dataesr/scientific-paragraphs-categorization huggingface dataset:
Continuing our efforts to develop more and more biodiversity ontologies  [7, 25, 42, 43]  would allow us to envision increasing semantification of the ecology domain in the near future. Combined with tools such as inteGraph, which facilitate the conversion of biodiversity datasets into graph knowledge bases, these semantic resources could support the creation by different communities of numerous domain-specific KGs, which could eventually be interconnected to form a single biodiversity KG covering the entire tree of life and the full diversity of global ecosystems. support was provided by MIAI Grenoble Alpes. Wilfried Thuiller reports financial support was provided by Grenoble Alpes Métropole. Wilfried Thuiller reports financial support was provided by Département de l'Isère.
Acknowledgements We acknowledge support from the  European Union's Horizon Europe  under grant agreement N • 101060429 (NaturaConnect), the  French Agence Nationale de la Recherche  through the  EcoNet  ( ANR-18-CE02-0010 ),  GlobNet  ( ANR-16-CE02-0009 ) and  FishPredict  ( ANR-21-AAFI-0001 ) projects and the  MIAI@Grenoble Alpes  ( ANR-19-P3IA-0003 ) institute.
RELATED WORK In the literature of simulation-based validation for ADS  [19, 23, 34] , there is a large body of work on the generation of safety-critical scenarios  [7] , either by using scenario modeling languages  [12, 25] , or from available databases  [10, 31]. Scenic is a well-known probabilistic programming language for generating scenarios for ADS  [12]. Paracosm  [25]  is another software system that also allows users to describe complex driving situations with specific characteristics and generate scenarios by different parameter configurations. Our approach is based on a semantic model extracted from the Simulator. We generate scenarios based on coverage criteria and test them against properties specified in a logic and verified by verification techniques. Many other works adopt search-based algorithms to generate scenarios challenging the autonomous driving systems.
For example, Av-fuzzer  [22]  utilizes a genetic algorithmbased search to detect situations where an autonomous driving system can run into safety violations. MOSAT  [31]  uses genes to encode basic driving maneuvers and applies a multi-objective genetic algorithm to search for adversarial and diverse test scenarios. None of them considers violation of properties or traffic rules as the criteria in identifying safety-critical scenarios. Efforts are also made to generate scenarios according to map topology. To facilitate the virtual testing of motion planners for automated vehicles, the work in  [18]  first extracts a large variety of road networks from OpenStreetMap. Then it uses the traffic simulator SUMO to generate traffic scenarios for these road networks. The criticality of the scenarios is reinforced by the use of nonlinear optimization.
Search-based testing leverages on optimization to generate concrete test cases  [15]. In this work, we use equivalence on scenarios as a metamorphic testing relation. However, we use an Oracle applying runtime verification to detect discrepancies. Table  6  compares the characteristics of the most recent tools for systematically testing autonomous systems, showing significant differences with RvADS. The criteria considered are the simulation environment, the number of ego vehicles, the type of properties tested, the specification language, and the test case generation method. It should be noted that RvADS, after making appropriate modifications to the Simulator, allows each vehicle to have its own autopilot. On the contrary, the other tools handle a single vehicle with autopilot. The other vehicles follow either a predefined trajectory or interact randomly with the ego vehicle.
Thus, the scenarios in RvADS involve the autonomous movement of all the vehicles, allowing realistic critical situations to be created and tested. Furthermore, in RvADS we use a specification language that can deal with configurations between vehicles, and so to describe traffic rules for junctions. This is achieved using an appropriately formalized map on which we can reason and test vehicle configurations. LawBreaker is the closest tool to RvADS. However, its logic cannot handle vehicle configurations on a map. In addition, Law-Breaker's scenario description language is taken from AVUnit 2  , which can introduce NPC vehicles that are unclear to what extent they can be controlled to achieve specific global configurations.
