and F1 over all OAEI 2020 participants

A gold standard dataset for large knowledge graphs matching

In the last decade, a remarkable number of Knowledge Graphs (KGs) were developed, such as DBpedia, NELL and Google knowledge graph. These KGs are the core of many web-based applications such as query answering and semantic web navigation. The majority of these KGs are semi-automatically constructed, which has resulted in a significant degree of heterogeneity. KGs are highly complementary; thus, mapping them can benefit intelligent applications that require integrating different KGs such as recommendation systems and search engines. Although the problem of ontology matching has been investigated and a significant number of systems have been developed, the challenges of mapping large-scale KGs remain significant. In 2018, OAEI has introduced a specific track for KG matching systems. Nonetheless, a major limitation of the current benchmark is their lack of representation of real-world KGs. In this work we introduce a gold standard dataset for matching the schema of large, automatically constructed, less-well structured KGs based on DBpedia and NELL. We evaluate OAEI's various participating systems on this dataset, and show that matching large-scale and domain independent KGs is a more challenging task. We believe that the dataset which we make public in this work makes the largest domain-independent gold standard dataset for matching KG classes

KGMatcher results for OAEI 2021

KGMatcher is a scalable and domain independent matching tool that matches the schema (classes) of larger Knowledge Graphs by following a hybrid matching approach. KGMatcher is composed of an instance-based matcher which only uses annotated instances of knowledge graph classes to generate candidate class alignments, and a stringbased matcher. This year is the first OAEI participation of KGMatcher, and it is the best performing system on the common knowledge graph track. Although KGMatcher results are promising, further improvements of the matching techniques' and matcher combination can be introduced

A visual quality control scale for clinical arterial spin labeling images

Background: Image-quality assessment is a fundamental step before c linical evaluation of mag netic resonance images. The aim of this study was to introduce a vi sual scoring system that provides a qual ity control standard for arterial spin labeling (ASL) and that can be applied to cerebral blood flow (CBF) maps, as well as to ancillary ASL images. Methods: The proposed image quality control (QC) system had two components: (1) contrast-based QC (cQC), describing the visual contrast between anatomical structures; a nd (2) artifact-based QC (aQC), evaluating image quality of theCBFmapforthepresenceofcommontypesofartifacts. Three raters evaluated cQC an d aQC for 158 quantitative signal targeting with alternating radiofrequency labelling o f arterial regions (QUASAR) ASL scans (CBF, T1 relaxation rate, arterial blood volume, and arterial transie nt time). Spearman correlation coefficient ( r ), intraclass correlation coefficients (ICC), and receiver operating characteristic analysis were used. Results: Intra/inter-rater agreement ranged from moderate to excellent; inter-rater ICC was 0.72 for cQC, 0.60 for aQC, and 0.74 for the combined QC (cQC + aQC). Intra-rater ICC was 0.90 for cQC; 0.80 for aQC, and 0.90 for the combined QC. Strong correlations were found between aQC and CBF maps quality ( r = 0.75), and between aQC and cQC ( r = 0.70). A QC score of 18 was optimal to discriminate between high and low quality clinical scans. Conclusions: The proposed QC system provided high reproducibility and a reliable threshold for discarding low quality scans. Future research should compare this v isualQCsystemwithanautomaticQCsystem

A visual quality control scale for clinical arterial spin labeling images

BACKGROUND: Image-quality assessment is a fundamental step before clinical evaluation of magnetic resonance images. The aim of this study was to introduce a visual scoring system that provides a quality control standard for arterial spin labeling (ASL) and that can be applied to cerebral blood flow (CBF) maps, as well as to ancillary ASL images. METHODS: The proposed image quality control (QC) system had two components: (1) contrast-based QC (cQC), describing the visual contrast between anatomical structures; and (2) artifact-based QC (aQC), evaluating image quality of the CBF map for the presence of common types of artifacts. Three raters evaluated cQC and aQC for 158 quantitative signal targeting with alternating radiofrequency labelling of arterial regions (QUASAR) ASL scans (CBF, T1 relaxation rate, arterial blood volume, and arterial transient time). Spearman correlation coefficient (r), intraclass correlation coefficients (ICC), and receiver operating characteristic analysis were used. RESULTS: Intra/inter-rater agreement ranged from moderate to excellent; inter-rater ICC was 0.72 for cQC, 0.60 for aQC, and 0.74 for the combined QC (cQC + aQC). Intra-rater ICC was 0.90 for cQC; 0.80 for aQC, and 0.90 for the combined QC. Strong correlations were found between aQC and CBF maps quality (r = 0.75), and between aQC and cQC (r = 0.70). A QC score of 18 was optimal to discriminate between high and low quality clinical scans. CONCLUSIONS: The proposed QC system provided high reproducibility and a reliable threshold for discarding low quality scans. Future research should compare this visual QC system with an automatic QC system

Results of the Ontology Alignment Evaluation Initiative 2021

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 (e.g., blind evaluation, open evaluation, or consensus). The OAEI 2021 campaign offered 13 tracks and was attended by 21 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