This document discusses PowerMagpie, a semantic browsing tool. It recognizes named entities using ontologies and provides interpretation support by locating additional contextual information. It aims to discover interesting terms from text and relate them to ontologies while hiding unnecessary information. The document suggests ways PowerMagpie could improve, such as processing entity definitions, clustering entities and ontologies, and discovering relations across ontologies. It also discusses using conceptual spaces to represent concepts as regions to measure similarity through distance.

1. PowerMagpie
Laurian Gridinoc
Knowledge Media Institute, The Open University
2009/03/10

2. Semantic Browsing
semantic links, not syntactic ones
provide interpretation support

3. Magpie (2003)
ontology-driven named entity recognition
semantic services
http://projects.kmi.open.ac.uk/magpie/main.html

5. PowerMagpie (2007)
find ontologies at runtime: Watson (2006)
provide “interpretation support”?
http://watson.kmi.open.ac.uk/
http://powermagpie.open.ac.uk/

6. PowerMagpie

8. Interpretation
A process of locating and making additional information
explicit in a way that makes it coherent and “look
orderly”
Information always comes in packages expressing
certain points of view
Nelson, T. H. (1997). The future of information. Ideas,
Connections, and the Gods of Electronic Literature.

9. Interpretation
The application of a certain point of view is usually
subject to subscribing to some ‘sense of order’
Humans exhibit innate senses of order, and use
different systems of order to filter out or transform the
facts that do not fit the expected order.
To understand a point of view, one need to recognise
first the primary systems of order, as other abstract
systems of order may compose upon them.
(Ted Nelson)

10. On semantic web statements reflect specific points of
view which usually adhere to certain ontologies as
systems of order

11. PowerMagpie
What it does? What it should do?
discover interesting hide URIs and
terms superfluous relations and
concepts
relate them with
ontologies less trees/graphs, more
text/tagclouds
show some ontological
information persist annotations
add RDFa annotations “semantic links”

12. How?
get more instance data (LOD)
process all the literals of an entity’s CBD, show a
tagcloud as entity’s definition
cluster entities
cluster ontologies (by domain?)
show at most key terms from an ontology, or key
related terms
discover relations across ontologies
(scarlet.open.ac.uk)

13. Visualisation developed by Takayuki Goto,
Knowledge as Media Group (KasM) Tokyo.
http://www-kasm.nii.ac.jp/

16. Medical Ontologies
Epidemiological O.
Geographical O.

17. Conceptual Spaces
Euclidian space, highly dimensional
quality dimensions
concepts as (convex) regions
objects as points
similarity = distance
(Gärdenfors, P. (2004) Conceptual Spaces: The
Geometry of Thought)

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19. <http://example.com/#xpointer(string-range(/html/body/p[2],quot;documentationquot;))>
<http://openguid.net/rdf#identical>
<http://www.daml.org/2002/01/experiences/experiences.daml#documentation> .

20. Next
de-couple UI from back-end
RESTful API
sample implementation with Mozilla Ubiquity
build on top of Talis Platform

21. Thank you.
http://purl.org/net/laur