MnM: Ontology Driven Semi-automatic and Automatic Support for Semantic Markup

Vargas-Vera, Maria; Motta, Enrico; Domingue, John; Lanzoni, Mattia; Stutt, Arthur; Ciravegna, Fabio

doi:10.1007/3-540-45810-7_34

Cited by 189 publications

(120 citation statements)

References 9 publications

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“…Ideally, what is required is a knowledge extraction capability in which relevant domain knowledge can be automatically extracted from source documents. We are currently exploring an ability to augment textual resources with semantic annotations in order to identify the entities contained in the resource document [38,39]; however, we also intend to explore an additional capability for the extraction of relational information. While the extraction of relational information is significantly more complex than entity recognition, the advantage of the technique is that it provides a much richer substrate for information fusion and knowledge processing, especially in situations where information content is dispersed across multiple source documents.…”

Section: Content Acquisitionmentioning

confidence: 99%

AKTiveSA: A Technical Demonstrator System For Enhanced Situation Awareness

Smart

Russell

Shadbolt

et al. 2007

The Computer Journal

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The issue of improved situation awareness is a key concern for military agencies, promising to deliver strategic advantages in a variety of conflict and non-conflict scenarios. Improved situation awareness can benefit operational effectiveness by facilitating the planning process, improving the quality and timeliness of decisions, and providing better feedback regarding the strategic consequences of military actions. In this paper, we aim to show how a combination of semantic technologies and user interface design initiatives can be used to improve situation awareness in a simulated humanitarian relief scenario. We describe the development of a technical demonstrator system, the AKTiveSA TDS, which integrates a variety of knowledge technologies and visualization components within the context of a unitary application framework. We also describe our approach to scenario development, knowledge acquisition, ontology engineering and system design. Some specific problems encountered during system development are discussed, e.g. the performance overheads associated with rules-based processing, and potential solution strategies for these problems are presented alongside a description of future development activities.

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Section: Content Acquisitionmentioning

confidence: 99%

AKTiveSA: A Technical Demonstrator System For Enhanced Situation Awareness

Smart

Russell

Shadbolt

et al. 2007

The Computer Journal

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“…iii) By information extraction -we also have used a dedicated tool -MnM [27] developed in-house, which enables information extraction engines, such as Amilcare [4] to be integrated with the Magpie knowledge model component.…”

Section: Before Deploying Magpiementioning

confidence: 99%

“…MnM [27], is a tool, which incorporates a web browser and interfaces to web based ontology libraries and to information extraction engines. MnM enables users to attach information extraction mechanisms to ontological classes.…”

Section: Before Deploying Magpiementioning

confidence: 99%

“…Much less attention has been paid to the second problem, supporting the interpretation of web pages. Annotation technology [17,23,27] allows users to associate meta-information with web resources, which can then be used to facilitate their interpretation. While this technology provides a useful way to support group discussion and shared interpretation, it is nevertheless very limited.…”

Section: Introductionmentioning

confidence: 99%

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Semantic Layering with Magpie

Domingue

Džbor

Motta

2004

Handbook on Ontologies

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Browsing the web involves two main tasks: finding the right web page and then making sense of its content. A significant amount of research has gone into supporting the task of finding web resources through 'standard' information retrieval mechanisms, or semantics-enhanced search. Much less attention has been paid to the second problem. In this paper we describe Magpie, a tool which supports the interpretation of web pages. Magpie acts as a complementary knowledge source, which a reader can call upon to quickly gain access to any background knowledge relevant to a web resource. Magpie works by automatically associating an ontology based semantic layer to web resources, allowing relevant services to be directly invoked within a standard web browser. The functionality of Magpie is illustrated using examples of how it has been integrated with our lab's web resources.

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“…For instance, the Amaya editor implementing the Annotea framework [15], the CREAM-based Ont-O-Mat/Annotizer [13], MnM [24], and the SHOE framework [14]. The main difference between these efforts and the approach taken by Magpie and KIM lies in the origin of the annotations.…”

Section: Related Workmentioning

confidence: 99%

Collaborative Semantic Web Browsing with Magpie

Domingue

Džbor

Motta

2004

Lecture Notes in Computer Science

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Abstract. Web browsing is often a collaborative activity. Users involved in a joint information gathering exercise will wish to share knowledge about the web pages visited and the contents found. Magpie is a suite of tools supporting the interpretation of web pages and semantically enriched web browsing. By automatically associating an ontology-based semantic layer to web resources, Magpie allows relevant services to be invoked as well as remotely triggered within a standard web browser. In this paper we describe how Magpie trigger services can provide semantic support to collaborative browsing activities.

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MnM: Ontology Driven Semi-automatic and Automatic Support for Semantic Markup

Cited by 189 publications

References 9 publications

AKTiveSA: A Technical Demonstrator System For Enhanced Situation Awareness

AKTiveSA: A Technical Demonstrator System For Enhanced Situation Awareness

Semantic Layering with Magpie

Collaborative Semantic Web Browsing with Magpie

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