Human Performance and Cognition Ontology - Revision history

Jibril: /* Phase 1 Results and Future Work Presentation */

2016-02-04T14:08:34Z

‎Phase 1 Results and Future Work Presentation

Jibril at 13:49, 26 May 2015

2015-05-26T13:49:22Z

Jibril: /* Phase 1 Results and Future Work Presentation */

2015-05-19T20:51:24Z

‎Phase 1 Results and Future Work Presentation

Rama: /* Domain Hierarchy Creation */

2011-07-26T19:30:49Z

‎Domain Hierarchy Creation

Rama: /* Scooner: Knowledge-Based Search and Browsing Application */

2011-07-26T19:30:24Z

‎Scooner: Knowledge-Based Search and Browsing Application

Rama: /* Domain Hierarchy Creation */

2011-07-26T19:30:06Z

‎Domain Hierarchy Creation

Rama: /* Phase 1 Results and Future Work Presentation */

2011-06-22T16:20:48Z

‎Phase 1 Results and Future Work Presentation

Rama: /* Publications */

2011-02-28T15:45:48Z

‎Publications

Rama at 20:13, 21 February 2011

2011-02-21T20:13:39Z

Rama at 19:54, 21 February 2011

2011-02-21T19:54:27Z

@@ Line 24: / Line 24: @@
 {{#widget:SlideShare
-|url=http://www.slideshare.net/slideshow/embed_code/key/bHnvamnr38QmBG
+|id=6976186
 |width=425
 |height=355

@@ Line 25: / Line 25: @@
 {{#widget:SlideShare
 |url=http://www.slideshare.net/slideshow/embed_code/key/bHnvamnr38QmBG
-|width=400
+|width=425
-|height=348
+|height=355
 |padding=25px
 }}

@@ Line 24: / Line 24: @@
 {{#widget:SlideShare
-|doc=scooner-talk-feb2011-110220211753-phpapp01
+|url=http://www.slideshare.net/slideshow/embed_code/key/bHnvamnr38QmBG
 }}

@@ Line 40: / Line 40: @@
 ===Domain Hierarchy Creation===
-The Wikipedia corpus contains a vast category graph on top of its articles. We build the domain hierarchy by using an "expand and reduce" paradigm (Doozer [2]) that allows us to first explore and exploit the concept space before reducing the concepts that were initially deemed interesting to those that are closest to the actual domain of interest. The extracted hierarchy describes the HPC domain as specified by the AFRL scientists with a keyword-based domain description. Later the model was pruned manually to remove ambiguous categories which, although relevant, are not found essential to the domain. Please see Doozer's [wiki page] for further details.
+The Wikipedia corpus contains a vast category graph on top of its articles. We build the domain hierarchy by using an "expand and reduce" paradigm (Doozer [2]) that allows us to first explore and exploit the concept space before reducing the concepts that were initially deemed interesting to those that are closest to the actual domain of interest. The extracted hierarchy describes the HPC domain as specified by the AFRL scientists with a keyword-based domain description. Later the model was pruned manually to remove ambiguous categories which, although relevant, are not found essential to the domain. Please see [http://wiki.knoesis.org/index.php/Doozer Doozer's wiki] for further details.
 ===Triple Extraction===

← Older revision		Revision as of 19:30, 26 July 2011
Line 54:		Line 54:

