Initial Ontology Research Articles

The Future of School Health Education in the United States: An Ontology.

As summarized in this article, the widespread implementation of modern school health education (SHE) could become one of the most effective means available to improve the well-being of people in the United States and in other nations. However, the development and evolution of SHE largely remains unorganized, underdeveloped, and neglected by health and education agencies, policymakers, and the public. Essential to the development of any scientific discipline, scientists today use the word ontology to refer to efforts to organize knowledge in particular domains. A useful working definition of a scientific ontology is an explicit, formal specification of a shared conceptualization-a systematic set of shared terms and an explication of their interrelationships. Nine interdependent questions are outlined to help guide the development of an initial, broad, and actionable scientific ontology for SHE. Whether and how we respond to these questions arguably will determine the future of SHE research, policy, practice, and equity in the United States. An initial ontology might help conceptualize, inform, and facilitate more systematic and strategic local, state, national, and international deliberations and actions to improve SHE.

Determining the invariant of inter-frame processing for constructing the image similarity metric

The relevance of modeling digital images is determined by the need to implement approaches in the study of localization and identification of objects in order to reduce the amount of data. In this paper, the study object is the topology of a discrete two-dimensional image within the framework of the problem of determining the invariants of diffeomorphic transformations. Geographic information objects (GIOs) refer to objects that are on a given surface or objects that locally change the surface. With regard to objects, it is assumed that the change in their geolocation in the process of forming both single images and an extended series of frames obtained in the process of continuous monitoring is insignificant. In the process of scanning the surface, possible changes in the position of the image source are taken into account, for example, such as yawing, rolling, and pitch in the case of unmanned aerial vehicles (UAVs). These maneuvers are represented as a group of diffeomorphisms that are controlled by the internal gyroscopes of the carrier and the external navigation system. Based on the studies reported here, the initial ontology of digital images (ODI) has been determined by using the model of color spaces and functions of a special kind. The presence of an ontology makes it possible to build an adequate topology of color distribution in the image and take into account the specificity of the distribution of different colors in a digital image. The study results indicate that a promising method is to determine the similarity by constructing a color atlas structure graph (CASG) based on ODI and by determining invariants as a fragment of CASG inherited by all images in the sequence. The scope and conditions for the practical use of the result include its application to the analysis of images by methods of artificial intelligence

How do others cope? Extracting coping strategies for adverse drug events from social media

Patients advise their peers on how to cope with their illness in daily life on online support groups. To date, no efforts have been made to automatically extract recommended coping strategies from online patient discussion groups. We introduce this new task, which poses a number of challenges including complex, long entities, a large long-tailed label space, and cross-document relations. We present an initial ontology for coping strategies as a starting point for future research on coping strategies, and the first end-to-end pipeline for extracting coping strategies for side effects. We also compared two possible computational solutions for this novel and highly challenging task; multi-label classification and named entity recognition (NER) with entity linking (EL). We evaluated our methods on the discussion forum from the Facebook group of the worldwide patient support organization ‘GIST support international’ (GSI); GIST support international donated the data to us. We found that coping strategy extraction is difficult and both methods attain limited performance (measured with F1 score) on held out test sets; multi-label classification outperforms NER+EL (F1=0.220 vs F1=0.155). An inspection of the multi-label classification output revealed that for some of the incorrect predictions, the reference label is close to the predicted label in the ontology (e.g. the predicted label ‘juice’ instead of the more specific reference label ‘grapefruit juice’). Performance increased to F1=0.498 when we evaluated at a coarser level of the ontology. We conclude that our pipeline can be used in a semi-automatic setting, in interaction with domain experts to discover coping strategies for side effects from a patient forum. For example, we found that patients recommend ginger tea for nausea and magnesium and potassium supplements for cramps. This information can be used as input for patient surveys or clinical studies.

