Research on Concept Clustering of Order Task Ontology based on Modified PART

2014 ◽  
Vol 9 (8) ◽  
Author(s):  
Yi Liu ◽  
Tianri Wang ◽  
Mingzhong Yang
Keyword(s):  
Order Task ◽  
Concept Clustering ◽  
Task Ontology

Task-order representations in dual tasks: Separate or integrated with component task sets?

2021 ◽  
pp. 174702182110184
Author(s):  
Lynn Huestegge ◽  
Mareike A Hoffmann ◽  
Tilo Strobach
Keyword(s):  
Temporal Order ◽  
Spatial Task ◽  
Task Characteristics ◽  
Switch Costs ◽  
Task Order ◽  
Order Task ◽  
Component Task ◽  
Specific Manner ◽  
Oculomotor Responses ◽  
Task Sets

In situations requiring the execution of two tasks at around the same time, we need to decide which of the tasks should be executed first. Previous research has revealed several factors that affect the outcome of such response order control processes, including bottom-up factors (e.g., the temporal order of the stimuli associated with the two tasks) and top-down factors (e.g., instructions). In addition, it has been shown that tasks associated with certain response modalities are preferably executed first (e.g., temporal prioritisation of tasks involving oculomotor responses). In this study, we focused on a situation in which task order has to be unpredictably switched from trial to trial and asked whether task-order representations are coded separately or integrated with the component task sets (i.e., in a task-specific manner). Across three experiments, we combined two tasks known to differ in prioritisation, namely an oculomotor and a manual (or pedal) task. The results indicated robust task-order switch costs (i.e., longer RTs when task order was switched vs. repeated). Importantly, the data demonstrate that it is possible to show an asymmetry of task-order switch costs: While these costs were of similar size for both task orders in one particular experimental setting with specific spatial task characteristics, two experiments consistently indicated that it was easier for participants to switch to their prioritised task order (i.e., to execute the dominant oculomotor task first). This suggests that in a situation requiring frequent task-order switches (indicated by unpredictable changes in stimulus order), task order is represented in an integrated, task-specific manner, bound to characteristics (here, associated effector systems) of the component tasks.

Number of Shoes Tested During a Running Shoe Mass Perception Task May Not Influence Accuracy

2020 ◽  
Vol 127 (1-4) ◽  
pp. 23-29
Author(s):  
James G. Saxton ◽  
Joel G. Greenya ◽  
Christopher L. Kliethermes ◽  
David S. Senchina
Keyword(s):  
Young Adult ◽  
Experimental Design ◽  
Rank Order ◽  
Mass Accuracy ◽  
Testing Session ◽  
Adult Males ◽  
Order Task ◽  
Running Shoes ◽  
Design Factors ◽  
Analogue Scales

Commercially available running shoes differ in terms of their relative masses. It is unclear how well consumers may be able to judge mass differences from wearing alone, though previous studies suggest that perceptual outcomes may be influenced by experimental design factors such as the length of time worn. The purpose of this study was to investigate how the number of shoes used in a testing session impacts wearers' mass perceptual accuracy. Forty-eight young adult males ran for 5 min in 4 pairs of shoes (their own running shoes plus 3 unfamiliar pairs) before being asked whether an unfamiliar running shoe was heavier or lighter than their own, and to indicate perceptions of shoe heaviness (mass), comfort, stability, and temperature using visual analogue scales (VAS). A subset (n=18) was also asked to provide global rank orderings after wearing all 4 pairs of shoes. Participants were 67% accurate in the heavier/lighter task and 64% accurate in the global rank order task. Global rank order scores and VAS heaviness marks were significantly and positively correlated. Mass accuracy scores (n=48) were then compared to a previous study (n=25) performed by the same investigators using the same methods but with 6 pairs of shoes instead of 4. No difference in accuracy scores for either the heavier/lighter comparisons or global rank order scores between the study populations was found, suggesting that the number of test shoes may not influence mass perception accuracy.

scholarly journals Roles of Ontologies in Cartography

2019 ◽  
Vol 1 ◽  
pp. 1-2
Author(s):  
Otakar Čerba
Keyword(s):  
Information Science ◽  
Open Data ◽  
Domain Ontology ◽  
Linked Open Data ◽  
Semantic Description ◽  
General Terms ◽  
Application Ontology ◽  
Domain Ontologies ◽  
Task Ontology ◽  
Logical Rules