Many platforms for benchmarking optimization algorithms offer users the possibility of sharing their experimental data with the purpose of promoting reproducible and reusable research. However, different platforms use different data models and formats, which drastically inhibits identification of relevant data sets, their interpretation, and their interoperability. Consequently, a semantically rich, ontology-based, machine-readable data model is highly desired. We report in this paper on the development of such an ontology, which we name OPTION (OPTImization algorithm benchmarking ONtology). Our ontology provides the vocabulary needed for semantic annotation of the core entities involved in the benchmarking process, such as algorithms, problems, and evaluation measures. It also provides means for automated data integration, improved interoperability, powerful querying capabilities and reasoning, thereby enriching the value of the benchmark data.
THE OPTION ONTOLOGY In this paper, we present the OPTimization Algorithm Benchmarking ONtology (OPTION). The ontology we propose was designed with the main goal of standardizing and formalizing knowledge from the domain of benchmarking optimization algorithms, where an emphasis was put on the representation of data from the benchmark performance space. OPTION offers a comprehensive description of the domain, covering the benchmarking process, as well as the core entities involved in the process, such as optimization algorithms, benchmark problems, evaluation measures, etc. The OPTION ontology is publicly available on BioPortal 1  the largest repository of ontologies. We have also constructed a semantic annotation pipeline for annotating performance data, as well as a querying endpoint for querying the knowledge base.
USE CASE To demonstrate the utility of the OPTION ontology for semantic annotation, we consider the BBOB benchmark suite as a use case. We use a subset of BBOB data that includes the algorithms from the 2015-2017 competitions (three years). This gives us a total of 32 algorithms, which we semantically annotate using the OPTION ontology. All annotations were produced with the Apache Jena RDF library in the form of RDF graphs. For each of the 32 algorithms, a separate RDF graph was generated and uploaded to Apache Jena TDB2, a native triple store that efficiently stores RDF data. The data in the ontology provides the full information on the used problem/dimension/instance/algorithm, as well as the corresponding performance data. In addition, data provenance information has been manually collected and linked to the performance data to trace its origin.
QUERYING THE KNOWLEDGE BASE After populating the knowledge base, we can use the SPARQL query language to ask queries. To enable this functionality, we have set up an Apache Jena Fuseki2 server and implemented two services. The query service provides an endpoint for handling SPARQL queries in a RESTful manner, while the upload service enables the upload of RDF data into the Apache Jena TDB2 triple store. A non-exhaustive list of queries that are currently supported by the OPTION knowledge base is as follows: (1) For a given algorithm (e. g. , CMA-ES) and fixed budget scenario (e. g. , between 1000 -2000 function evaluations), return the noise-free fitness -Fopt calculated for a set of benchmark problems (e. g. , f1 and f7) and a set of problem instances (e. g. , the first 5 instances); (2) For a given algorithm (e.
CONCLUSIONS & FUTURE WORK The development of the OPTION ontology is a big step forward in the process of making benchmark data more reusable and interoperable. By annotating a large subset of the BBOB-data, we have shown the potential of the ontology to aid with data integration, while simultaneously providing powerful querying capabilities for direct analysis of the required datasets. This significantly reduces the time needed to collect data across many functions and algorithms, while being flexible in the type of performance perspective (i. e. , fixed-budget, fixed-target) to be used. Our next steps are as follows. First, we will annotate more data sets, starting with those provided by IOHprofiler  [1, 3]  and those of Nevergrad  [2]. Further extensions include a web-based GUI, similar to what is shown in Figure  1.
ACKNOWLEDGMENTS The authors acknowledge the support of the  Slovenian Research Agency  through research core grants No. P2-0103  and  P2-0098 , project grants No. J2-9230  and  Z2-1867 , and young researcher grant No. PR-09773  to AK, as well as the EC through grant No. 952215  (TAILOR). Our work is also supported by the  Paris Ile-de-France region.
