Standard Knowledge Representation Research Articles

Risk assessment of flammable liquid transportation on waterways: An ontology-driven dynamic Bayesian network approach

Accidents involving the transportation of flammable liquids on waterways often lead to severe consequences, highlighting the importance of effective risk assessment under conditions of uncertainty. This paper presents a novel methodology for evaluating the risk associated with the spatiotemporal evolution of flammable liquid transportation on waterways. By leveraging ontology models and dynamic Bayesian networks, the approach involves analyzing factors that impact risk, constructing a standardized knowledge representation model, and mapping this onto a dynamic Bayesian network for comprehensive risk assessment. The methodology incorporates fuzzy theory and the Best-Worst Method to calculate probabilities within the Bayesian framework, enabling detailed analysis of factor impacts on transportation risk. A practical application is illustrated through the development of a dynamic risk evaluation model for octane transportation in the Yangtze River, demonstrating the model's capability to predict risk under varying conditions. This ontology-driven Bayesian network model provides a robust foundation for making informed management decisions in the transportation of flammable liquids, effectively addressing the challenges of semantic expression and inferencing under uncertainty to enhance safety in waterway transportation.

An interoperable ontology for CPS-enabled Polyhouse Solar Dryer: A case study of the AgroESP project

Polyhouse is a commonly used conventional method for solar drying of food products. These Polyhouse Solar Driers (PSDs) are characterized by their enclosed structure and translucent covering, providing a controlled environment conducive to efficient food drying. Smart polyhouses, equipped with a Cyber-Physical System (CPS), further enhance this process by optimizing environmental conditions and enabling real-time monitoring. In smart PSDs, the data are obtained from diverse sources with different specifications in accuracy, resolution, and range. This multifaceted nature of the information obtained from various sources significantly compounds the complexity of the system. This complexity of data from diverse sources within smart polyhouses necessitates a standardized knowledge representation. Ontologies serve this purpose by establishing a common vocabulary and structure for data integration, promoting semantic interoperability and effective communication among diverse systems. This paper proposes a novel unified ontology designed to model complex polyhouse CPSs, aiming to address semantic interoperability issues and streamline data integration across various domains. The proposed polyhouse ontology attempts to reuse the concepts defined in existing ontologies rather defining new concepts for efficient knowledge sharing and enhanced understanding of polyhouse operations. The practical applicability of the polyhouse ontology has been verified with competency questions and through field deployment in a CPS enabled smart Polyhouse Solar Dryer.

Improving Visual Perception of Artificial Social Companions Using a Standardized Knowledge Representation in a Human-Machine Interaction Framework

Improving Visual Perception of Artificial Social Companions Using a Standardized Knowledge Representation in a Human-Machine Interaction Framework

Design without representation

Abstract Shapes are perceived unanalyzed, without rigid representation of their parts. They do not comply with standard symbolic knowledge representation criteria; they are treated and judged by appearance. Resolving the relationship of parts to parts and parts to wholes has a constructive role in perception and design. This paper presents a computational account of part–whole figuration in design. To this end, shape rules are used to show how a shape is seen, and shape decompositions having structures of topologies and Boolean algebras reveal alternative structures for parts. Four examples of shape computation are presented. Topologies demonstrate the relationships of wholes, parts, and subparts, in the computations enabling the comparison and relativization of structures, and lattice diagrams are used to present their order. Retrospectively, the topologies help to recall the generative history and establish computational continuity. When the parts are modified to recognize emergent squares locally, other emergent shapes are highlighted globally as the topology is re-adjusted. Two types of emergence are identified: local and global. Seeing the local parts modifies how we analyze the global whole, and thus, a local observation yields a global order.

Representation and Presentation of Culinary Tradition as Cultural Heritage

This paper presents a knowledge representation framework and provides tools to allow the representation and presentation of the tangible and intangible dimensions of culinary tradition as cultural heritage including the socio-historic context of its evolution. The representation framework adheres to and extends the knowledge representation standards for the Cultural Heritage (CH) domain while providing a widely accessible web-based authoring environment to facilitate the representation activities. In strong collaboration with social sciences and humanities, this work allows the exploitation of ethnographic research outcomes by providing a systematic approach for the representation of culinary tradition in the form of recipes, both in an abstract form for their preservation and in a semantic representation of their execution captured on-site during ethnographic research.

RuleMatrix: Visualizing and Understanding Classifiers with Rules.

