Knowledge entry as the graphical assembly of components

Clark, Peter E.; Thompson, John A.; Barker, Ken; Porter, Bruce; Chaudhri, Vinay K.; Rodríguez, Andrés; Thomere, Jerome; Mishra, Sunil Kumar; Gil, Yolanda; Hayes, Pat; Reichherzer, Thomas

doi:10.1145/500737.500745

Cited by 34 publications

(27 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The idea of reusing and combining chunks of knowledge rather than building knowledge bases from scratch has later been adopted by the knowledge engineering community for building real-world knowledge bases (e.g. see [7]). McIlraith and Amir argue that a modularization of knowledge bases has also advantages for reasoning, even if the modularization is done a posteriori.…”

Section: Related Workmentioning

confidence: 99%

Reasoning and change management in modular ontologies

Stuckenschmidt

Klein

2007

Data & Knowledge Engineering

View full text Add to dashboard Cite

The benefits of modular representations are well known from many areas of computer science. In this paper, we concentrate on the benefits of modular ontologies with respect to local containment of terminological reasoning. We define an architecture for modular ontologies that supports local reasoning by compiling implied subsumption relations. We further address the problem of guaranteeing the integrity of a modular ontology in the presence of local changes. We propose a strategy for analyzing changes and guiding the process of updating compiled information.Key words: Ontologies, Reasoning, Distributed Knowledge Representation, Change Management MotivationCurrently, research in the area of the semantic web is in a state where ontologies are ready to applied in real applications such as semantic web portals, information retrieval or information integration. In order to lower the effort of building ontology-based applications, there is a clear need for a representational and computational infrastructure in terms of general purpose tools for building, storing and accessing ontologies. A number of such tools have been developed, i.e. ontology editors [1,2], reasoning systems [3,4] and more recently storage and query systems (e.g. [5]). Most of these tools, however,

show abstract

Section: Related Workmentioning

confidence: 99%

Reasoning and change management in modular ontologies

Stuckenschmidt

Klein

2007

Data & Knowledge Engineering

View full text Add to dashboard Cite

show abstract

“…The work reported by Chaudhri et al (2004) and Clark et al (2001) allow experts to update knowledge in a knowledge base by using graphical representation. These systems demonstrated encouraging results.…”

Section: Related Workmentioning

confidence: 99%

“…Another system (Dimitrova et al, 2008) uses the notion of controlled natural language (Rabbit) for guiding experts for ontology development. Similar to Chaudhri et al (2004) and Clark et al (2001), this system also requires training by users. This system facilitates the users for developing ontologies in different ways.…”

Section: Related Workmentioning

confidence: 99%

An ontology development approach using concept maps driven by automatic term extraction

Iqbal

Murad

Sharef

2017

IJICT

View full text Add to dashboard Cite

A number of ontology engineering methodologies have been proposed to date. Distinct methodologies rely on different techniques and activities for developing ontologies. The concept mapping technique has been recently used for developing ontologies related to different domains. However, the existing approaches using concept mapping do not make use of any automatic term extraction process to ease the overall ontology development process and their evaluation procedures are not robust to specifically meet the need for ontology designs emerging from concept maps. This paper proposes a new approach which uses the concept mapping technique coupled with a term extraction engine. The engine automatically extracts the candidate terms from the competency questions which reduces ontology development time and efforts. Furthermore, considering the importance of evaluation, a robust evaluation procedure is proposed which validates the ontologies at both the formal level and the graphical level.

show abstract

“…Other systems focus on detecting errors in the knowledge specified by the user (Gil and Melz, 1996;McGuinness et al, 2000;Kim and Gil, 1999). Some systems use a variety of elicitation techniques to acquire descriptive knowledge (Gaines and Shaw, 1993;Clark et al, 2001;Shadbolt and Burton, 1989) often in semi-formal forms. There are some isolated reports of users with no formal background in computer science that are now able to use acquisition systems to build sizeable knowledge bases (Kim and Gil, 2000;Clark et al, 2001).…”

Section: Introductionmentioning

confidence: 99%

“…Some systems use a variety of elicitation techniques to acquire descriptive knowledge (Gaines and Shaw, 1993;Clark et al, 2001;Shadbolt and Burton, 1989) often in semi-formal forms. There are some isolated reports of users with no formal background in computer science that are now able to use acquisition systems to build sizeable knowledge bases (Kim and Gil, 2000;Clark et al, 2001). However, the majority of the burden of the acquisition task still remains with the user: users have to decide how and when to enter knowledge, and of what nature.…”

Section: Introductionmentioning

confidence: 99%

Incorporating tutoring principles into interactive knowledge acquisition

Kim¹,

Gil²

2007

International Journal of Human-Computer Studies

Self Cite

View full text Add to dashboard Cite

This paper argues that interactive knowledge acquisition systems would benefit from a tighter and more thorough incorporation of tutoring and learning principles. Current acquisition systems learn from users in a passive manner, and could instead be designed to incorporate the proactive capabilities that one expects of a good student. We first describe our analysis of the literature on teacher-student interaction and present a compilation of tutoring and learning principles that are relevant to interactive knowledge acquisition systems. We then point out what tutoring and learning principles have been used to date in the acquisition literature, though unintentionally and implicitly, and discuss how a more thorough and explicit representation of these principles would help improve how computers learn from users. We present our design and an initial implementation of an acquisition dialogue system called SLICK that represents acquisition principles and goals explicitly and declaratively, making the system actively reason about various acquisition tasks and generate its interactions dynamically. Finally, we discuss promising directions in designing acquisition systems by structuring interactions with users according to tutoring and learning principles.

show abstract

Knowledge entry as the graphical assembly of components

Cited by 34 publications

References 5 publications

Reasoning and change management in modular ontologies

Reasoning and change management in modular ontologies

An ontology development approach using concept maps driven by automatic term extraction

Incorporating tutoring principles into interactive knowledge acquisition

Contact Info

Product

Resources

About