Showing papers on "Ontology (information science) published in 2022"

Service planning oriented efficient object search: A knowledge-based framework for home service robot

Abstract: In the unstructured family environment, robots are expected to provide various services to improve the quality of human life, based on the performance of specific action sequences generated by service planning. This paper focuses on one of the greatest challenges in service planning that is aimed at accomplishing the service tasks by generating appropriate object sequences to guide the robot on searching the corresponding target objects efficiently and reasonably. A well-structured knowledge-based framework of object search is proposed in our approach as well as taking into account the multi-domain knowledge of applying object, scene, and service in design. In order to improve the searching efficiency and reasonability, an ontology-based hierarchical and interrelated knowledge structure is formed to support the implementation of complicated service planning with either single or multiple tasks. The proposed framework is tested by comprehensive experiments, and the performance is evaluated with other mainstream methods in both simulation and real-world environments. The experimental results demonstrate the feasibility and effectiveness of applying this knowledge-based framework to efficient object searching aspect in service planning.

Introduction to Sentiment Analysis Covering Basics, Tools, Evaluation Metrics, Challenges, and Applications

Abstract: Sentiment analysis has been applied to the datasets collected from social networking websites to get valuable insights. The exemplary growth of social networking has attracted researchers, and there has been a vast contribution in this area. In this chapter, we discuss the basics of sentiment analysis and its methodology, including data collection, data pre-processing, and feature extraction methods. Next, we discuss enhancement techniques for sentiment analysis, including text categorization, feature selection, data integration, ontology-based approaches, and so on. The chapter further provides information about available sentiment analysis tools, challenges, and evaluation metrics. We also discuss sentiment analysis applications and insights for further attention.

Making trouble with ontogenesis: Collaborative writing, becoming, and concept forming as event

Abstract: Through the authors’ continuing creative encounters with practices of collaborative writing, this article offers a troubling of approaches to ontology that inform a construction of simply human sel...

Constructing ontologies by mining deep semantics from XML Schemas and XML instance documents

Abstract: With the development of the Semantic Web and Artificial Intelligence techniques, ontology has become a very powerful way of representing not only knowledge but also their semantics. Therefore, how to construct ontologies from existing data sources has become an important research topic. In this paper, an approach for constructing ontologies by mining deep semantics from eXtensible Markup Language (XML) Schemas (including XML Schema 1.0 and XML Schema 1.1) and XML instance documents is proposed. Given an XML Schema and its corresponding XML instance document, 34 rules are first defined to mine deep semantics from the XML Schema. The mined semantics is formally stored in an intermediate conceptual model and then is used to generate an ontology at the conceptual level. Further, an ontology population approach at the instance level based on the XML instance document is proposed. Now, a complete ontology is formed. Also, some corresponding core algorithms are provided. Finally, a prototype system is implemented, which can automatically generate ontologies from XML Schemas and populate ontologies from XML instance documents. The paper also classifies and summarizes the existing work and makes a detailed comparison. Case studies on real XML data sets verify the effectiveness of the approach.

Semantic coupling of path planning and a primitive action of a task plan for the simulation of manipulation tasks in a virtual 3D environment

Abstract: This work deals with the simulation of complex manipulation tasks in virtual environments. Validating such complex tasks, possibly to be performed under strong geometric constraints, requires considering task and path planning jointly. The contribution of this work focuses on using task-related information at the path planning level. We propose an ontology-based approach to a) model the 3D environment where the simulated task is executed, based on an original multi-level environment model involving higher abstraction level data than the purely geometric models traditionally used, and b) automatically define path planning queries for the primitive actions of a task plan, together with task-related geometric constraints on these queries. This approach allows the improvement of the state of the art from two points of view. First, our joint task and path planning approach allows the improvement of path planning through better semantic control of the path planning process. Second, if compared to hard-coded geometric constraints, the proposed ontology-based approach introduces a more flexible way of defining geometric constraints through an inference process, and can be adapted to different applications of manipulation tasks.

A Fuzzy Declarative Approach to Classify Unlabeled Short Texts Based on Automatically Constructed WordNet Ontologies

Abstract: In this paper, we present an approach to categorizing short texts which only require, as user input, the category names defined using an ontology of terms automatically extracted from WordNet and modeled as a set of proximity equation. The use of a fuzzy extension of Prolog allows us to develop an efficient text classifier in which the classification method is explainable. The results of the experiments showed that the proposed method achieved a reasonable good performance.

Ontology for Preliminary Detection of COVID-19

Abstract: During the recent COVID-19 pandemic, healthcare workers are trying to manage this medical emergency at war footing. It is the need of the time to have a COVID-19 detection procedure that can be used to stothe kiosk-based operation is shownp the spread among the general people. This paper proposes an ontology-based technique to detect and preliminarily diagnose suspected COVID-19 patients without direct intervention of healthcare professionals. The proposed procedure works as a part of remote healthcare services and tries to respond timely to the situation. The ontology is used for handling general people, rather than patients, to detect COVID-19 primarily.