Learning Bayesian Networks from the Knowledge of a Never-Ending Learning System

Never-Ending Learning (NEL) is a new paradigm of machine learning in which computer systems can learn continuously and incrementally. The first never-ending learning system reported in literature is named NELL (Never-Ending Language Learning). NELL has built a Knowledge Base (KB) containing many types of knowledge. In this paper, we propose to learn Bayesian networks from the knowledge stored in the NELLs KB aiming to assist in the development of expert systems in the future. In addition, Bayesian networks have shown to be promising to solve the problem of representing semantic relations and extending the NELLs initial ontology. In the initial experiments, we have built a dataset on the domain of diseases from relations existing in NELL and applied two learning algorithms of Bayesian networks named K2 and DMBC. The empirical results have shown that this proposal is promising.

An Approach for Automatic Ontology Enrichment from Texts

The automatic ontology enrichment consists of automatic knowledge extraction from texts related to a domain of discourse in the aim to enrich automatically an initial ontology of the same domain. However, the passage, from a plain text to an enriched ontology requires a number of steps. In this paper, we present a three steps ontology enrichment approach. In the first step, we apply natural language processing techniques to obtain tagged sentences. The second step allows us to reduce each extracted sentence to an SVO (Subject, Verb, and Object) sentence, supposed to preserve main information carried by the original sentence(s) from which it is extracted. Finally, in the third step, we proceed to enrich an initial ontology built manually by adding extracted terms in the generated SVO as new concepts or instances of concepts and new relations. To validate our approach, we have used “Phytotherapy" domain because of the availability of related texts on the WWW and also because its usefulness for pharmaceutical industry. The first results obtained, after experiments on a set of different texts, testify the performance of the proposed approach.

Representation of behaviour change interventions and their evaluation: Development of the Upper Level of the Behaviour Change Intervention Ontology.

Background: Behaviour change interventions (BCI), their contexts and evaluation methods are heterogeneous, making it difficult to synthesise evidence and make recommendations for real-world policy and practice. Ontologies provide a means for addressing this. They represent knowledge formally as entities and relationships using a common language able to cross disciplinary boundaries and topic domains. This paper reports the development of the upper level of the Behaviour Change Intervention Ontology (BCIO), which provides a systematic way to characterise BCIs, their contexts and their evaluations. Methods: Development took place in four steps. (1) Entities and relationships were identified by behavioural and social science experts, based on their knowledge of evidence and theory, and their practical experience of behaviour change interventions and evaluations. (2) The outputs of the first step were critically examined by a wider group of experts, including the study ontology expert and those experienced in annotating relevant literature using the initial ontology entities. The outputs of the second step were tested by (3) feedback from three external international experts in ontologies and (4) application of the prototype upper-level BCIO to annotating published reports; this informed the final development of the upper-level BCIO. Results: The final upper-level BCIO specifies 42 entities, including the BCI scenario, elaborated across 21 entities and 7 relationship types, and the BCI evaluation study comprising 10 entities and 9 relationship types. BCI scenario entities include the behaviour change intervention (content and delivery), outcome behaviour, mechanism of action, and its context, which includes population and setting. These entities have corresponding entities relating to the planning and reporting of interventions and their evaluations. Conclusions: The upper level of the BCIO provides a comprehensive and systematic framework for representing BCIs, their contexts and their evaluations.

Development of an ontological model of the distance learning system

An ontological model of the distance learning system has been built, which captures and structures the knowledge common to this subject area. This allows it to be used repeatedly within the subject area as the basis of a single model of knowledge, which ensures logical consistency between the individual ontologies when combined. An ontology has been formed to build a single information space of the distance learning system. This single information space allows you to navigate the developed knowledge base. Requirements of semantic search and acquisition of new knowledge are realized by means of initial ontology and are transferred in the form of updating in uniform information space. Information hierarchies for designing an intellectual model of distance learning process using elements of the methodology of object-oriented approach are constructed. The application of the ontological approach is a very effective way to design an intellectual model of the distance learning process. Based on the theory of system analysis and synthesis of organizational management, as well as on methodologies for automating the design of information technology processes and systems, a method of creating a single information space for the implementation of operational management of higher education. An ontological method of modeling information systems based on multidimensional models is proposed for use. The tools of this method allow you to develop an infographic model of any information system that will fully reflect the pragmatics of the system under study, and thus eliminate the use of any additional methods for its creation.