<p><strong>Abstract.</strong> Ontologies (in computer science and information science) represent the essential tool for a formalised description of concepts, data, information, knowledge and other entities as well as relations among them. Their history is relatively old. The idea of ontologies in informatics started in the mid-1970s, but ontology as the philosophical discipline connected to existence and nature of reality came from the Ancient Greek. The ontologies as a part of knowledge-based systems were discussed in the 1980s. In 1993 Thomas R. Gruber defined ontology in information science as "a specification of a conceptualisation". After that, the first languages and formats coding ontologies have been developed, and massive construction process of ontologies began. For example, the Basel Register of Thesauri, Ontologies and Classifications presents about 700 ontologies and more the 1000 other tools with a similar character. The theory of ontologies and development as ontologies are entirely on a high level. However, their implementation (especially in several domains) is in its infancy.</p><p> For example, in the geographical domain, there are many ontologies (called geo-ontologies) such as FAO (Food and Agriculture Organization of the United Nations) Geopolitical Ontology, ontologies of USGS (United States Geological Survey) or ontologies of Ordnance Survey. However, their implementation is usually limited by home organisations, which provide for the management, development and updating of ontologies. In many cases, they are not an integral part of Linked Open Data (LOD). This fact can be considered as the critical shortcoming because only in connection with Linked Open Data and free data sharing and combining the main benefits of ontologies (emphasis on a semantic description, derivation of new knowledge or complete independence) can be fully appreciated.</p><p> This document has to describe opportunities for the implementation of ontologies in cartography. The purpose of the implementation of an ontology depends on various types of ontologies. There are defined four essential types of ontologies - upper ontologies, domain ontologies, task ontologies and application ontologies.</p><p> Upper and domain ontologies contain general terms (in the case of upper ontologies) and domain-specific terms (in case of domain ontologies). Annotation properties (labels, definitions or comments) usually describe these terms, interconnected by data properties and/or object properties and restricted by logical axioms. Such ontologies are usually provided as vocabularies or thesauri. They can be used in two ways. Domain ontologies can describe cartography as a science or human activity. In previous years several paper and articles were discussing the term "cartography" and its position in Linked Open Data space, including various ontologies, ontological description of cartographic knowledge or ontological comparison of various definitions of the term "map". These activities can aim for the development of a cartographic knowledge base or building of semantic tools such as multilingual thesauri or vocabularies.</p><p> The second way consists in the exploitation of domain ontologies containing semantic information about data visualising by a map. In this case, such domain ontology can be used as a tool for development of a legend of a map, especially in a case where a map is focused on particular issues. If such ontology is published as Linked Open Data, it is possible to generate such legend automatically as well as to reflect any changes. Such solution enables an efficient interconnection of cartographers and domain experts. Domain ontologies can be used for a definition of logical rules restricting and describing data, information and knowledge. These rules and knowledge extracted in the reasoning process can be applied during the map development. They can provide information on possible combinations of data or a hierarchy of objects visualising by a map and described by a map legend.</p><p> The task ontologies are not focused on a complicated system of classes (representing types of object) as domain ontologies. They are usually based on instances (individuals) representing concrete data objects. Therefore they can be used as data resources. However, the overwhelming majority of geo-ontologies does not contain any geometry (coordinates) to enable a visualisation in a map. This apparent disadvantage shows the importance of LOD. If a task ontology is published as 5-star LOD (RDF /Resource Description Framework/ data with interconnection to external data resources published on the Web under an open license), and identity relation (links to equivalent object published in other data sets) are filled, it is possible to find in LOD space geometries as well as other additional information and attributes for visualization.</p><p> The remaining type of ontologies is called application ontology. It is a combination of both previous kinds &amp;ndash; domain ontology and task ontology. Application ontologies usually provide vocabularies as well as data stored in an ontological structure. Such a combination allows controlling data correctness and integrity by a set of logical rules. This functionality is emphasised by the rich possibilities of the Description Logic (quantifiers or types of relations). Their implementation in cartography corresponds with methods discussed in previous paragraphs. The main advantage of the approach using an application ontology consists in a homogeneous interconnection of data and semantics.</p><p> The real implementation of ontologies, other semantic resources and Linked Open Data principles in cartography can make web mapping development process more efficient, because the normalised semantic description enables to automatize many activities, including a derivation of new data and knowledge or checking of data as well as cartographic processes. Such an approach can bring the cartography closer to knowledge bases and systems and realise ideas of real-time cartography.</p><p> The research reported in this paper has been supported by the following project &amp;ndash; Sustainability support of the centre NTIS &amp;ndash; New Technologies for the Information Society, LO1506, Czech Ministry of Education, Youth and Sports.</p>

On the Ontology of a Decision Support System in Health Informatics

2011 ◽  
pp. 120-142 ◽  
Author(s):  
Pirkko Nykanen
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Health Informatics ◽  
System Development ◽  
Systems Science ◽  
Problem Analysis ◽  
Application Characteristics ◽  
Task Ontology ◽  
Knowledge Creation And Sharing

A decision support system can be approached from two major disciplinary perspectives, those of information systems science (ISS) and artificial intelligence (AI). We present in this chapter an extended ontology for a decision support system in health informatics. The extended ontology is founded on related research in ISS and AI and on performed case studies in health informatics. The ontology explicates relevant constructs and presents a vocabulary for a decision support system, and emphasises the need to cover environmental and contextual variables as an integral part of decision support system development and evaluation methodologies. These results help the system developers to take the system’s context into account through the set of defined variables that are linked to the application domain. This implies that domain and application characteristics, as well as knowledge creation and sharing aspects, are considered at every phase of development. With these extensions the focus in decision support systems development shifts from a task ontology towards a domain ontology. This extended ontology gives better support for development because from it follows that a more thorough problem analysis will be performed.