With the growing adoption of machine learning techniques, there is a surge of research interest towards making machine learning systems more transparent and interpretable. Various visualizations have been developed to help model developers understand, diagnose, and refine machine learning models. However, a large number of potential but neglected users are the domain experts with little knowledge of machine learning but are expected to work with machine learning systems. In this paper, we present an interactive visualization technique to help users with little expertise in machine learning to understand, explore and validate predictive models. By viewing the model as a black box, we extract a standardized rule-based knowledge representation from its input-output behavior. Then, we design RuleMatrix, a matrix-based visualization of rules to help users navigate and verify the rules and the black-box model. We evaluate the effectiveness of RuleMatrix via two use cases and a usability study.

Data and knowledge standards for learning health: A population management example using chronic kidney disease.

The widespread creation of learning health care systems (LHSs) will depend upon the use of standards for data and knowledge representation. Standards can facilitate the reuse of approaches for the identification of patient cohorts and the implementation of interventions. Standards also support rapid evaluation and dissemination across organizations. Building upon widely‐used models for process improvement, we identify specific LHS activities that will require data and knowledge standards. Using chronic kidney disease (CKD) as an example, we highlight the specific data and knowledge requirements for a disease‐specific LHS cycle, and subsequently identify areas where standards specifications, clarification, and tools are needed. The current data standards for CKD population management recommendations were found to be partially ambiguous, leading to barriers in phenotyping, risk identification, patient‐centered clinical decision support, patient education needs, and care planning. Robust tools are needed to effectively identify patient health care needs and preferences and to measure outcomes that accurately depict the multiple facets of CKD. This example presents an approach for defining the specific data and knowledge representation standards required to implement condition‐specific population health management programs. These standards specifications can be promoted by disease advocacy and professional societies to enable the widespread design, implementation, and evaluation of evidence‐based health interventions, and the subsequent dissemination of experience in different settings and populations.

Knowledge Management in Product-Service Systems – A Product Lifecycle Perspective

The current paper aims to investigate the knowledge management practice in PSS based on literature, and presents propositions for both academia and practitioners from the perspective of product lifecycle. In particular, we look at knowledge requirement, knowledge reuse, and knowledge sharing throughout the entire product lifecycle. Our findings suggest that more appropriate knowledge representation manners and standard knowledge representation form, the identification and classification of the most important knowledge for different stakeholders, and balanced application of personalization and codification strategy will be very important for companies in PSS domain to manage knowledge. Trigging by the increasing concern of sustainability in PSS context, we propose a product lifecycle model integrated raw materials extraction and material production, therefore making a more integral close information loop in PSS.

Leveraging the electronic health record for research and quality improvement: Current strengths and future challenges

Passage of the Health Information Technology for Economic and Clinical Health (HITECH) Act in 2009 resulted in the rapid adoption of electronic health record (EHR) systems. With this, came the promise of increased access to patient data for beneficial secondary uses such as research and quality improvement. However, clinicians and researchers interested in leveraging HIT for such purposes often find EHR data as heterogeneous and complex. As such, EHR clinical data often remain underutilized. Biomedical and Health Informaticians are developing systems and techniques to use the EHR for clinical knowledge discovery and dissemination. Applicable approaches may require biostatistical, programming, and other technical knowledge, as well as information technology data infrastructure often consisting of the institution׳s clinical data repository (CDR). Other techniques can also be instituted by practicing clinicians to leverage EHR systems including the use of clinical decision support (CDS) tools and standard knowledge representation for surgical applications.

Ontology for Querying Heterogeneous Data Sources in Freight Transportation

AbstractNavigating through multiple heterogeneous freight data sources to find the ones that are relevant to answering a question can be a challenging task when performed manually. It is highly dependent on the individual’s knowledge of all available data sources and the information contained in each one. Multiple factors contribute to freight data heterogeneity, including differences in data element definitions, level of disaggregation, and classification systems. This paper proposes a standardized knowledge representation of freight data sources using an ontology that both computer systems and domain experts can understand. The ontology is developed from a formal representation of data elements found to exist in freight data sources. The paper also presents a querying algorithm for searching through the ontology and determining relevant freight data sources that can be used for answering user queries. The proposed ontology and querying algorithm facilitate the querying and identification of relevant fre...