Extracting Operational Insights from Everyday IoT Data, Generated by IoT Sensors Over LoRaWAN

Abstract: The operational management of a smart city environment using IoT data is a challenge. Resource descriptor framework (RDF) techniques interpret relationships in activity profiles to generate better situationally aware operational information. The data analysis uses a system process approach, to create an effective smart city monitoring and management system. The two-year project uses a LoRaWAN network of sensor nodes across a 127-ha campus, using customised sensors, producing a daily operational record of three important criteria: (a) human traffic count, (b) temperature and (c) humidity, at each of the seventy-one carefully selected locations. An ontology-based semantic framework is used to describe, in process-driven operational terms, campus-related activity, including before, during and in the waning days of the COVID-19 pandemic. This paper shows how the technology is used on the Macquarie University Campus and environs, process optimization methods tried, predictive techniques developed to improve operational effectiveness in an evolving smart city environment.

Enriching WordNet with Subject Specific Out of Vocabulary Terms Using Existing Ontology

Abstract: WordNet is a huge repository being used as a tool in various fields. With an increasing number of applications referring to WordNet as a dictionary, several attempts have been made to update it. The paper proposes to extend the huge repository by adding words and relationships derived from students’ class notes through wikidata. These terms can be phrases, technical terms, or any subject specific terminology appearing in students’ notes of a specific subject. Although various WordNet enriching techniques are available, it is for the first time that subject specific terminology is being added. The resulting version of WordNet has some very common phrases and technical terms along with the generic terms. Making subject specific and generic terms available in a hierarchy can improve the accuracy of various applications like text summarization and clustering for text belonging to a specific domain.

Conceptualization, Visualization, and Modeling of Ontologies for Elementary Kinematics

Abstract: Ontology, in general, is the study of understanding the way of being. In terms of computer science and engineering, it is the way of explaining a concept in both ways, i.e. human-understandable and machine-understandable formats. This mainly explains the relationship between two entities which are considered. Ontology is conceived where the process has conceptualization and visualization. The modeling of Ontology is done with Protege—an interface which codes the given Ontology into a .owl file. Flow Mapping has been accomplished for the visualization of the problem taken. In this paper, the relationships between displacement, distance, velocity, speed, acceleration, and retardation are intricated into a dynamic Ontology model, with the condition that all the objects considered obey Newton’s laws. Kinematics mainly deals with the motion of objects in a plane or space, which is an elementary concept in physics. The relationship between the objects is established using various attributes such as “Speed is inversely proportional to the time passed;” here the “inversely proportional” describes the relationship between the entities speed and time. An Ontology model with a reuse ratio of 0.0706 is proposed in the paper.

BOLD: an ontology-based log debugger for C programs

Abstract: Program debugging is often an ad hoc activity, with a combination of manual and semi-automated processing. A challenge posed by debugging is the lack of standardized procedures for instrumenting, representing, and analyzing the execution trace. Further, debugging is often low level, getting into the nitty-gritty details of variables and their semantics—rather than at a high-level. The presence of libraries only exacerbates these issues. We propose BOLD, an Ontology-based Log Debugger, to unify various activities involved in the debugging of sequential C programs. The syntactical information of programs can be represented as Resource Description Framework (RDF) triples. BOLD automatically instruments programs by querying these triples. It represents the execution trace of the program also as RDF triples called trace triples. BOLD’s novel high-level reasoning abstracts these triples as spans. A span gives a way of examining the values of a particular variable over certain portions of the program execution. The properties of the spans are defined formally as a Web Ontology Language ontology. A developer can debug a given buggy program by querying the trace triples and reasoning with the spans. To empirically assess BOLD, we debugged the programs in standard Software-artifact Infrastructure Repository. Experiments related to debugging through automated reasoning show improvement in conciseness of the developers’ specifications and run time performance of BOLD compared to gdb-Python.

Components of an Ontology for a Smart Village

Abstract: The development of the science of smart villages is dependent on how well we describe a village, and the attributes that make it ‘smart’. This chapter aims to present components of an ontology for a ‘smart village’. We critically explore examples of characteristics of a smart city proposed by researchers in the existing literature and discuss their adaptation for the construction of an ontology for a smart village. We propose the usage of specific terms that can express the domain knowledge top-down by first highlighting general concepts and then specialized ones. The chapter also emphasizes learning which is key and unique to our model of smart villages.

Approaching Faith Behind the Prison Walls

Abstract: This chapter identifies the key approaches used to collect data; this will be done by firstly exploring the research philosophy. Moving on, ontology and epistemology; research design; and reflexivity are considered.

Research on Security Awareness to Protect Data Through Ontology and Cloud Computing

Abstract: Nowadays, providing security for the data is the major concern especially in the cloud storage unit. Therefore, a security aware mechanism has to be developed to improve the security using ontology concepts in the cloud storage most of the existing servers used in the networks are used for storing and accessing the data, but these servers are built in a centralized manner, providing security for the data is a key requirement for the servers. The major threat in the existing servers is the data stored in the servers are subjected to hacking. This paper presents a technique to efficiently store the data in cloud by using concepts of ontology and Secret Sharing Scheme (SSS) has been introduced in the proposed framework to protect the data. This paper presents a model whereby the data can be securely stored for the longer duration in the distributed environments. Ultimately the proposed security scheme has achieved better performance and outperformed the conventional technique.

Data-Driven Design of Smart Product

Abstract: This chapter presents a structured framework that can guide designers to develop smart products with context-smartness, network-smartness, and service-smartness, the definitions of which are explained from the perspective of product development. The framework is developed based on the theoretical foundation of the Situated Function-Behavior-Structure (FBS) ontology. A systematic step-by-step design process is prescribed to leverage various data operations to support design operations involved in the process of designing a smart product with stronger context-smartness, network-smartness, and service-smartness. Some existing design methods and emerging design tools are also incorporated into the framework.