Formal Ontology Research Articles

Disassembly task planning for end-of-life automotive traction batteries based on ontology and partial destructive rules

Disassembly is an essential step for the cascade utilization of end-of-life automobile power batteries. The diversity in types and structures of end-of-life automobile power batteries has led to much waste in time and cost in manual disassembly processes. With the incoming large-scale retirement of automobile power batteries, it is urgent to use artificial intelligence technology to enable the automation of battery disassembly planning. In order to establish a complete and open product information model to realize the automatic disassembly task planning of end-of-life automobile power battery, a disassembly task planning method of automobile power batteries is proposed based on ontology and partial destructive rules. This presented approach is in combination with case-based reasoning/rule-based reasoning, which is utilized as the mechanism for disassembly knowledge reuse and reasoning. Firstly, a disassembly information ontology of automobile power batteries is constructed to describe the components information and assembly relation. Then, a set of partial destructive rules are formulated to guide the dismount of parts with destructive connection. Thirdly, a disassembly sequence generation method is presented to infer feasible planning schemes from the rule base. Finally, the effectiveness of the method is tested with a case study of a power lithium-ion battery pack. The case study has indicated that this presented method can generate the disassembly task schemes quickly and effectively, when applied to the disassembly of large-scale heterogeneous automobile power batteries.

Approaching Healthy City Ontology: First-Level Classes Definition Using BFO

In recent decades, the concept of the healthy city (HC) has become more and more relevant in many fields, such as city administration and scientific environment, and has become a commonly understood concept in the general public. Due to the breakneck growth of people living in urban contexts, the subsequent necessity to guarantee good urban conditions for all kinds of citizens, and the general deterioration of the hearth environment caused by human activities (concentrated in urban settlements), this issue is increasing in its relevance. In this paper, the authors discuss the concept of the HC from an ontological point of view to organize the highly complex system of elements and the mutual relations that constitute the idea of HC. The main goals of an HC are quite intuitive, but the number of components that define and manage it is vast and related to different disciplines: sustainability, urban management, urban planning, and health and social studies. With the presented research, the authors intend to start an organizational definition of the HC using basic formal ontology (BFO). Considering the definition of HC, the authors focus on the ontology process and the different typologies of ontological structures. Then, the authors describe a first-level scheme of HC ontology and, finally, discuss possible applications of the presented study and next research steps.

GeoReservoir: An ontology for deep-marine depositional system geometry description

An ontology is a logical theory that accounts for a domain vocabulary’s intended meaning, allowing us to develop a computational artifact that explicitly and formally represents the community’s conceptualization related to a vocabulary. This paper presents the GeoReservoir ontology — the result of the ontological analysis of the terminology adopted by geologists in sedimentological studies about deep-marine depositional systems, which is one of the most relevant types of oil and gas reservoirs around the world. Despite the variety of studies describing the patterns of productive reservoirs, this domain demanded new approaches in conceptual modeling methodologies because the available terminology presents issues such as ambiguity and contamination of different interpretations in the terms’ definitions. Previous work has dealt with these issues in geology, resulting in the GeoCore, an ontology that explicitly defines the most generic entities in geology. However, the domain in focus still demanded a specialized ontology containing the particular terms found in reports about deep-marine deposits. GeoReservoir is an extension of GeoCore, which, in its turn, extends the Basic Formal Ontology (BFO), a foundational ontology for scientific domains. GeoReservoir takes advantage of both BFO and GeoCore’s foundations, allowing us to focus our effort on defining the domain-specific entities. A team of professional reservoir geologists supported the knowledge acquisition process. We developed the ontology in iterative steps from an initial prototype to a complete artifact containing the taxonomy of entities and the relations between the entities, being increasingly refined by the experts. We further present a case study demonstrating how one could describe a depositional system in GeoReservoir terms. Moreover, we validated the ontology against defined competency questions (CQs). This work’s final result is offering a sound, consistent and unambiguous terminology to support the integration of data and knowledge about deep-marine depositional system geometry and lithology.

From Graphematics to Phrasal, Sentential, and Textual Semantics Through Morphosyntax by Means of Corpus-Driven Grammar and Ontology: A Case Study on One Tibetan Text

Abstract This article presents the current results of an ongoing study of the possibilities of fine-tuning automatic morphosyntactic and semantic annotation by means of improving the underlying formal grammar and ontology on the example of one Tibetan text. The ultimate purpose of work at this stage was to improve linguistic software developed for natural-language processing and understanding in order to achieve complete annotation of a specific text and such state of the formal model, in which all linguistic phenomena observed in the text would be explained. This purpose includes the following tasks: analysis of error cases in annotation of the text from the corpus; eliminating these errors in automatic annotation; development of formal grammar and updating of dictionaries. Along with the morpho-syntactic analysis, the current approach involves simultaneous semantic analysis as well. The article describes semantic annotation of the corpus, required by grammar revision and development, which was made with the use of computer ontology. The work is carried out with one of the corpus texts – a grammatical poetic treatise Sum-cu-pa (VII c.).