It seeks to integrate materials databases, model provenance data, and support knowledge extraction. Initially using DOLCE as its upper ontology, it weaves in pre-existing ontologies to describe diverse elements like units, time, and scientific experiments. Key concepts include Material, Property, Family, Process, Structure, and Measurement. It is composed of 848 classes, 351 of which do not have an elucidation, and 96 properties. It presents a nested structure, where classes are organized in a hierarchy with different levels of subsumption relationships. MatOnto also provides a unique way to describe relational database relationships, allowing dynamic SQL statement creation. Its primary application is in the fuel cell domain, and it's employed to discover new compounds for fuel cell electrolytes.
MatOnto also serves as the backbone for MatSeek, a web search tool. Currently, MatOnto is being updated to version 2. 0, planning to switch its upper ontology to BFO and enhance its logical consistency and commonality. e: MDO-FULL: Material Design Ontology  [25]  The Materials Design Ontology (MDO) aims to address the challenges associated with data accessibility and interoperability across disparate materials databases, particularly prevalent in the materials design domain. It is composed of 37 classes, 8 of which do not have an elucidation, and 64 properties. The majority of the classes are superclasses without children. It sports a FAIRness score of 242. Given that materials databases typically have different data models, users often struggle to locate and integrate data from various sources.
The MDO leverages ontologies and ontology-based techniques to improve data availability and interoperability by formalizing domain knowledge representation. Crafted using domain knowledge from materials science, particularly solid-state physics, and guided by data sourced from several materials design databases, MDO is a comprehensive ontology encapsulating knowledge within the materials design field. The application of MDO has been demonstrated on data extracted from established materials databases, showcasing its practical utility in enhancing data interoperability. f: MOCO: Mat-O-lab Container Ontology  [26] ,  [27]  This ontology offers a lightweight solution for detailing the structure of tabular data (series data) stored in hdf5 containers. It has been effectively applied to describe time-forcedisplacement data from tensile tests.
Utilizing ontologies, open standards, and interoperable designs, eNanoMapper promotes a unified approach to European nanotechnology research. Its database supports diverse data and provides an infrastructure for data sharing, analysis, and modeling of ENMs, accessible through an API. A key feature is a configurable spreadsheet parser that simplifies data preparation and upload. Additionally, a web application allows users to retrieve and analyze experimental data using machine learning algorithms. Importantly, the database supports the import and online publication of ENM data from various sources, facilitating the development of reproducible quantitative structure-activity relationships for nanomaterials (NanoQSAR). f: LPBFO: Laser Powder Bed Fusion Ontology  [34]  LPBFO, the Linked Product Basic Formal Ontology, was developed and effectively employed within the AluTrace project to establish digital connections between disparate data and knowledge silos that often emerge throughout the course of industrial product development and manufacturing cycles.
To ensure accessibility and widespread usage, the NPO is made available to the public through the BioPortal website, which is maintained by the National Center for Biomedical Ontology. This facilitates seamless access to the ontology for interested parties. Furthermore, mechanisms for editorial and governance processes are under development to support the ongoing maintenance, review, and expansion of the NPO, ensuring its relevance and accuracy as the field of nanotechnology and cancer research progresses. j: RXNO: Reaction Ontology  [39] ,  [40]  The RXNO Ontology, or more formally the Reaction Ontology, is a resource designed to provide a standardized classification and description of chemical reactions. This ontology is designed to annotate and integrate reaction data across various databases and information sources, thereby promoting interoperability.
It includes all the concepts related to the fundamental atomistic theory. Most of its concepts are under EMMO's Reductionistic perspective, and use elucidations from IUPAC Goldbook, IEC standards, and Wikipedia. The ontology, including its imports (the TLO it is based on), encompasses 533 classes and 46 object properties. b: BattINFO: Battery INterFace Ontology  [42]  The Battery Interface Ontology (BattINFO) is a free, opensource domain ontology designed to address the current challenges in the battery data landscape characterized by heterogeneous sources and inconsistent metadata. BattINFO aims to enhance the interoperability, reusability, and machinereadability of battery data. Developed within the scope of the EU H2020 project BIG-MAP, it fosters data interoperability across over 30 battery research institutes and companies in Europe.