A Layered Approach for Merging Application Ontologies: A Case Study from Agriculture Domain

Ontology is a machine interpretable way of representing knowledge in a precise and complete way favorable to solve many problems in the field of knowledge engineering. Different knowledge areas evolve with the time and the applications which use ontologies should be updated with new knowledge accordingly. It is more effective to combine the ontologies with new knowledge with the existing application ontologies rather than designing a new ontology from scratch. When combining ontologies, keeping the original usability of the initial ontologies and the heterogeneity of the components of the ontologies are the main obstacles. As there are no universal standard for naming of ontology components, this is a major reason for the heterogeneity problem. Methods for overcoming these problems are needed. In this research, we have proposed two algorithms to overcome the aforementioned problems. These algorithms for finding correspondences of ontology concepts and merging domain specific application ontologies keeping the original usability of the initial ontologies are the main outcomes of this research. The proposed algorithms are evaluated in terms of accuracy by comparing the resultant ontology merged using the proposed algorithm and the resultant ontology merged by an expert. The evaluation results prove that the proposed methodology merges the domain specific application ontologies very similar to the ontology merged by human intervention.

Representation of behaviour change interventions and their evaluation: Development of the Upper Level of the Behaviour Change Intervention Ontology

Background: Behaviour change interventions (BCI), their contexts and evaluation methods are heterogeneous, making it difficult to synthesise evidence and make recommendations for real-world policy and practice. Ontologies provide a means for addressing this. They represent knowledge formally as entities and relationships using a common language able to cross disciplinary boundaries and topic domains. This paper reports the development of the upper level of the Behaviour Change Intervention Ontology (BCIO), which provides a systematic way to characterise BCIs, their contexts and their evaluations. Methods: Development took place in four steps. (1) Entities and relationships were identified by behavioural and social science experts, based on their knowledge of evidence and theory, and their practical experience of behaviour change interventions and evaluations. (2) The outputs of the first step were critically examined by a wider group of experts, including the study ontology expert and those experienced in annotating relevant literature using the initial ontology entities. The outputs of the second step were tested by (3) feedback from three external international experts in ontologies and (4) application of the prototype upper-level BCIO to annotating published reports; this informed the final development of the upper-level BCIO. Results: The final upper-level BCIO specifies 42 entities, including the BCI scenario, elaborated across 21 entities and 7 relationship types, and the BCI evaluation study comprising 10 entities and 9 relationship types. BCI scenario entities include the behaviour change intervention (content and delivery), outcome behaviour, mechanism of action, and its context, which includes population and setting. These entities have corresponding entities relating to the planning and reporting of interventions and their evaluations. Conclusions: The upper level of the BCIO provides a comprehensive and systematic framework for representing BCIs, their contexts and their evaluations.

Initial safety posture investigations for earth regime rendezvous and proximity operations

Next-generation space activities, where companies and organizations begin to provide services for each others space assets, are real and coming on-line. The Consortium for Execution of Rendezvous and Servicing Operations (CONFERS) is an industry-led initiative meant to help enable new companies and industries in pursuit of on-orbit servicing (OOS) through metrics and standard developments. The University of Southern California’s (USC) Space Engineering Research Center (SERC) is chartered to examine past and present activities related to this new domain and uncover elements that might benefit from the application of standards or practices for the industry as a whole. This paper will 1) review and update USC’s initial technical rendezvous metrics, and 2) introduce OOS functional evaluations and an initial ontology for consideration.

