DAMLJessKB: a tool for reasoning with the Semantic Web

Kopena, Joseph B.; Regli, William C.

doi:10.1109/mis.2003.1200733

Cited by 65 publications

(31 citation statements)

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“…As shown in Figure 5, the Jess inference engine is at the center of the 717 reasoning module. We have used DAMLJessKB to load DAML+OIL ontologies 718 into Jess (for details see Kopena and Regli, 2003). DAMLJessKB uses Jena tool-719 kit to convert ontologies into RDF triples that are converted to Jess facts.…”

Section: Methodsmentioning

confidence: 99%

Ontology-Based User Modeling in an Augmented Audio Reality System for Museums

Hatala

Wakkary

2005

User Model User-Adap Inter

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Abstract. Ubiquitous computing is a challenging area that allows us to further our under-7 standing and techniques of context-aware and adaptive systems. Among the challenges is 8 the general problem of capturing the larger context in interaction from the perspective 9 of user modeling and human-computer interaction (HCI). The imperative to address this 10 issue is great considering the emergence of ubiquitous and mobile computing environments. 11This paper provides an account of our addressing the specific problem of supporting func-12 tionality as well as the experience design issues related to museum visits through user mod-13 eling in combination with an audio augmented reality and tangible user interface system. 14 This paper details our deployment and evaluation of ec (h) Introduction 25Fundamental to human-computer interaction (HCI) is the design of interactive 26 systems that support people's goals and respond to individual backgrounds. In 27 ubiquitous computing it is equally important to consider the influence of context 28 on people's interactions and experiences. The intent is, as Fischer argues "to say 29 the 'right' thing at the 'right' time in the 'right' way" (Fischer, 2001). A critical 30 factor in ubiquitous computing is that what is perceived as "right" is largely med-31 iated by the context within which the users find themselves. 32In the area of user-adapted interaction, user modeling has attempted to address 33 many issues related to HCI. Fischer provides a clear account of the successes and 34 * This paper has not been submitted elsewhere in identical or similar form, nor will it be during the first three months after its submission to UMUAI. is the general problem of capturing the larger context in interaction (see Fischer, 36 2001, pp. 80-81). The imperative to address this issue is great considering the 37 emergence of ubiquitous and mobile computing environments. This paper provides 38 an account of addressing the specific problem of supporting functionality as well 39 as the experience design issues related to museum visits through user modeling in 40 combination with an audio augmented reality and tangible user interface system. 41We developed and tested a museum guide prototype, known as ec(h)o in order 42 to research interaction design, user modeling, and adaptive information retrieval 43 approaches that respond to the richness of a museum visit and the museum con-44 text. 45Our aim is to support the limited input common to tangible user interfaces 46 while maintaining rich and adaptive information output via a three-dimensional 47 audio display. We believe an integrated modeling technique that is weighted toward 48 modeling of implicit communication works well with a tangible user interface in 49 creating a playful and discovery-rich experience. We believe this approach com-50 bined with ontologies and a rule-based system for information retrieval provides 51 a richness of information that is responsive to the context and unique aspects of 52 the museum visitor's interaction. 5...

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Section: Methodsmentioning

confidence: 99%

Ontology-Based User Modeling in an Augmented Audio Reality System for Museums

Hatala

Wakkary

2005

User Model User-Adap Inter

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“…ChickenFoot extracts information from the web using the command find() . [18] Piggy Bank is also an extension of the Firefox browser which is used for reducing the gap between what we have right now and the semantic web. The users of this software send scripts along with the regular expression to extract the data from the web by using the URLs of the desired websites.…”

Section: An Intelligent Survey Of Personalized Information Retrieval mentioning

confidence: 99%

An Intelligent Survey of Personalized Information Retrieval using Web Scraper

Dastidar¹,

Banerjee²,

Sengupta³

2016

IJEME

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“…A great deal of attention is being paid to inferencing on the Semantic Web in terms of the technicalities of the description logics and artificial reasoning routines that it embodies and how to produce technical tools capable of making use of this (Akerkar & Lingras, 2008;Cheng, Yang, & Cheng, 2011;Golbreich, 2004;Kopena & Regli, 2003;Stoilos, Grau, & Horrocks, 2010). However, much of this intricate work requires either a substantial background in logic, or in programming, or quite oen in both, to understand the work and make a contribution.…”

Section: How To Improve Inferencing On a Data Setmentioning

confidence: 99%

Inference and Linking on the Humanist’s Semantic Web

Simpson

2014

SRC

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The Semantic Web promises that the pools of semantic data it interweaves together will enable people to find information that they could not otherwise find by revealing knowledge not explicitly visible in the distributed source data. In order for this promise to be fulfilled within the humanities, the Semantic Web data being created must have certain features, but what are they? This article provides some background on Semantic Web inferencing and then argues that there are three things that humanists can do to prepare their data to participant in this sort of inference generation: add more data, reciprocate links across repositories, and add metadata specifically to support inferencing.

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DAMLJessKB: a tool for reasoning with the Semantic Web

Cited by 65 publications

References 7 publications

Ontology-Based User Modeling in an Augmented Audio Reality System for Museums

Ontology-Based User Modeling in an Augmented Audio Reality System for Museums

An Intelligent Survey of Personalized Information Retrieval using Web Scraper

Inference and Linking on the Humanist’s Semantic Web

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