Ontology Mapping Research Articles

An inter-frame motion solving method based on semantic assistance

Lidar and camera are the two most frequently used types of sensors in the fields of autonomous driving and mobile robots. The data fusion of ontology positioning and mapping has become a popular research direction in the field of simultaneous localization and mapping. Considering the characteristics of a planar mobile robot, this paper proposes an image semantics-based method to solve the inter-frame motion of the laser point cloud to achieve the fast real-time positioning of a mobile robot. First, the image cascade network is used to convert image samples to different resolutions, and network branches of different complexity are gradually fused into the final finer semantic segmentation result. Then, through the rapid segmentation and processing of the laser point cloud data, key points and surfels are extracted. The unified framework of semantic-assisted inter-frame motion estimation is established using semantic image data and point-cloud key-feature information. Finally, the stability of feature extraction, the accuracy of motion estimation, and the efficiency measured by calculation time are verified experimentally. The experimental results show that the standard deviation of the estimated motion is less than 0.0025, and the single operation time of the whole system is about 38 ms.

Aligning Standards Communities for Omics Biodiversity Data: Sustainable Darwin Core-MIxS Interoperability.

The standardization of data, encompassing both primary and contextual information (metadata), plays a pivotal role in facilitating data (re-)use, integration, and knowledge generation. However, the biodiversity and omics communities, converging on omics biodiversity data, have historically developed and adopted their own distinct standards, hindering effective (meta)data integration and collaboration. In response to this challenge, the Task Group (TG) for Sustainable DwC-MIxS Interoperability was established. Convening experts from the Biodiversity Information Standards (TDWG) and the Genomic Standards Consortium (GSC) alongside external stakeholders, the TG aimed to promote sustainable interoperability between the Minimum Information about any (x) Sequence (MIxS) and Darwin Core (DwC) specifications. To achieve this goal, the TG utilized the Simple Standard for Sharing Ontology Mappings (SSSOM) to create a comprehensive mapping of DwC keys to MIxS keys. This mapping, combined with the development of the MIxS-DwC extension, enables the incorporation of MIxS core terms into DwC-compliant metadata records, facilitating seamless data exchange between MIxS and DwC user communities. Through the implementation of this translation layer, data produced in either MIxS- or DwC-compliant formats can now be efficiently brokered, breaking down silos and fostering closer collaboration between the biodiversity and omics communities. To ensure its sustainability and lasting impact, TDWG and GSC have both signed a Memorandum of Understanding (MoU) on creating a continuous model to synchronize their standards. These achievements mark a significant step forward in enhancing data sharing and utilization across domains, thereby unlocking new opportunities for scientific discovery and advancement.

Combining different points of view on plant descriptions: mapping agricultural plant roles and biological taxa.

This article describes our study on the alignment of two complementary knowledge graphs useful in agriculture: the thesaurus of cultivated plants in France named French Crop Usage (FCU) and the French national taxonomic repository TAXREF for fauna, flora, and fungi. FCU describes the usages of plants in agriculture: "tomatoes" are crops used for human food, and "grapevines" are crops used for human beverage. TAXREF describes biological taxa and associated scientific names: for example, a tomato species may be "Solanum lycopersicum" or a grapevine species may be "Vitis vinifera". Both knowledge graphs contain vernacular names of plants but those names are ambiguous. Thus, a group of agricultural experts produced some mappings from FCU crops to TAXREF taxa. Moreover, new RDF properties have been defined to declare those new types of mapping relations between plant descriptions. The metadata for the mappings and the mapping set are encoded with the Simple Standard for Sharing Ontological Mappings (SSSOM), a new model which, among other qualities, offers means to report on provenance of particular interest for this study. The produced mappings are available for download in Recherche Data Gouv, the federated national platform for research data in France.