Early knowledge organization assisted by aspects

Throughout the lifecycle of software products, changes arise. The repercussions of these changes increase in more advanced stages of the development process. The aspect-oriented proposal is a software development paradigm that provides principles to manage changes. However, the current proposals for the development of the early stages of software do not provide an effective means for functional changes and updates. This paper presents a knowledge modeling methodology in the aspect-oriented context focused on the early stages of the software development. This methodology aims to ease the software evolution through a standardized knowledge representation, a multidimensional organization, and an appropriate advanced separation of concerns. The proposal of a conceptual modeling of early-aspect provides the means for an organization of multidimensional knowledge. The use of ontological techniques under the principles of knowledge-engineering allows specification of early knowledge. This paper presents a case study developed using the methodology and is used as a base to evaluate the methodology, comparing the efficiency in the changes against classic approaches. The results show that it is possible to ease the changes with the proposal presented in this paper.

LapOntoSPM: an ontology for laparoscopic surgeries and its application to surgical phase recognition.

The rise of intraoperative information threatens to outpace our abilities to process it. Context-aware systems, filtering information to automatically adapt to the current needs of the surgeon, are necessary to fully profit from computerized surgery. To attain context awareness, representation of medical knowledge is crucial. However, most existing systems do not represent knowledge in a reusable way, hindering also reuse of data. Our purpose is therefore to make our computational models of medical knowledge sharable, extensible and interoperational with established knowledge representations in the form of the LapOntoSPM ontology. To show its usefulness, we apply it to situation interpretation, i.e., the recognition of surgical phases based on surgical activities. Considering best practices in ontology engineering and building on our ontology for laparoscopy, we formalized the workflow of laparoscopic adrenalectomies, cholecystectomies and pancreatic resections in the framework of OntoSPM, a new standard for surgical process models. Furthermore, we provide a rule-based situation interpretation algorithm based on SQWRL to recognize surgical phases using the ontology. The system was evaluated on ground-truth data from 19 manually annotated surgeries. The aim was to show that the phase recognition capabilities are equal to a specialized solution. The recognition rates of the new system were equal to the specialized one. However, the time needed to interpret a situation rose from 0.5 to 1.8 s on average which is still viable for practical application. We successfully integrated medical knowledge for laparoscopic surgeries into OntoSPM, facilitating knowledge and data sharing. This is especially important for reproducibility of results and unbiased comparison of recognition algorithms. The associated recognition algorithm was adapted to the new representation without any loss of classification power. The work is an important step to standardized knowledge and data representation in the field on context awareness and thus toward unified benchmark data sets.

Applying Decisional DNA to Internet of Things: The Concept and Initial Case Study

In this article, we present a novel approach utilizing Decisional DNA to help the Internet of Things capture decisional events and reuse them for decision making in future operations. The Decisional DNA is a domain-independent, standard and flexible knowledge representation structure that allows its domains to acquire, store, and share experiential knowledge and formal decision events in an explicit way. We apply this approach to our current work—SmartBike, a sensor-equipped bicycle built under the concept of Internet of Things. By using Decisional DNA and machine learning algorithms, the SmartBike is able to distinguish its user's patterns based on past riding data. The presented conceptual approach demonstrates how Decisional DNA can be applied to the Internet of Things and bring to them intelligence required by forthcoming semantic networks.

Implementing Virtual Engineering Objects (VEO) with the Set of Experience Knowledge Structure (SOEKS)

This paper illustrates the idea of Virtual Engineering Object (VEO) powered by Set of Experience Knowledge Structure (SOEKS) and Decisional DNA (DDNA). A VEO is the knowledge representation of an engineering object, having embodiment of all its associated knowledge and experience within it. Moreover, VEO is a specialization of Cyber-Physical System (CPS) in terms of that its extension in knowledge gathering and reuse, whereas CPS is only aimed towards data and information management. The SOEKS/DDNA is a flexible and standard knowledge representation structure to acquire and store experiential knowledge. The article also presents a case study to demonstrate implementation of VEO in the manufacturing scenario. The decision making in industrial design and manufacturing will benefit from this approach, as it includes capturing, storage and reuse of experience and knowledge of an engineering artefact.

Decentralized case-based reasoning and Semantic Web technologies applied to decision support in oncology

AbstractThis article presents the Kasimir system dedicated to decision knowledge management in oncology and which is built on top of Semantic Web technologies, taking benefit from standard knowledge representation formalisms and open reasoning tools. The representation of medical decision protocols, in particular for breast cancer treatment, is based on concepts and instances implemented within the description logic OWL DL (Web ontology language description logic). The knowledge units related to a protocol can then be applied for solving specific medical problems, using instance or concept classification. However, the straight application of a protocol is not always satisfactory, for example, because of contraindications, necessitating an adaptation of the protocol. This is why the principles and methods of case-based reasoning (CBR) in the framework of DLs have been used. In addition, the domain of oncology is complex and involves several specialties, for example, surgery and chemotherapy. This complexity can be better undertaken with a viewpoint-based representation of protocols and viewpoint-based reasoning, for either application or adaptation of the protocols. Accordingly, a distributed DL has been used for representing a viewpoint-based protocol. The application and the adaptation of the viewpoint-based protocol to medical cases is carried out using global instance classification and decentralized CBR.

