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

Evaluating the exit pressure method for measurements of normal stress difference at high shear rates

A challenge for polymer rheology is the reliable determination of shear dependent first normal stress difference (N-1 values) at high shear rates (>10 s(-1)). Here, we evaluate the correctness of the commonly applied exit pressure method focusing on polypropylene and high and low density polyethylene melts at 200 degrees C. It is demonstrated that the linear extrapolation of pressure values toward the die exit, which is a key step in the application of the exit pressure method, is affordable to determine N-1 values despite that these extrapolated exit pressure values are characterized by a relative deviation of 25%-40%. The validity of the exit pressure method is further supported by an excellent match with rheological data from the Laun rule (exponent close to 0.7) and a representative simulation of extrudate swelling data in the width and height direction, considering tuned parameters for the Phan-Thien-Tanner constitutive model. Also, the absence of a significant viscous heating effect near the die exit is highlighted based on numerical analysis. (c) 2020 The Society of Rheology

Machine Learning-Friendly Biomedical Datasets for Equivalence and Subsumption Ontology Matching

Ontology Matching (OM) plays an important role in many domains such as bioinformatics and the Semantic Web, and its research is becoming increasingly popular, especially with the application of machine learning (ML) techniques. Although the Ontology Alignment Evaluation Initiative (OAEI) represents an impressive effort for the systematic evaluation of OM systems, it still suffers from several limitations including limited evaluation of subsumption mappings, suboptimal reference mappings, and limited support for the evaluation of ML-based systems. To tackle these limitations, we introduce five new biomedical OM tasks involving ontologies extracted from Mondo and UMLS. Each task includes both equivalence and subsumption matching; the quality of reference mappings is ensured by human curation, ontology pruning, etc.; and a comprehensive evaluation framework is proposed to measure OM performance from various perspectives for both ML-based and non-ML-based OM systems. We report evaluation results for OM systems of different types to demonstrate the usage of these resources, all of which are publicly available as part of the new BioML track at OAEI 2022.Comment: Accepted paper in the 21st International Semantic Web Conference (ISWC-2022); DOI for Bio-ML Dataset: 10.5281/zenodo.651008

A Simple Standard for Sharing Ontological Mappings (SSSOM).

Despite progress in the development of standards for describing and exchanging scientific information, the lack of easy-to-use standards for mapping between different representations of the same or similar objects in different databases poses a major impediment to data integration and interoperability. Mappings often lack the metadata needed to be correctly interpreted and applied. For example, are two terms equivalent or merely related? Are they narrow or broad matches? Or are they associated in some other way? Such relationships between the mapped terms are often not documented, which leads to incorrect assumptions and makes them hard to use in scenarios that require a high degree of precision (such as diagnostics or risk prediction). Furthermore, the lack of descriptions of how mappings were done makes it hard to combine and reconcile mappings, particularly curated and automated ones. We have developed the Simple Standard for Sharing Ontological Mappings (SSSOM) which addresses these problems by: (i) Introducing a machine-readable and extensible vocabulary to describe metadata that makes imprecision, inaccuracy and incompleteness in mappings explicit. (ii) Defining an easy-to-use simple table-based format that can be integrated into existing data science pipelines without the need to parse or query ontologies, and that integrates seamlessly with Linked Data principles. (iii) Implementing open and community-driven collaborative workflows that are designed to evolve the standard continuously to address changing requirements and mapping practices. (iv) Providing reference tools and software libraries for working with the standard. In this paper, we present the SSSOM standard, describe several use cases in detail and survey some of the existing work on standardizing the exchange of mappings, with the goal of making mappings Findable, Accessible, Interoperable and Reusable (FAIR). The SSSOM specification can be found at http://w3id.org/sssom/spec. Database URL: http://w3id.org/sssom/spec

Results of the ontology alignment evaluation initiative 2019

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

How Can I Best Support Classroom Teachers With Their Use And Understanding Of Tier Two Interventions?

The research question addressed in this study was, how can I best support classroom teachers with their use and understanding of Tier Two interventions? It documents one teacher’s use of qualitative research to create a Tier Two intervention toolkit for classroom teachers. The author documents what the RtI process looks like at each tier and how to implement the Tier Two interventions into the classroom. She also created the toolkit digitally to help teacher access the interventions quickly and easily put them into practice. Each intervention can easily be found by clicking on the link on each skill page. In addition to the links, the author added word lists to assist teachers as they teach a phonics skill