Ontological modeling of the International Classification of Functioning, Disabilities and Health (ICF): Activities&Participation and Environmental Factors components

BackgroundThe International Classification of Functioning, Disability and Health (ICF) is a classification of health and health-related states developed by the World Health Organization (WHO) to provide a standard and unified language to be used as a reference model for the description of health and health-related states. The concept of functioning on which ICF is based is that of a “dynamic interaction between a person’s health condition, environmental factors and personal factors”. This overall model has been translated into a classification covering all the main components of functioning. However, the practical use of ICF has highlighted some formal problems, mainly concerning conceptual clarity and ontological coherence.MethodsIn the present work, we propose an initial ontological formalization of ICF beyond its current status, focusing specifically on the interaction between activities and participation and environmental factors. The formalization has been based on ontology engineering methods to drive goal and scope definition, knowledge acquisition, selection of an upper ontology for mapping, conceptual model definition and evaluation, and finally representation using the Ontology Web Language (OWL).ResultsA conceptual model has been defined in a graphical language that included 202 entities, when possible mapped to the SUMO upper ontology. The conceptual model has been validated against 60 case studies from the literature, plus 6 ad-hoc case studies. The model has been then represented using OWL.ConclusionsThis formalization might provide the basis for a revision of the ICF classification in line with current efforts made by WHO on the International Classification of Diseases and on the International Classification of Health Interventions.

Semantic knowledge in generation of 3D layouts for decision-making

Generative computation has the potential to enhance the accuracy, effectiveness, and creativity of spatial layout in design and planning. The paper proposes a methodology to separate the knowledge about objects, spatial relationships, and constraints from the generative process. The separation between the knowledge in a domain and its possible practical uses is an important achievement of semantic technologies, because it grants access to a large body of knowledge, spanning various aspects and processes across buildings and cities, which is being codified into formal ontologies. The present study has reused existing knowledge from two established ontologies. An illustrative case-project demonstrates the suitability of the methodology for a complex layout planning problem, involving a large number of decision-makers, with multiple competing objectives and criteria. The system implements multidimensional visual interactive tools to assist designers, planners, and decision-makers in exploring the layouts and the criteria, to develop their confidence in what qualifies as a good and effective solution.

DARSI: An Ontology for Facilitating the Development of Data Sharing and Use Agreements

The advantages of data sharing across organizations and disciplines are indisputable; although, sensitive and restricted data cannot be easily shared due to policies and legal matters. The research presented in this paper takes a step toward systematizing the sharing of sensitive and restricted research data by developing an ontology to frame and guide DSUA (Data Sharing and Usage Agreement) development. The paper provides background context, describes the ontology creation process, and introduces the Data Sharing Agreements for Restricted and Sensitive Information (DARSI) ontology. DARSI contains four top level classes, 20 sub-classes, 33 sub-categories and 17 simple properties for categories applicable at various levels. The discussion provides further insight into the work accomplished, and the conclusion identifies next steps.

BFO-based ontology enhancement to promote interoperability in BIM

Building Information Modelling (BIM) is a process for managing construction project information in such a way as to provide a basis for enhanced decision-making and for collaboration in a construction supply chain. One impediment to the uptake of BIM is the limited interoperability of different BIM systems. To overcome this problem, a set of Industry Foundation Classes (IFC) has been proposed as a standard for the construction industry. Building on IFC, the ifcOWL ontology was developed in order to facilitate representation of building data in a consistent fashion across the Web by using the Web Ontology Language (OWL). This study presents a critical analysis of the ifcOWL ontology and of the associated interoperability issues. It shows how these issues can be resolved by using Basic Formal Ontology (ISO/IEC 21838-2) as top-level architecture. A set of competency questions is used as the basis for comparison of the original ifcOWL with the enhanced ontology, and the latter is used to align with a second ontology – the ontology for building intelligent environments (DOGONT) – in order to demonstrate the added value derived from BFO by showing how querying the enhanced ifcOWL yields useful additional information.