No Pain No Gain: Standards mapping in Latimer Core development

Latimer Core (LtC) is a new proposed Biodiversity Information Standards (TDWG) data standard that supports the representation and discovery of natural science collections by structuring data about the groups of objects that those collections and their subcomponents encompass (Woodburn et al. 2022). It is designed to be applicable to a range of use cases that include high level collection registries, rich textual narratives and semantic networks of collections, as well as more granular, quantitative breakdowns of collections to aid collection discovery and digitisation planning. As a standard that is (in this first version) focused on natural science collections, LtC has significant intersections with existing data standards and models (Fig. 1) that represent individual natural science objects and occurrences and their associated data (e.g., Darwin Core (DwC), Access to Biological Collection Data (ABCD), Conceptual Reference Model of the International Committee on Documentation (CIDOC-CRM)). LtC’s scope also overlaps with standards for more generic concepts like metadata, organisations, people and activities (i.e., Dublin Core, World Wide Web Consortium (W3C) ORG Ontology and PROV Ontology, Schema.org). LtC represents just an element of this extended network of data standards for the natural sciences and related concepts. Mapping between LtC and intersecting standards is therefore crucial for avoiding duplication of effort in the standard development process, and ensuring that data stored using the different standards are as interoperable as possible in alignment with FAIR (Findable, Accessible, Interoperable, Reusable) principles. In particular, it is vital to make robust associations between records representing groups of objects in LtC and records (where available) that represent the objects within those groups. During LtC development, efforts were made to identify and align with relevant standards and vocabularies, and adopt existing terms from them where possible. During expert review, a more structured approach was proposed and implemented using the Simple Knowledge Organization System (SKOS) mappingRelation vocabulary. This exercise helped to better describe the nature of the mappings between new LtC terms and related terms in other standards, and to validate decisions around the borrowing of existing terms for LtC. A further exercise also used elements of the Simple Standard for Sharing Ontological Mappings (SSSOM) to start to develop a more comprehensive set of metadata around these mappings. At present, these mappings (Suppl. material 1 and Suppl. material 2) are provisional and not considered to be comprehensive, but should be further refined and expanded over time. Even with the support provided by the SKOS and SSSOM standards, the LtC experience has proven the mapping process to be far from straightforward. Different standards vary in how they are structured, for example, DwC is a ‘bag of terms’, with informal classes and no structural constraints, while more structured standards and ontologies like ABCD and PROV employ different approaches to how structure is defined and documented. The various standards use different metadata schemas and serialisations (e.g., Resource Description Framework (RDF), XML) for their documentation, and different approaches to providing persistent, resolvable identifiers for their terms. There are also many subtle nuances involved in assessing the alignment between the concepts that the source and target terms represent, particularly when assessing whether a match is exact enough to allow the existing term to be adopted. These factors make the mapping process quite manual and labour-intensive. Approaches and tools, such as developing decision trees (Fig. 2) to represent the logic involved and further exploration of the SSSOM standard, could help to streamline this process. In this presentation, we will discuss the LtC experience of the standard mapping process, the challenges faced and methods used, and the potential to contribute this experience to a collaborative standards mapping within the anticipated TDWG Standards Mapping Interest Group.

Wild Diplomacy: Cohabiting with Wolves on a New Ontological Map, written by Baptiste Morizot

Wild Diplomacy: Cohabiting with Wolves on a New Ontological Map, written by Baptiste Morizot

Investigating the Landscape of Ontologies for Catalysis Research Data Management

This work provides a survey of ontologies for catalysis research to improve the findability, accessibility, interoperability, and reusability (FAIRness) of research data. Applying tools that are commonly used by lab scientists, ontologies relevant to catalysis research are classified in a simple, well formatted spreadsheet template (Excel). This enables a scientist and domain expert without programming skills to evaluate a certain ontology. The entries of this template are then processed and visualized through automated creation of markdown files on GitHub using Python scripts. Furthermore, ontology mapping by searching for similar pairs of classes across different ontologies is performed, using the outcome of the ontology classification. This work contributes to the development of ontologies for catalysis research, facilitating better data integration and knowledge sharing while reusing existing semantic artefacts.

Finding a Common Ground for NFDI Terminologies

We present the goals and first results of the NFDI Section Metadata Working Group on Ontology Harmonisation and Mapping. The ongoing analysis of the used terminologies within the NFDI consortia suggests that, agreeing on a single NFDI wide ontological framework for very general and interdisciplinary concepts needed in the semantic annotation of research date is not feasible in the short run due to domain specific requirements. We thus present how the Research Data Alliance (RDA) framework I-ADOPT, which focuses only on the formal description of observation variables in scientific studies, could be utilised to provide an NFDI wide and global common ground for data interoperability.