	A literature search and browsing application, [http://wiki.knoesis.org/index.php/Scooner Scooner (version 1)], based on the knowledge base was developed to explore PubMed abstracts [3]. The 4th component of the architecture diagram above shows the implementational aspects of Scooner including the technologies used. While the full functionality of the conventional search has been incorporated, this has been further enhanced with additional browsable links created using knowledge base entities that are spotted in the retrieved search results, via their relationships, to facilitate navigation. This provides substantial improvement over the search provided by PubMed: In Scooner, users can navigate between abstracts via the relationships among concepts mentioned in the abstracts, thus providing webpage-like navigation over raw text abstracts that are devoid of hyperlinks (and hence were unconnected hitherto). In other words, this is a concrete realization of what we have termed the Relationship Web [1]. Moreover, using a search based on the triple component labels, abstracts retrieved when browsing via relationships are very likely to be those from which the relationships were extracted. As such, Scooner lets users verify relationships and understand them in the relevant surrounding context. Users can also create new meaningful trails by combining individual triples. The workbench in Scooner facilitates a central aggregation of important abstracts imported for further review. The work bench can be filtered to only show only those abstracts that pertain to a selected set of triples or trails. Additionally, collaborative features were incorporated using which users can create persistent search projects, write comments on abstracts they find relevant, and share the (sub) projects with other users on a public dashboard. The Scooner application has been tested by a group of 5 researchers at the AFRL who reported satisfaction with its current functionality and provided critical suggestions to provide finer control over projects shared on the dashboard. The revised version that addresses these suggestions is being deployed in AFRL/RHPB and is currently in use. For a screencast of Scooner in use, please visit: http://knoesis.wright.edu/library/demos/scooner-demo/		A literature search and browsing application, [http://wiki.knoesis.org/index.php/Scooner Scooner (version 1)], based on the knowledge base was developed to explore PubMed abstracts [3]. The 4th component of the architecture diagram above shows the implementational aspects of Scooner including the technologies used. While the full functionality of the conventional search has been incorporated, this has been further enhanced with additional browsable links created using knowledge base entities that are spotted in the retrieved search results, via their relationships, to facilitate navigation. This provides substantial improvement over the search provided by PubMed: In Scooner, users can navigate between abstracts via the relationships among concepts mentioned in the abstracts, thus providing webpage-like navigation over raw text abstracts that are devoid of hyperlinks (and hence were unconnected hitherto). In other words, this is a concrete realization of what we have termed the Relationship Web [1]. Moreover, using a search based on the triple component labels, abstracts retrieved when browsing via relationships are very likely to be those from which the relationships were extracted. As such, Scooner lets users verify relationships and understand them in the relevant surrounding context. Users can also create new meaningful trails by combining individual triples. The workbench in Scooner facilitates a central aggregation of important abstracts imported for further review. The work bench can be filtered to only show only those abstracts that pertain to a selected set of triples or trails. Additionally, collaborative features were incorporated using which users can create persistent search projects, write comments on abstracts they find relevant, and share the (sub) projects with other users on a public dashboard. The Scooner application has been tested by a group of 5 researchers at the AFRL who reported satisfaction with its current functionality and provided critical suggestions to provide finer control over projects shared on the dashboard. The revised version that addresses these suggestions is being deployed in AFRL/RHPB and is currently in use. For a screencast of Scooner in use, please visit: http://knoesis.wright.edu/library/demos/scooner-demo/
−

	==Publications==		==Publications==

@@ Line 60: / Line 60: @@
-#Amit Sheth and Cartic Ramakrishnan, Relationship Web: Blazing Semantic Trails between Web Resources, IEEE Internet Computing, July-August 2007, pp. 84-88.
+#Amit Sheth and Cartic Ramakrishnan, [http://knoesis.wright.edu/library/publications/SR07-IEEEIC-RelationshipWeb.pdf Relationship Web: Blazing Semantic Trails between Web Resources], IEEE Internet Computing, July-August 2007, pp. 84-88.
-#Christopher Thomas, Pankaj Mehra, Roger Brooks and Amit Sheth. Growing Fields of Interest -Using an Expand and Reduce Strategy for Domain Model Extraction. In Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, Sydney, 2008, pp. 496-502.
+#Christopher Thomas, Pankaj Mehra, Roger Brooks and Amit Sheth. [http://knoesis.wright.edu/students/topher/publications/Growing_fields_of_interest.pdf Growing Fields of Interest -Using an Expand and Reduce Strategy for Domain Model Extraction]. In Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, Sydney, 2008, pp. 496-502.
-#D. Cameron, P. N. Mendes, A. P. Sheth, V. Chan, Semantics-Empowered Text Exploration for Knowledge Discovery, 48th ACM Southeast Conference, ACMSE2010, Oxford Mississippi, April 15-17, 2010.
+#D. Cameron, P. N. Mendes, A. P. Sheth, V. Chan, [http://knoesis.wright.edu/library/download/CMSC.4.15.10.ACMSE10.pdf Semantics-Empowered Text Exploration for Knowledge Discovery], 48th ACM Southeast Conference, ACMSE2010, Oxford Mississippi, April 15-17, 2010.
 #Christopher J. Thomas, Pankaj Mehra, Ramakanth Kavuluru, Amit P. Sheth, Wenbo Wang, and Gerhard Weikum. Automatic Domain Model Creation using Linked Open Data and Pattern-Based Information Extraction, Kno.e.sis Center Technical Report, 2010.