Scr eening of key genes and pathways of brain aging and prediction of potential effective Chinese medicinals: based on bioinformatics

Microarray data of hippocampal tissue(HC) of the cognitively intact elderly(60-99 years old) were compared with those of the middle-aged and the young(20-59 years old) by bioinformatics techniques to initially screen out differentially expressed genes(DEGs) and then predict potential effective Chinese medicinals for the treatment of brain aging. The gene expression profile(accession: GSE11882) was downloaded from the Gene Expression Omnibus(GEO) and DEGs were screened based on R package. The key DEGs were identified by STRING, Cytoscape and the plug-in, followed by Gene Ontology(GO) term and Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway analysis. Then the key genes and the medical ontology information retrieval platform(Coremine Medical) were mapped against each other to single out the Chinese medicinals for the treatment of brain aging and construct the " Chinese medicinal-active constituent-target" network. Among the resultant 268 DEGs(246 down-regulated and 22 up-regulated), the 15 key genes were mainly involved in biological processes such as leukocyte migration, neutrophil activation, cell chemotaxis, microglia activation and response to external stimulus, and pathways such as inflammatory process, immune response, cytokine-cytokine receptor interaction, PI3 K-Akt signaling pathway, Rap1 signaling pathway, chemokine signaling pathway and Toll-like receptor signaling pathway. The potential effective Chinese medicinals were Notoginseng Radix et Rhizoma, Ginseng Radix et Rhizoma, Salviae Miltiorrhizae Radix et Rhizoma and Astragali Radix. The analysis of DEGs and key genes enhances the understanding of the mechanisms of brain aging. This study provides potential gene targets and ideas for the development of Chinese medicine for brain aging.

A formal representation of the divine comedy’s primary sources: The Hypermedia Dante Network ontology

Abstract Hypermedia Dante Network (HDN) is a 3-year Italian National Research Project, started in 2020, which aims to enrich the functionalities of the DanteSources Digital Library to efficiently represent knowledge about the primary sources of Dante’s Comedy. DanteSources allows users to retrieve and visualize the list and the distribution of Dante's primary sources that have been identified by recent commentaries of five of Dante’s minor works (i.e. Vita nova, De vulgari eloquentia, Convivio, De Monarchia, and Rime). The digital library is based on a formal ontology expressed in Resource Description Framework Schema (RDFS) language. Based on the DanteSources experience, the HDN project aims to formally represent the primary sources of the Divine Comedy whose identification is based on several commentaries included in the Dartmouth Dante Project corpus. To reach this goal, we restructured and extended the DanteSources ontology to provide a wider and more complete representation of the knowledge concerning the primary sources of the Comedy. In this article, we present the result of this effort, i.e. the HDN ontology. The ontology is expressed in OWL and has as reference ontologies the CIDOC CRM and its extension FRBRoo, including its in-progress reformulation LRMoo. We also briefly describe the semi-automatic tool that will be used by the scholars to populate the ontology.

ContSOnto: A Formal Ontology for Continuity of Care.

The global pandemic over the past two years has reset societal agendas by identifying both strengths and weaknesses across all sectors. Focusing in particular on global health delivery, the ability of health care facilities to scale requirements and to meet service demands has detected the need for some national services and organisations to modernise their organisational processes and infrastructures. Core to requirements for modernisation is infrastructure to share information, specifically structural standardised approaches for both operational procedures and terminology services. Problems of data sharing (aka interoperability) is a main obstacle when patients are moving across healthcare facilities or travelling across border countries in cases where emergency treatment is needed. Experts in healthcare service delivery suggest that the best possible way to manage individual care is at home, using remote patient monitoring which ultimately reduces cost burden both for the citizen and service provider. Core to this practice will be advancing digitalisation of health care underpinned with safe integration and access to relevant and timely information. To tackle the data interoperability issue and provide a quality driven continuous flow of information from different health care information systems semantic terminology needs to be provided intact. In this paper we propose and present ContSonto a formal ontology for continuity of care based on ISO 13940:2015 ContSy and W3C Semantic Web Standards Language OWL (Web Ontology Language). ContSonto has several benefits including semantic interoperability, data harmonization and data linking. It can be use as a base model for data integration for different healthcare information models to generate knowledge graph to support shared care and decision making.

Integration Strategy and Tool between Formal Ontology and Graph Database Technology

Ontologies, and especially formal ones, have traditionally been investigated as a means to formalize an application domain so as to carry out automated reasoning on it. The union of the terminological part of an ontology and the corresponding assertional part is known as a Knowledge Graph. On the other hand, database technology has often focused on the optimal organization of data so as to boost efficiency in their storage, management and retrieval. Graph databases are a recent technology specifically focusing on element-driven data browsing rather than on batch processing. While the complementarity and connections between these technologies are patent and intuitive, little exists to bring them to full integration and cooperation. This paper aims at bridging this gap, by proposing an intermediate format that can be easily mapped onto the formal ontology on one hand, so as to allow complex reasoning, and onto the graph database on the other, so as to benefit from efficient data handling.