Representation of behaviour change interventions and their evaluation: Development of the Upper Level of the Behaviour Change Intervention Ontology

Background: Behaviour change interventions (BCI), their contexts and evaluation methods are heterogeneous, making it difficult to synthesise evidence and make recommendations for real-world policy and practice. Ontologies provide a means for addressing this. They represent knowledge formally as entities and relationships using a common language able to cross disciplinary boundaries and topic domains. This paper reports the development of the upper level of the Behaviour Change Intervention Ontology (BCIO), which provides a systematic way to characterise BCIs, their contexts and their evaluations. Methods: Development took place in four steps. (1) Entities and relationships were identified by behavioural and social science experts, based on their knowledge of evidence and theory, and their practical experience of behaviour change interventions and evaluations. (2) The outputs of the first step were critically examined by a wider group of experts, including the study ontology expert and those experienced in annotating relevant literature using the initial ontology entities. The outputs of the second step were tested by (3) feedback from three external international experts in ontologies and (4) application of the prototype upper-level BCIO to annotating published reports; this informed the final development of the upper-level BCIO. Results: The final upper-level BCIO specifies 42 entities, including the BCI scenario, elaborated across 21 entities and 7 relationship types, and the BCI evaluation study comprising 10 entities and 9 relationship types. BCI scenario entities include the behaviour change intervention (content and delivery), outcome behaviour, mechanism of action, and its context, which includes population and setting. These entities have corresponding entities relating to the planning and reporting of interventions and their evaluations. Conclusions: The upper level of the BCIO provides a comprehensive and systematic framework for representing BCIs, their contexts and their evaluations.

D4.3 - Initial NanoCommons Ontology

This deliverable describes the outcomes of the first 18 months of work on the NanoCommons Ontology, which builds on and expands the FP7-funded eNanoMapper ontology. With significantly fewer resources dedicated to ontologies than the eNanoMapper project had, the NanoCommons project has taken over the continued development of the eNanoMapper ontology, and the managed release of updated versions at regular intervals. NanoCommons has made two major releases of the ontology in a period of 18 months, based on needs from other NanoSafety Cluster projects and efforts, like Nanoreg2, Gracious, ACENano, the USA NIKC project, and the Malta Initiative. This deliverable introduces the history of the eNanoMapper ontology, how the NanoCommons project has taken over release management, the recent updates to the development of the ontology and updates to the content of the ontology. It also describes our efforts for continued dissemination of the ontology to support others in utilisation of the ontology to support interlinking of datasets from different projects and sources and the collaboration with other projects with specific ontology work packages or tasks to guarantee a harmonized approach. Significant work went into maintenance of the ontology, requiring updates in third-party ontologies, updating the hosting of the ontology, and exploration of current needs from other European, American and international projects, and the integration of an ontology mapping tool for integration of datasets into the NanoCommons Knowledgebase. This deliverable also summarizes new dissemination and networking activities, consisting of both physical meetings and online tutorials, and an outlook of the upcoming work resulting from gaps identified in the reported initial ontology, which includes the need to add definitions to many of the terms, and to adjust the organisation of some of the terms based on user feedback.

Ontology expansion based on UWN reusability

In the present era of big data the demand for developing efficient knowledge representation techniques for different applications is expanding steadily. Knowledge representation is largely based on ontology in the Semantic Web. However, one of the major difficulties to construct a new ontology require users the high cost and long time. Collecting complete knowledge about the domain of opening is time consuming and cannot guarantee expected results too. Therefore, reuse of existing resources can offer a cost and time benefit than building a new one from scratch. Hence, an approach is proposed to reuse the existing ontologies available on the web or online libraries then enrich it. This paper proposed Uyghur domain ontology constructing method based on UWN reusability, which can extract domain ontology factors from UWN and get the initial ontology in which the concepts and relations are already relative standard, then expand it automatically via Word2Vec models, at last construct the final higher quality version after revising and improving. The method is simple, but effective on ontology enrichment. After evaluating the accuracy, the experimental results show that our method is feasible and effective.