The Role of Women and the Contextualization of Peace in Modern Times: Analysis on Hadith Perspective

Muslim women have been acknowledged for their huge contributions in building community, serving society, and promoting peace as reported in Hadith literature and Sīrah Nabawiyyah. This study aims to highlight related Hadiths concerning women and their roles in promoting peace, particularly from two authentic Hadith collections: Ṣaḥīḥ al-Bukhāri and Ṣaḥīḥ Muslim. This study also intends to present ontology visualisation of the collected Hadiths concerning women and peace. It focuses on how the Hadith describes the concept of peace in relation to women, and how women can practice her rights in building and maintaining peace. As a qualitative study, there are different approaches applied in determining the relationship between women and peace. First, gathering Hadiths into several themes related to peace by applying the thematic approach. Second, is the ontological mapping approach which aims in building and exhibit the relationship between women and peace, from the Hadith perspective. Third, an analytical approach is applied in order to analyze historical, logical, and contextual aspects of the related Hadiths. The finding shows that though the number of Hadiths concerning women and peace might be limited, those related Hadiths give a massive impact, particularly on women, and all mankind.

Understanding relevance in maps through the use of knowledge graphs

ABSTRACT The relation between the ontological and spatial dimensions is not always evident. A tool called SeMaptics that connects the two domains has been developed to better understand this relation. Ontological mapping allows for discrete ontologies to be projected into the spatial field. Such ontologies are regularly seen in a continuous or overlapping layered format in the spatial dimension. However, integrating both spaces results in a novel method, which could add additional perspectives to the map-making process. The objective is to link and map both dimensions enabling user interactions and enhancing semantic probing. This exploration contains some criteria of geographical relevance. These criteria allow for better identification of high-interest areas within any given dataset. SeMaptics implements a graph structure to accommodate graph visualisations using D3js. In this work data from an isolated administrative unit in Austria is presented under both the ontological and spatial dimensions. In order to identify the criteria elements that comprise geographical relevance. Such criteria points are over-viewed in this style of mapping to better understand relevance when exploring spatial data from a semantic viewpoint.

Mapping.bio: Piloting FAIR semantic mappings for biodiversity digital twins

Biodiversity research has a strong focus on the links between environment and functional traits, e.g., to assess how anthropogenic drivers of change impact ecological systems (Díaz et al. 2013). Interoperable exchange and integration of such data is enabled through the use of ontologies that provide ”meaning” to data and enable downstream processing involving learning and inference over graph-structured models of these data (Kulmanov et al. 2020). However, the development of thematically similar semantic artifacts, e.g., the Environmental Ontology (ENVO, Buttigieg et al. 2016) and the Semantic Web for Earth and Environment Technology Ontology (SWEET, DiGiuseppe et al. 2014), in biodiversity-related disciplines (e.g., environmental genomics and earth observation) can introduce substantial conceptual overlaps, and highlights the need for bridging technologies to facilitate reuse of biodiversity data across those knowledge fields (Karam et al. 2020). A recent design study, funded by the European Open Science Cloud (EOSC), proposes a framework to create, document and publish mappings and crosswalks linking different semantic artifacts within a particular scientific community and across scientific domains under the label of "Flexible Semantic Mapping Framework" (SEMAF, Broeder et al. 2021). SEMAF puts a strong emphasis on so-called pragmatic mappings, i.e., mappings that are driven by specific interoperability goals such as translations between specific observation measurements (e.g., sensor configurations) and metadata descriptions. Within the Horizon Europe Project “Biodiversity Digital Twin for Advanced Modelling, Simulation and Prediction Capabilities" (BioDT), a mapping tool leveraging SEMAF is currently under development: Mapping.bio provides a lightweight web service to read semantic artifacts, visualize them, add mappings as graphical connections and store the mappings as FAIR (Findable, Accessible, Interoperable Reusable) Digital Objects (FDOs, De Smedt et al. 2020) in a repository. To foster reusability, sustainably and long-term availability of digital objects, mapping.bio features mappings compliant with the Simple Standard for Sharing Ontological Mappings (SSSOM, Matentzoglu et al. 2022), a machine-interpretable and extensible vocabulary enabling the self-contained exploration and processing of annotated mappings by machines (machine actionability, Jacobsen et al. 2020).