A behaviour sequencing and composition architecture based on ontologies for entertainment humanoid robots

The research in Human–Robot interaction stresses the importance of non verbal behaviours in order to enforce credibility and believability in robots. When using small and cheap entertainment robots, verbal cues are very limited, gaze and facial gestures can be absent, whereas body gestures comprise–as a matter of fact–only stereotyped behaviours. The main contribution of this article is an evaluation study about the effects of pure body gestures when entertainment robots are observed by humans in an interaction process. Robots are required to execute a sequence of behaviours, as if they were playing on a stage. To this aim, a model for composing behaviour primitives for entertainment humanoid robots is presented. Both single and multi robot scenarios are considered. The framework can sequence robot behaviours using complex scripts, which can be easily programmed using standard ontology-based knowledge representation mechanisms. Procedures are used to operate on the ontology representation in order to execute behaviours in a fully hierarchical and recursive fashion. Experimental validation with an interview campaign has been carried out using toy Kondo robots.

Abstraction of informed virtual geographic environments

We propose a novel method for the automated generation of virtual geographic environments that allows using geographic information system data to build what we call informed virtual geographic environment (IVGE). The description of an IVGE integrates semantic information expressed using conceptual graphs, a standard knowledge representation technique. In addition, we propose an abstraction process that uses geometric, topologic, and semantic characteristics of geographic features to build a hierarchical graph-based structure describing this IVGE. Our IVGE model enables the support of large-scale and complex geographic environment modeling for multiagent geo-simulations in which the agents are situated and with which they interact.

GAINING KNOWLEDGE THROUGH EXPERIENCE: DEVELOPING DECISIONAL DNA APPLICATIONS IN ROBOTICS

In this article, we explore an approach that integrates Decisional DNA, a domain-independent, flexible, and standard knowledge representation structure, with robots in order to test the usability and suitability of this novel knowledge representation structure. Core issues in using this Decisional DNA–based method include capturing of knowledge, storage and indexing of knowledge, organization of the knowledge base memory, and retrieval of knowledge from memory according to current problems. We demonstrate our approach in a set of experiments in which the robots capture knowledge from their tasks and are able to reuse such knowledge in subsequent tests.

INDUCTION OF CLASSIFIERS THROUGH NON-PARAMETRIC METHODS FOR APPROXIMATE CLASSIFICATION AND RETRIEVAL WITH ONTOLOGIES

This work concerns non-parametric approaches for statistical learning applied to the standard knowledge representation languages adopted in the Semantic Web context. We present methods based on epistemic inference that are able to elicit and exploit the semantic similarity of individuals in OWL knowledge bases. Specifically, a totally semantic and language-independent semi-distance function is introduced, whence also an epistemic kernel function for Semantic Web representations is derived. Both the measure and the kernel function are embedded in non-parametric statistical learning algorithms customized for coping with Semantic Web representations. Particularly, the measure is embedded in a k-Nearest Neighbor algorithm and the kernel function is embedded in a Support Vector Machine. The implemented algorithms are used to perform inductive concept retrieval and query answering. An experimentation on real ontologies proves that the methods can be effectively employed for performing the target tasks, and moreover that it is possible to induce new assertions that are not logically derivable.

Monitoring the execution of robot plans using semantic knowledge

Even the best laid plans can fail, and robot plans executed in real world domains tend to do so often. The ability of a robot to reliably monitor the execution of plans and detect failures is essential to its performance and its autonomy. In this paper, we propose a technique to increase the reliability of monitoring symbolic robot plans. We use semantic domain knowledge to derive implicit expectations of the execution of actions in the plan, and then match these expectations against observations. We present two realizations of this approach: a crisp one, which assumes deterministic actions and reliable sensing, and uses a standard knowledge representation system ( LOOM); and a probabilistic one, which takes into account uncertainty in action effects, in sensing, and in world states. We perform an extensive validation of these realizations through experiments performed both in simulation and on real robots.