Semantic Fusion with Deep Learning and Formal Ontologies for Evaluation of Policies and Initiatives in the Smart City Domain

Decision makers and policy analysts at different administrative levels often lack a holistic view of the problem as there are semantic variations in policy documents due to domain-specific content. For example, smart city initiatives are derived from national and international initiatives which may influence the incentives for local participants, but local initiatives reflect the local contextual elements of the city. Balanced assessment of smart city initiatives should include a systemic evaluation of the initiatives at multiple levels including the city, the country in which the city resides as well as at international level. In this paper, a knowledge elicitation methodology is presented for multi-granularity evaluation of policies and initiatives. The methodology is demonstrated on the evaluation of smart city initiatives generated at different administrative levels. Semantic networks are constructed using formal ontologies and deep learning methods for automatic semantic evaluation of initiatives to abstract knowledge found in text. Three smart city initiatives published by different administrative levels including international, national, and city level are evaluated in terms of relevance, coherence, and alignment of multi-level smart city initiatives. Experiments and analysis ultimately provide a holistic view of the problem which is necessary for decision makers and policy analysts of smart cities.

Model and Knowledge Representation for the Reuse of Design Process Knowledge Supporting Design Automation in Mass Customization

Mass customization aims to meet individual requirements and, therefore, is one way to attract and retain customers—a key challenge in the design industry. The increase in design automation has offered new opportunities to design customized products at high speed in a way that is cost equivalent to mass production. Design automation is built upon the reuse of product and process knowledge. Ontologies have proven to be a feasible, highly aggregated knowledge representation in engineering design. While product and process knowledge from other lifecycle phases are represented in multiple approaches, the design process of the product as well as the adaption process of product variants is missing, causing breakpoints or additional iterations in design automation. Therefore, suitable knowledge representation tailored to design automation is still missing. Accordingly, this contribution proposes a novel knowledge representation approach to enable design automation for mass customization. Methodically, this novel approach uses semantic enrichment of CAD environments to automatically deduce information about a design task, design rationale, and design process represented by a formal ontology. The integration of the design process significantly differentiates the approach from previous ones. The feasibility of the approach is demonstrated by a bike crank customization process.

An Ontological Approach to Enhancing Information Sharing in Disaster Response

Managing complex disaster situations is a challenging task because of the large number of actors involved and the critical nature of the events themselves. In particular, the different terminologies and technical vocabularies that are being exchanged among Emergency Responders (ERs) may lead to misunderstandings. Maintaining a shared semantics for exchanged data is a major challenge. To help to overcome these issues, we elaborate a modular suite of ontologies called POLARISCO that formalizes the complex knowledge of the ERs. Such a shared vocabulary resolves inconsistent terminologies and promotes semantic interoperability among ERs. In this work, we discuss developing POLARISCO as an extension of Basic Formal Ontology (BFO) and the Common Core Ontologies (CCO). We conclude by presenting a real use-case to check the efficiency and applicability of the proposed ontology.

Development of the International Classification of Diseases Ontology (ICDO) and its application for COVID-19 diagnostic data analysis

BackgroundThe 10th and 9th revisions of the International Statistical Classification of Diseases and Related Health Problems (ICD10 and ICD9) have been adopted worldwide as a well-recognized norm to share codes for diseases, signs and symptoms, abnormal findings, etc. The international Consortium for Clinical Characterization of COVID-19 by EHR (4CE) website stores diagnosis COVID-19 disease data using ICD10 and ICD9 codes. However, the ICD systems are difficult to decode due to their many shortcomings, which can be addressed using ontology.MethodsAn ICD ontology (ICDO) was developed to logically and scientifically represent ICD terms and their relations among different ICD terms. ICDO is also aligned with the Basic Formal Ontology (BFO) and reuses terms from existing ontologies. As a use case, the ICD10 and ICD9 diagnosis data from the 4CE website were extracted, mapped to ICDO, and analyzed using ICDO.ResultsWe have developed the ICDO to ontologize the ICD terms and relations. Different from existing disease ontologies, all ICD diseases in ICDO are defined as disease processes to describe their occurrence with other properties. The ICDO decomposes each disease term into different components, including anatomic entities, process profiles, etiological causes, output phenotype, etc. Over 900 ICD terms have been represented in ICDO. Many ICDO terms are presented in both English and Chinese. The ICD10/ICD9-based diagnosis data of over 27,000 COVID-19 patients from 5 countries were extracted from the 4CE. A total of 917 COVID-19-related disease codes, each of which were associated with 1 or more cases in the 4CE dataset, were mapped to ICDO and further analyzed using the ICDO logical annotations. Our study showed that COVID-19 targeted multiple systems and organs such as the lung, heart, and kidney. Different acute and chronic kidney phenotypes were identified. Some kidney diseases appeared to result from other diseases, such as diabetes. Some of the findings could only be easily found using ICDO instead of ICD9/10.ConclusionsICDO was developed to ontologize ICD10/10 codes and applied to study COVID-19 patient diagnosis data. Our findings showed that ICDO provides a semantic platform for more accurate detection of disease profiles.