Multi-domain knowledge graph embeddings for gene-disease association prediction

BackgroundPredicting gene-disease associations typically requires exploring diverse sources of information as well as sophisticated computational approaches. Knowledge graph embeddings can help tackle these challenges by creating representations of genes and diseases based on the scientific knowledge described in ontologies, which can then be explored by machine learning algorithms. However, state-of-the-art knowledge graph embeddings are produced over a single ontology or multiple but disconnected ones, ignoring the impact that considering multiple interconnected domains can have on complex tasks such as gene-disease association prediction.ResultsWe propose a novel approach to predict gene-disease associations using rich semantic representations based on knowledge graph embeddings over multiple ontologies linked by logical definitions and compound ontology mappings. The experiments showed that considering richer knowledge graphs significantly improves gene-disease prediction and that different knowledge graph embeddings methods benefit more from distinct types of semantic richness.ConclusionsThis work demonstrated the potential for knowledge graph embeddings across multiple and interconnected biomedical ontologies to support gene-disease prediction. It also paved the way for considering other ontologies or tackling other tasks where multiple perspectives over the data can be beneficial. All software and data are freely available.

An ontology-based knowledge framework for selection of joining process in plastic assembly

Selection of assembly process is one of the important stage of product development which need to tackle at the earlier stage product realization. Selecting the most appropriate joining process needs various assembly joint information (liaison) which includes both the geometric and non-geometric information. To capture, represent, and reuse this information across domains is a highly knowledge-intensive process that necessitates the use of an active artificial intelligence (AI) tool. In this research, an AI tool called ontology-based knowledge framework is used to assist with identifying the best plastic assembly process to effectively support designers and process planners. The methodology for selecting a joining process and developing a knowledge-based framework is described, together with industrial application of the suggested approach is proposed. To express many key concepts like liaison, process, requirement for variant product etc., a joining process selection (JPS) ontology is created. To get the necessary knowledge for process selection that integrates several instances and knowledge rules, ontology mapping of joining method selection concepts is done using Semantic Web Rule Language (SWRL). The suggested method uses rule-based reasoning to automatically infer the best joining methods. Finally, industrial application of the suggested framework is proposed to assess the applicability of this approach.

Knowledge Representation and Management 2022: Findings in Ontology Development and Applications.

To select, present, and summarize the best papers in 2022 for the Knowledge Representation and Management (KRM) section of the International Medical Informatics Association (IMIA) Yearbook. We conducted PubMed queries and followed the IMIA Yearbook guidelines for performing biomedical informatics literature review to select the best papers in KRM published in 2022. We retrieved 1,847 publications from PubMed. We nominated 15 candidate best papers, and two of them were finally selected as the best papers in the KRM section. The topics covered by the candidate papers include ontology and knowledge graph creation, ontology applications, ontology quality assurance, ontology mapping standard, and conceptual model. In the KRM best paper selection for 2022, the candidate best papers encompassed a broad range of topics, with ontology and knowledge graph creation remaining a considerable research focus.

An ontology-based methodology to establish city information model of digital twin city by merging BIM, GIS and IoT

With the development of digital city and smart city construction, the City Information Model (CIM) has played a critical role as a container of spatial–temporal data to establish the Digital Twin City. For a digital twin city, a virtual high-fidelity CIM model that corresponds closely to the real physical world is the premise and cornerstone of its construction. Therefore, the integration of BIM, GIS and IoT has become the preferred topic for researchers and has received much more attention from a wide academic circle. However, traditional integration mainly focuses on the conversion of both IFC and CityGML, and IoT data are also often used as visualizations. More importantly, the underlying data formats of GIS, BIM and IoT are still independent of each other without a unified data structure expression, so real data-driven analysis and decision-making cannot be implemented. This study aims to establish a general CIM ontology to integrate heterogeneous BIM, GIS and IoT data. First, the related work of BIM, GIS and IoT integration is studied and analyzed. A comparison of three mainstream approaches, data conversion, standard extension and data linking, is conducted, and it illustrates the advantages of ontology techniques in solving data interoperability problems. Second, a technical framework of BIM, GIS and IoT data integration based on ontology technology is proposed. The approach is mainly divided into five steps: geometry processing, data instantiation, ontology construction, ontology mapping and querying application. On the basis of the CIM ontology, an application ontology is built for a specific application domain to illustrate rule-based mapping, querying and inferring. Finally, the case study shows that the Ontology-based methodology in this paper has contributed to establish a general pattern for CIM data integration by mapping and linking concepts from the semantic level. It avoids changes in the original data sources and the missing data problem.