Both imported ontologies contain the fundamental concepts, while CIF Core, the more detailed one, takes a large percentage of the space. The ontology, including its imports, encompasses 1806 classes and 47 object properties. d: CHAMEO: Characterization Methodologies Ontology  [43]  The CHAMEO ontology, a domain ontology, seeks to bridge the divergence in terminologies and data management approaches that exist in the wide-ranging field of materials characterization. Stemming from a recent CEN Workshop Agreement (CWA 17815), CHAMEO is grounded in a standardized terminology and the characterization Data (CHADA) documentation scheme. The overarching goal of CHAMEO is to serve as a unifying framework, harmonizing methodspecific ontologies by reusing and specializing its generic constructs. CHAMEO is part of a broader European Materials Modelling Council (EMMC) initiative to develop interconnected materials modeling ontologies based on the Elementary Multiperspective Material Ontology (EMMO) root.
By systematically organizing and categorizing the terminology, this initiative seeks to enhance understanding, communication, and research within the field of photovoltaic technology. It imports another module called Photovoltaics that contains the classes related to the composition of the Photovoltaic cells layers and inherent measurements. l: Precipitation Model  [51]  This ontology includes many physical, statistical and simulation models for the calculation for studying how dislocations move and their position. It is focused on linear defects in material structures. As well as the Mechanical Testing Ontology earlier described, it composes the metal alloy module in EMMO, covering both the experimental and numerical observations. m: Virtual Material MarketPlace (VIMMP) Ontology  [52]  The Virtual Materials Marketplace project is an initiative that focuses on creating an open platform for offering and utilizing services related to materials modeling.
A significant part of the project deals with the development of ontologies and advancing data technology. VIMMP has established a series of marketplace-level ontologies designed to describe services, models, and user interactions, all within the framework of the European Materials and Modelling Ontology, which serves as a top-level ontology. These ontologies play a vital role in data annotation, facilitating the storage of this information within the ZONTAL Space component of VIMMP. Additionally, these ontologies are instrumental in managing data and metadata intake and retrieval at the VIMMP marketplace's front-end.
4) DLOs in Materials Science aligned with SIO a: MM: MaterialsMine [53] A materials ontology to support data publication involving nanomaterials and metamaterials. The MaterialsMine Team brings together expertise across five research institutions in the fields of mechanics, materials, design, manufacturing, data science, and computer science to build and develop an open-source, user-friendly materials data resource guided by FAIR principles, with current modules geared toward research communities in the domains of polymer nanocomposites (NanoMine) and mechanical metamaterials (MetaMine). MaterialsMine is an extension of NanoMine and incorporates curated data from research articles in the field. It is composed of 2052 classes, 1711 of which do not have an elucidation, and 325 properties. The majority of the classes have at least a subclass.
C. ONTOLOGY ALIGNMENT AND INTEGRATION APPROACHES An analysis of the existing literature revealed no relevant deviations or innovations with respect to standard alignment and integration approaches or techniques  [56]  for the Materials Science area. Notably, automatic techniques used in many contexts (e. g. [57] -  [59] ) are strongly privileged, possibly due to the large number of concepts appearing in the relevant ontologies -one of the peculiarities of the domain. On the other hand, in Materials Science the focus has been on Ontology-based data access, extraction and integration  [60]. To cite two examples, the authors of  [61]  proposed a hybrid Natural Language Understanding-driven technique to extract data from journal articles, whereas the authors of  [62]  created a database (the Crystallography Open Database) and employed it as a basis to unify and harmonize a plurality of resources.