Task Ontology-Based Human-Computer Interaction

2009 ◽  
pp. 950-960
Author(s):  
Kazuhisa Seta
Keyword(s):  
Problem Solving ◽  
Real World ◽  
Practical Knowledge ◽  
Building Blocks ◽  
Research Field ◽  
Knowledge Based Systems ◽  
Engineering Research ◽  
Knowledge Based ◽  
Task Ontology ◽  
Problem Solving Task

In ontological engineering research field, the concept of “task ontology” is well-known as a useful technology to systemize and accumulate the knowledge to perform problem-solving tasks (e.g., diagnosis, design, scheduling, and so on). A task ontology refers to a system of a vocabulary/ concepts used as building blocks to perform a problem-solving task in a machine readable manner, so that the system and humans can collaboratively solve a problem based on it. The concept of task ontology was proposed by Mizoguchi (Mizoguchi, Tijerino, & Ikeda, 1992, 1995) and its validity is substantiated by development of many practical knowledge-based systems (Hori & Yoshida, 1998; Ikeda, Seta, & Mizoguchi, 1997; Izumi &Yamaguchi, 2002; Schreiber et al., 2000; Seta, Ikeda, Kakusho, & Mizoguchi, 1997). He stated: …task ontology characterizes the computational architecture of a knowledge-based system which performs a task. The idea of task ontology which serves as a system of the vocabulary/concepts used as building blocks for knowledge-based systems might provide an effective methodology and vocabulary for both analyzing and synthesizing knowledge-based systems. It is useful for describing inherent problem-solving structure of the existing tasks domain-independently. It is obtained by analyzing task structures of real world problem. ... The ultimate goal of task ontology research is to provide a theory of all the vocabulary/concepts necessary for building a model of human problem solving processes. (Mizoguchi, 2003) We can also recognize task ontology as a static user model (Seta et al., 1997), which captures the meaning of problem-solving processes, that is, the input/output relation of each activity in a problem-solving task and its effects on the real world as well as on the humans’ mind.

Capturing a Conceptual Model for End-User Programming: Task Ontology As a Static User Model

User Modeling ◽  
1997 ◽  
pp. 203-214 ◽  
Author(s):  
Kazuhisa Seta ◽  
Mitsuru Ikeda ◽  
Osamu Kakusho ◽  
Riichiro Mizoguchi
Keyword(s):  
Conceptual Model ◽  
User Model ◽  
End User ◽  
End User Programming ◽  
Task Ontology

Browsing Large Concept Lattices through Tree Extraction and Reduction Methods

2013 ◽  
Vol 9 (4) ◽  
pp. 16-34 ◽  
Author(s):  
Cassio Melo ◽  
Bénédicte Le-Grand ◽  
Marie-Aude Aufaure
Keyword(s):  
Fault Tolerance ◽  
Formal Concept Analysis ◽  
Concept Analysis ◽  
Hasse Diagram ◽  
Formal Concept ◽  
Concept Lattices ◽  
Direct Graph ◽  
Concept Clustering ◽  
Reduction Methods ◽  
Tree Extraction

Browsing concept lattices from Formal Concept Analysis (FCA) becomes a problem as the number of concepts can grow significantly with the number of objects and attributes. Interpreting the lattice through direct graph-based visualisation of the Hasse diagram rapidly becomes difficult and more synthetic representations are needed. In this work the authors propose an approach to simplify concept lattices by extracting and visualising trees derived from them. The authors further simplify the browse-able trees with two reduction methods: fault-tolerance and concept clustering.

Enhancing User Experience with Context-Dependent Tasks in Smart Home

2010 ◽  
pp. 612-627
Author(s):  
Hongbo Ni ◽  
Xingshe Zhou ◽  
Zhiwen Yu ◽  
Daqing Zhang
Keyword(s):  
Pervasive Computing ◽  
Smart Home ◽  
Rule Based ◽  
Computing Environments ◽  
Context Dependent ◽  
The Right ◽  
High Level ◽  
Dependent Tasks ◽  
Task Ontology ◽  
Average User

The vision of pervasive computing is floating into the domain of the household and aims to assist inhabitants (users) to live more conveniently and harmoniously. Due to the dynamic and heterogeneous nature of pervasive computing environments, it is difficult for an average user to obtain right service and information in the right place at the right time. This chapter proposes a context-dependent task approach to address the challenge. The most important component is its task model, which provides an adequate high-level description of user-oriented tasks and their related contexts. Leveraging the model, multiple entities can easily exchange, share, and reuse their knowledge. The conversion of OWL task ontology specifications to the First-Order Logic (FOL) representations is presented. The performance of FOL rule-based deducing in terms of task number, context size, and time is evaluated. Finally, we present a task supporting system (TSS) to aid an inhabitant’s tasks in light of his or her lifestyle and environment conditions in smart home.

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