Systematic Evaluation of the Application of Zebrafish in Toxicology (SEAZIT): Developing a Data Analysis Pipeline for the Assessment of Developmental Toxicity with an Interlaboratory Study.

The embryonic zebrafish is a useful vertebrate model for assessing the effects of substances on growth and development. However, cross-laboratory developmental toxicity outcomes can vary and reported developmental defects in zebrafish may not be directly comparable between laboratories. To address these limitations for gaining broader adoption of the zebrafish model for toxicological screening, we established the Systematic Evaluation of the Application of Zebrafish in Toxicology (SEAZIT) program to investigate how experimental protocol differences can influence chemical-mediated effects on developmental toxicity (i.e., mortality and the incidence of altered phenotypes). As part of SEAZIT, three laboratories were provided a common and blinded dataset (42 substances) to evaluate substance-mediated effects on developmental toxicity in the embryonic zebrafish model. To facilitate cross-laboratory comparisons, all the raw experimental data were collected, stored in a relational database, and analyzed with a uniform data analysis pipeline. Due to variances in laboratory-specific terminology for altered phenotypes, we utilized ontology terms available from the Ontology Lookup Service (OLS) for Zebrafish Phenotype to enable additional cross-laboratory comparisons. In this manuscript, we utilized data from the first phase of screening (dose range finding, DRF) to highlight the methodology associated with the development of the database and data analysis pipeline, as well as zebrafish phenotype ontology mapping.

English Translation proofreading System based on Information Technology: Construction of semantic Ontology Translation Model

ABSTRACT Translation as a bridge between different cultures has received significant attention in recent years, especially with the rise of globalization and the increasing communication between nations. Translation has become a crucial tool for connecting people, promoting cross-cultural understanding, and facilitating international trade, academic exchange, and outbound tourism. With the advancement of information technology, research on improving English translation algorithms has become a hot topic. This is due to the complexities and variability of language, where words can have different meanings in different contexts. To address the issue of semantic coherence in current English translation proofreading systems, this paper proposes a semantic ontology translation model that utilizes fuzzy mapping to translate English and selects the most semantically appropriate translation using a decision function. The contributions of this research are twofold. Firstly, it provides a new approach to English translation proofreading by incorporating semantic ontology and fuzzy mapping. Secondly, it demonstrates the effectiveness of this approach through the improved efficiency and accuracy of English proofreading. The key findings of this study show that the proposed semantic ontology translation model can significantly improve the semantic coherence of English translation proofreading. This makes this article different from other studies by offering a unique solution to the problem of semantic coherence in English translation proofreading.

Integrative comparative analysis of avian chromosome evolution by in-silico mapping of the gene ontology of homologous synteny blocks and evolutionary breakpoint regions

Avian chromosomes undergo more intra- than interchromosomal rearrangements, which either induce or are associated with genome variations among birds. Evolving from a common ancestor with a karyotype not dissimilar from modern chicken, two evolutionary elements characterize evolutionary change: homologous synteny blocks (HSBs) constitute common conserved parts at the sequence level, while evolutionary breakpoint regions (EBRs) occur between HSBs, defining the points where rearrangement occurred. Understanding the link between the structural organization and functionality of HSBs and EBRs provides insight into the mechanistic basis of chromosomal change. Previously, we identified gene ontology (GO) terms associated with both; however, here we revisit our analyses in light of newly developed bioinformatic algorithms and the chicken genome assembly galGal6. We aligned genomes available for six birds and one lizard species, identifying 630 HSBs and 19 EBRs. We demonstrate that HSBs hold vast functionality expressed by GO terms that have been largely conserved through evolution. Particularly, we found that genes within microchromosomal HSBs had specific functionalities relevant to neurons, RNA, cellular transport and embryonic development, and other associations. Our findings suggest that microchromosomes may have conserved throughout evolution due to the specificity of GO terms within their HSBs. The detected EBRs included those found in the genome of the anole lizard, meaning they were shared by all saurian descendants, with others being unique to avian lineages. Our estimate of gene richness in HSBs supported the fact that microchromosomes contain twice as many genes as macrochromosomes.