“Onto-Computer-Project”, a Computer Project Domain Ontology : Construction and Validation

Ontologies, nowadays, play a primordial role in the representation, the re-use and the sharing of knowledge of a well given domain in a consensual and explicit way more precisely in the computing field. It is in this context that we have proposed a domain ontology baptised onto-computer-project which presents the key to our research goal. This essential goal is to arrive at a final step to elaborate a knowledge based system for computer projects reusing. The aimed system is essentially based on the construction of a memory projects. This memory projects could be defined as a collection of historical and achieved projects around the sphere of computer. This sphere is so wide including many subfields beginning at the database, software engineering fields and arriving at the fields of artificial intelligence, computer vision and so on. This research work requires at first to construct a well-defined ontology in the way to structure and to unify vocabulary often shared by multi actors in the domain of computer projects. To concretize this goal, our paper will describe a construction approach for the proposed domain ontology which is mainly based on an existing methodology named “methontology”. The proposed ontology construction approach, which is composed of seven steps, is the result of a comparative study between some ontology construction approaches belonging to different categories of methodology. In fact, we can distinguish four main categories of ontology development approaches: ontology construction approaches from zero, text-based construction approaches, building approaches based on the reuse of already existing ontologies, and crowd souring-based approaches. In our research work, we are interested by the approach of building ontology from zero. Indeed, the construction of the proposed ontology follows an autonomous approach which is not based on any other existing ontology or the updating of an already constructed ontology. In addition, in this paper we are interested by the problem of validating the content of domain ontology and in this context we have proposed an incremental approach for validating the proposed ontology which is composed of six steps. In this context we have studied some ontology validation approaches: those which are questionnaire based, others based on question answering. The problem here that all approaches studied are single actor approaches where a single validation actor can validate the entire ontology and this by applying the semantic and the structural validation definitively with no return. The main originality of our validation approach consists essentially of three criteria: the incremental validation, the multi-intervention, and the respecting of the “V” cycle. In fact, the passage from one validation step to another results in an update of the initial ontology and this by the intervention of three experts (project management expert, a project computer expert and a specialist in ontology engineering). Our proposal approach requires a feedback between all the validation phases and can return to any expert for revalidation if needed. The result of this research is improved a validated ontology which is allowed us to build our project memory and to feed our knowledge base which will serve us to develop our knowledge-based system.

Data-driven ontology generation and evolution towards intelligent service in manufacturing systems

Abstract To support intelligent manufacturing, providing a unified production data view by integrating distributed data collected by different enterprise information systems is critical. Because various information systems are often heterogeneous, ontology is widely adopted to present a global reference view for data integration. However, construction and maintenance of these ontologies is difficult because of the heterogeneity and dynamism of these large-scale data. In this paper, with the objective of intelligent manufacturing application implementation, we propose a comprehensive ontology generation and evolution method that automatically abstracts ontology from raw production data and dynamically adjusts the ontology in accordance with changes in the manufacturing data environment. The proposed method comprises four phases: data extraction, ontology construction, ontology connection, and ontology evolution. In the first phase, data from different sources are mapped to data entities to form a unified data structure. In the second phase, an initial ontology is generated via instance-driven ontology construction. In the third phase, to support intelligent manufacturing, the initial ontologies are organised in terms of the dimensions of the various business elements, such as stuff, machine, product, process, and scenarios. In the fourth phase, rules regarding ontology restrictions are formulated to realise ontology evolution that respond to changes in the manufacturing environment. To verify the efficacy of the proposed method, a prototype was implemented with real data from a manufacturing factory, in which the constructed ontology was used as the metadata of product data in intelligent manufacturing.

Using Association Rules to Enrich Arabic Ontology

In this article, we propose the use of a minimal generic base of associative rules between term association rules, to automatically enrich an existing domain ontology. Initially, non-redundant association rules between terms are extracted from an Arabic corpus. Then, the matching of the candidate terms is done through the matching between the concepts of the initial ontology and the premises of the association rules, with three distance measures that we define.

AN INITIAL ONTOLOGY FOR SYSTEM QUALITIES

ABSTRACTThis paper presents an initial ontology for reasoning about a system's System Qualities (SQs), ilities, or non‐functional requirements (reliability, usability, affordability, and more). The need for such ontology is based primarily on two factors. One is the importance of getting the SQs sufficiently well defined such that the system's definition, development, and evolution result in a satisfactory balance of SQ values for the system's success‐critical stakeholders, given the frequent system shortfalls and overruns that occur when the system does not achieve this balance. The other is that current system acquisition and evolution guidance descriptions have numerous deficiencies and inconsistencies in their coverage of SQ considerations. This situation is becoming more serious as systems and their stakeholders become increasingly complex, dynamic, and diverse. This paper provides an elaboration of the needs, a set of initial SQ ontology elements and definitions, examples of their application to some key SQs and their relationships, and an identification of further research and development needed to make the ontology fully useful and evolvable.