A. CLASSIFICATION OF SUB-DOMAINS IN MATERIALS SCIENCE Materials Science is a very wide field encompassing a number of disciplines and sub-domains. In order to attain a clearer picture of DLOs' domain coverage, it was decided to assess the ontologies depending on the terms appearing in their documentation. Though indicative and susceptible to errors, this approach was deemed appropriate (and conductive to reasonably accurate results) given the sheer volume of data to be analyzed. The well-respected, de facto standard textbook for Materials Science, i. e. Callister's Materials Science and Engineering (now at its 10 th edition  [64] ) with its glossary of some 700 terms, was chosen as a point of reference. To ensure comprehensiveness, supplementary terms were sourced from the materials modeling CWA  [65]  (based on the Review on Materials Modeling (RoMM)  [66] ) and the materials characterization CWA.
In short, a statistical analysis of the terms employed in the involved DLOs serves to individuate core nodes in the network of ontologies. Standalone entities are engineered taking the individuated terms as a starting point; they are informally characterized referring to golden standards in a FAIR and extensive fashion; semantic connections are established with ontology entities belonging to Materials Science's DLOs and relevant TLOs. Bridge concepts operate as minimal data pipelines, enabling data exchange at core junctures.
The Unique Name Assumption (UNA) supposes that two terms with distinct identifiers from the same knowledge base do not refer to the same real-world entity. The UNA can be used to detect errors in large integrated knowledge bases. For example, some identity link can be erroneous if they are in a path that connects two entities (that refer to different real-world objects) defined in the same knowledge base. For large knowledge bases, however, the UNA does not always hold due to redundant IRIs that capture various encodings, languages, namespaces, versions, letter cases, etc. The UNA can still be useful for identifying erroneous links provided good adaption to the exceptions. For this, we propose a concrete definition of the UNA with tolerance towards multiple exceptions, namely the internal UNA (iUNA).
In Section 3, we propose a new definition of the UNA and we test the different UNA definitions and examine their reliability for error detection in Section 4, by validating them over data of the LOD cloud. In Section 5, we present our refinement algorithm and we evaluate it in Section 6. Finally, discussion and future work are presented in Section 7. Our main contributions 5  are as follows: 1. We propose a new definition of the UNA, namely the iUNA and check it against a large integrated knowledge graph together with other definitions. 2. We design an inconsistency-based refinement algorithm that evaluates definitions of the UNA by employing an SMT solver. 3. We publish a gold standard of over 8K manually annotated entities (200K owl:sameAs links) together with some additional information such as redirection and equivalence under different encoding schemes.
Related Work Estimates of the proportion of erroneous identity links in the semantic web range from around 3%  [11, 15]  to 20%  [10]. Existing approaches for detecting errors in identity graphs fall into three categories  [17]. Content-based approaches exploit the descriptions associated with each resource for evaluating the correctness of an identity link. They typically rely on additional information such as vocabulary alignments and textual descriptions for each entity. However, such information is not always available  [17, 8]  on open Web datasets, and in practice, such algorithms often do not scale to the size of the LOD Cloud. The networkbased approaches  [9, 16]  take advantage of graph-theoretical algorithms for the detection of erroneous links. For instance,  [16]  rely on the Louvain community detection algorithm for assigning an error degree for each identity link.
This error degree is based on the density of the community in which an identity link occurs Fig. 2 : An example CC with links expressing identity (black), redirection (red), and encoding equivalence (blue) (see also  Section 3). in, and the weight of the owl:sameAs (i. e. reciprocally asserted owl:sameAs have a lower error degree, hence a higher chance of correctness). These error degrees are published online as part of the MetaLink dataset  [3]. However, the accuracy of these methods is limited due to a lack of understanding of the underlying semantics. Finally, the inconsistency-based approaches  [11, 14]  hypothesise that owl:sameAs links that lead to logical inconsistencies have a higher chance of being incorrect. They typically require the presence of a large number of ontology axioms and alignment of the vocabularies.