An Ontology-Based Approach for Consolidating Patient Data Standardized With European Norm/International Organization for Standardization 13606 (EN/ISO 13606) Into Joint Observational Medical Outcomes Partnership (OMOP) Repositories: Description of a Methodology

To discover new knowledge from data, they must be correct and in a consistent format. OntoCR, a clinical repository developed at Hospital Clínic de Barcelona, uses ontologies to represent clinical knowledge and map locally defined variables to health information standards and common data models. The aim of the study is to design and implement a scalable methodology based on the dual-model paradigm and the use of ontologies to consolidate clinical data from different organizations in a standardized repository for research purposes without loss of meaning. First, the relevant clinical variables are defined, and the corresponding European Norm/International Organization for Standardization (EN/ISO) 13606 archetypes are created. Data sources are then identified, and an extract, transform, and load process is carried out. Once the final data set is obtained, the data are transformed to create EN/ISO 13606-normalized electronic health record (EHR) extracts. Afterward, ontologies that represent archetyped concepts and map them to EN/ISO 13606 and Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) standards are created and uploaded to OntoCR. Data stored in the extracts are inserted into its corresponding place in the ontology, thus obtaining instantiated patient data in the ontology-based repository. Finally, data can be extracted via SPARQL queries as OMOP CDM-compliant tables. Using this methodology, EN/ISO 13606-standardized archetypes that allow for the reuse of clinical information were created, and the knowledge representation of our clinical repository by modeling and mapping ontologies was extended. Furthermore, EN/ISO 13606-compliant EHR extracts of patients (6803), episodes (13,938), diagnosis (190,878), administered medication (222,225), cumulative drug dose (222,225), prescribed medication (351,247), movements between units (47,817), clinical observations (6,736,745), laboratory observations (3,392,873), limitation of life-sustaining treatment (1,298), and procedures (19,861) were created. Since the creation of the application that inserts data from extracts into the ontologies is not yet finished, the queries were tested and the methodology was validated by importing data from a random subset of patients into the ontologies using a locally developed Protégé plugin ("OntoLoad"). In total, 10 OMOP CDM-compliant tables ("Condition_occurrence," 864 records; "Death," 110; "Device_exposure," 56; "Drug_exposure," 5609; "Measurement," 2091; "Observation," 195; "Observation_period," 897; "Person," 922; "Visit_detail," 772; and "Visit_occurrence," 971) were successfully created and populated. This study proposes a methodology for standardizing clinical data, thus allowing its reuse without any changes in the meaning of the modeled concepts. Although this paper focuses on health research, our methodology suggests that the data be initially standardized per EN/ISO 13606 to obtain EHR extracts with a high level of granularity that can be used for any purpose. Ontologies constitute a valuable approach for knowledge representation and standardization of health information in a standard-agnostic manner. With the proposed methodology, institutions can go from local raw data to standardized, semantically interoperable EN/ISO 13606 and OMOP repositories.

Ontology construction and mapping of multi-source heterogeneous data based on hybrid neural network and autoencoder

Ontology construction and mapping of multi-source heterogeneous data based on hybrid neural network and autoencoder

Gene expression data visualization tool on the o²S²PARC platform

Background: The identification of differentially expressed genes and their associated biological processes, molecular function, and cellular components are essential for genetic disease studies because they present potential biomarkers and therapeutic targets. Methods: In this study, we developed an o²S²PARC template to instantiate an interactive pipeline for gene expression data visualization, ontological mapping, and statistical evaluation. To demonstrate the tool's usefulness, we performed a case study on a publicly available dataset. Results: The tool enables users to identify the differentially expressed genes (DEGs) and visualize them in a volcano plot format. Ontologies associated with the DEGs are assigned and visualized in barplots. Conclusions: The “Expression data visualization” template is publicly available on the o²S²PARC platform.