Tableau-based revision for expressive description logics with individuals

Our understanding of an application domain evolves over time. If we consider an ontology as a set of semantic constraints which describes our understanding of an application domain, ontologies must be revised. Indeed, adding a new semantic constraint to an ontology may make it inconsistent. To restore consistency with minimal loss of semantics, we would change other semantic constraints of the ontology such that the resulting ontology is semantically as close as possible to the initial ontology. To be able to say that an ontology is semantically close to another one, it is needed to define a distance over finite structures representing models, called completion graphs, which characterize the semantics of an ontology. In this paper we present a tableau algorithm for building such completion graphs of an ontology expressed in the description logic SHIQ with individuals. Based on the distance defined over completion graphs, we introduce a revision operation applied to a SHIQ ontology with a set of new semantic constraints. This revision operation computes the completion graphs that a revised ontology should admit. However, there does not always exist an ontology expressible in SHIQ from which a tableau algorithm generates exactly a given set of completion graphs. This leads us to introduce the notion of upper approximation ontology from which a tableau algorithm can generate the smallest set of completion graphs including a given set of completion graphs. This notion allows us to design an algorithm for constructing a revised ontology from an initial ontology with a set of new semantic constraints. We also implement the proposed algorithms with optimizations and report some experimental results to show that a model-based approach to revision of expressive ontologies is practicable.

Tableau-Based Revision for Expressive Description Logics with Individuals

Our understanding of an application domain evolves over time. If we consider an ontology as a set of semantic constraints which describes our understanding of an application domain, ontologies must be revised. Indeed, adding a new semantic constraint to an ontology may make it inconsistent. To restore consistency with minimal loss of semantics, we would change other semantic constraints of the ontology such that the resulting ontology is semantically as close as possible to the initial ontology. To be able to say that an ontology is semantically close to another one, it is needed to define a distance over finite structures representing models, called completion graphs, which characterize the semantics of an ontology. In this paper we present a tableau algorithm for building such completion graphs of an ontology expressed in the description logic SHIQ with individuals. Based on the distance defined over completion graphs, we introduce a revision operation applied to a SHIQ ontology with a set of new semantic constraints. This revision operation computes the completion graphs that a revised ontology should admit. However, there does not always exist an ontology expressible in SHIQ from which a tableau algorithm generates exactly a given set of completion graphs. This leads us to introduce the notion of upper approximation ontology from which a tableau algorithm can generate the smallest set of completion graphs including a given set of completion graphs. This notion allows us to design an algorithm for constructing a revised ontology from an initial ontology with a set of new semantic constraints. We also implement the proposed algorithms with optimizations and report some experimental results to show that a model-based approach to revision of expressive ontologies is practicable.

The Key Roles of Maintainability in an Ontology for System Qualities

AbstractIn our INCOSE IS 2015 paper, “An Initial Ontology for System Qualities,” (SQs), we provided an IDEF5 class hierarchy of upper‐level SQs, where the top level reflected classes of stakeholder value propositions (Mission Effectiveness, Resource Utilization, Dependability, Flexibility), and the next level identified means‐ends enablers of the higher‐level SQs. In experimenting with, refining and formalizing the ontology, we focused on a depth‐first approach on a chosen SQ: Maintainability. It is key to reducing 75% of most systems’ life cycle costs. Also Maintainability plays key roles in three of the four top‐level SQs: Resource Utilization (now called Life Cycle Efficiency) Dependability, and Flexibility (now called Changeability). Dependability also needs Maintainability to relate Reliability to Availability; and Changeability also needs Maintainability to address new system challenges and opportunities. This paper summarizes resulting changes in the SQ ontology, and also provides examples of Maintainability need and use, quantitative relations where available, and summaries and references on improved practices.