• Aucun résultat trouvé

Clarification KBS as Consultation-Justification Mash Ups - Proposing A Novel Paradigm for All-in-One Knowledge-Based Systems

N/A
N/A
Info
Télécharger
Protected

Academic year: 2022

Partager "Clarification KBS as Consultation-Justification Mash Ups - Proposing A Novel Paradigm for All-in-One Knowledge-Based Systems"

Copied!
1
0
0

Chargement.... (Voir le texte intégral maintenant)

Texte intégral

(1)

Clarification KBS as Consultation-Justification Mash Ups

?

Proposing A Novel Paradigm for All-in-One Knowledge-based Systems

Martina Freiberg, Felix Herrmann, and Frank Puppe

Department of Artificial Intelligence and Applied Informatics, Institute of Computer Science, University of W¨urzburg, Am Hubland, D-97074 W¨urzburg, Germany

martina.freiberg@uni-wuerzburg.de felix.herrmann@uni-wuerzburg.de

frank.puppe@uni-wuerzburg.de

Abstract. Regarding knowledge-based systems (KBS), the seminal paradigm—perfectly mimicking human experts—is gradually replaced by an increasing demand for enabling users to influence the reasoning pro- cess according to their domain knowledge. Therefore, we propose a novel KBS paradigm:Clarification KBSas a mash up type of consultation and justification interaction—intended to foster active user participation ac- cording to users’ competency, the KBS’ explicability, and the support for learnability. We introduce the theoretical concept of clarification KBS, as well as appropriate UI-/interaction variants. Further, we discuss the results of iteratively evolving and evaluatingITree, a specific clarification KBS implementation for the legal domain.

Keywords: Knowledge-based System, Clarification, Justification, User Participation, Learnability, Explicability

Resubmission of Freiberg, M., Herrmann, F., Puppe, F.: Clarification KBS as Consultation-Justification Mash Ups—Proposing A Novel Paradigm for All- in-One Knowledge-based Systems. Submitted to: Proceedings of International Conference on Knowledge Engineering and Ontology Development (KEOD 2014)

?Copyright c 2014by the paper’s authors. Copying permitted only for private and academic purposes. In: T. Seidl, M. Hassani, C. Beecks (Eds.): Proceedings of the LWA 2014 Workshops: KDML, IR, FGWM, Aachen, Germany, 8-10 September 2014, published at http://ceur-ws.org

310

Références

Télécharger maintenant ( PDF - 1 Page - 1.10 MB )

Documents relatifs

Activating Thinking Skills with Mash-Up Research Environments: A Proposal

While these ensembles of artifacts (widget ensembles) have already its own value, the focus of this work is seen how this Mash-Up Research Environments can activate

Toward a New Paradigm for User Interaction on the Semantic Web to Support Life Sciences Investigation

For instance, RDFScape allows interactive browsing of knowledge base contents (Fig. 3a) that can be customized depending on user needs: users can decide which resources to

Adding mash-up based tailorability to VLEs for scripted Collaborative Learning

In the “Script Authoring” use case, educators describe their learning scenarios using an IMS-LD script authoring tool such as Collage 3 (interaction 1a), defining the sequence

Publishing and Using Ontologies as Mash-Up Services

This relates to the more general challenge of the Semantic Web today: on- tologies are typically published as files without support for using them in appli- cations. Each

VETO! A Peer-to-Peer Paradigm Based Medical Knowledge Management System

system gathers data from the peer community (here: healthcare providers) and checks it against domain specific knowledge of

A New Haptic Interaction Paradigm

In this position paper, we discuss how mid-air haptics are uniquely suited to explore wholly independent haptic interaction paradigms which allow the user to interact with a

RDFDigest+: A Summary-driven System for KBs Exploration

Dependence has not only to do with distance. Like TF-IDF, the basic hypothe- sis is that the greater the influence of a property on identifying a corresponding instance is, the

A Diagnosis and Repair Framework for DL-LiteA KBs

More specifically, we performed three sets of experiments: (i) the first set verified that our framework can handle millions of violations in real-world ABoxes that scale up to