Performability of Actions

Action theory may be regarded as a theoretical foundation of AI, because it provides in a logically coherent way the principles of performing actions by agents. But, more importantly, action theory offers a formal ontology mainly based on set-theoretic constructs. This ontology isolates various types of actions as structured entities: atomic, sequential, compound, ordered, situational actions etc., and it is a solid and non-removable foundation of any rational activity. The paper is mainly concerned with a bunch of issues centered around the notion of performability of actions. It seems that the problem of performability of actions, though of basic importance for purely practical applications, has not been investigated in the literature in a systematic way thus far. This work, being a companion to the book as reported (Czelakowski in Freedom and enforcement in action. Elements of formal action theory, Springer 2015), elaborates the theory of performability of actions based on relational models and formal constructs borrowed from formal lingusistics. The discussion of performability of actions is encapsulated in the form of a strict logical system . This system is semantically defined in terms of its intended models in which the role of actions of various types (atomic, sequential and compound ones) is accentuated. Since due to the nature of compound actions the system is not finitary, other semantic variants of are defined. The focus in on the system of performability of finite compound actions. An adequate axiom system for is defined. The strong completeness theorem is the central result. The role of the canonical model in the proof of the completeness theorem is highlighted. The relationship between performability of actions and dynamic logic is also discussed.

Low-Quality Error Detection for Noisy Knowledge Graphs

The automatic construction of knowledge graphs (KGs) from multiple data sources has received increasing attention. The automatic construction process inevitably brings considerable noise, especially in the construction of KGs from unstructured text. The noise in a KG can be divided into two categories: factual noise and low-quality noise. Factual noise refers to plausible triples that meet the requirements of ontology constraints. For example, the plausible triple <New_York, IsCapitalOf, America> satisfies the constraints that the head entity “New_York” is a city and the tail entity “America” belongs to a country. Low-quality noise denotes the obvious errors commonly created in information extraction processes. This study focuses on entity type errors. Most existing approaches concentrate on refining an existing KG, assuming that the type information of most entities or the ontology information in the KG is known in advance. However, such methods may not be suitable at the start of a KG's construction. Therefore, the authors propose an effective framework to eliminate entity type errors. The experimental results demonstrate the effectiveness of the proposed method.

Multi-faceted semantic clustering with text-derived phenotypes

Identification of ontology concepts in clinical narrative text enables the creation of phenotype profiles that can be associated with clinical entities, such as patients or drugs. Constructing patient phenotype profiles using formal ontologies enables their analysis via semantic similarity, in turn enabling the use of background knowledge in clustering or classification analyses. However, traditional semantic similarity approaches collapse complex relationships between patient phenotypes into a unitary similarity scores for each pair of patients. Moreover, single scores may be based only on matching terms with the greatest information content (IC), ignoring other dimensions of patient similarity. This process necessarily leads to a loss of information in the resulting representation of patient similarity, and is especially apparent when using very large text-derived and highly multi-morbid phenotype profiles. Moreover, it renders finding a biological explanation for similarity very difficult; the black box problem. In this article, we explore the generation of multiple semantic similarity scores for patients based on different facets of their phenotypic manifestation, which we define through different sub-graphs in the Human Phenotype Ontology. We further present a new methodology for deriving sets of qualitative class descriptions for groups of entities described by ontology terms. Leveraging this strategy to obtain meaningful explanations for our semantic clusters alongside other evaluation techniques, we show that semantic clustering with ontology-derived facets enables the representation, and thus identification of, clinically relevant phenotype relationships not easily recoverable using overall clustering alone. In this way, we demonstrate the potential of faceted semantic clustering for gaining a deeper and more nuanced understanding of text-derived patient phenotypes.

Model organism databases are in jeopardy.

Model organism databases are in jeopardy.