← Older revision		Revision as of 19:54, 21 February 2011
Line 56:		Line 56:
	===Scooner: Knowledge-Based Search and Browsing Application===		===Scooner: Knowledge-Based Search and Browsing Application===

−	A literature search and browsing application, Scooner (version 1), based on the knowledge base was developed to explore PubMed abstracts [3]. The 4th component of the architecture diagram above shows the implementational aspects of Scooner including the technologies used. While the full functionality of the conventional search has been incorporated, this has been further enhanced with additional browsable links created using knowledge base entities that are spotted in the retrieved search results, via their relationships, to facilitate navigation. This provides substantial improvement over the search provided by PubMed: In Scooner, users can navigate between abstracts via the relationships among concepts mentioned in the abstracts, thus providing webpage-like navigation over raw text abstracts that are devoid of hyperlinks (and hence were unconnected hitherto). In other words, this is a concrete realization of what we have termed the Relationship Web [1]. Moreover, using a search based on the triple component labels, abstracts retrieved when browsing via relationships are very likely to be those from which the relationships were extracted. As such, Scooner lets users verify relationships and understand them in the relevant surrounding context. Users can also create new meaningful trails by combining individual triples. The workbench in Scooner facilitates a central aggregation of important abstracts imported for further review. The work bench can be filtered to only show only those abstracts that pertain to a selected set of triples or trails. Additionally, collaborative features were incorporated using which users can create persistent search projects, write comments on abstracts they find relevant, and share the (sub) projects with other users on a public dashboard. The Scooner application has been tested by a group of 5 researchers at the AFRL who reported satisfaction with its current functionality and provided critical suggestions to provide finer control over projects shared on the dashboard. The revised version that addresses these suggestions is being deployed in AFRL/RHPB and is currently in use. For a screencast of Scooner in use, please visit: http://knoesis.wright.edu/library/demos/scooner-demo/	+	A literature search and browsing application, [http://wiki.knoesis.org/index.php/Scooner Scooner (version 1)], based on the knowledge base was developed to explore PubMed abstracts [3]. The 4th component of the architecture diagram above shows the implementational aspects of Scooner including the technologies used. While the full functionality of the conventional search has been incorporated, this has been further enhanced with additional browsable links created using knowledge base entities that are spotted in the retrieved search results, via their relationships, to facilitate navigation. This provides substantial improvement over the search provided by PubMed: In Scooner, users can navigate between abstracts via the relationships among concepts mentioned in the abstracts, thus providing webpage-like navigation over raw text abstracts that are devoid of hyperlinks (and hence were unconnected hitherto). In other words, this is a concrete realization of what we have termed the Relationship Web [1]. Moreover, using a search based on the triple component labels, abstracts retrieved when browsing via relationships are very likely to be those from which the relationships were extracted. As such, Scooner lets users verify relationships and understand them in the relevant surrounding context. Users can also create new meaningful trails by combining individual triples. The workbench in Scooner facilitates a central aggregation of important abstracts imported for further review. The work bench can be filtered to only show only those abstracts that pertain to a selected set of triples or trails. Additionally, collaborative features were incorporated using which users can create persistent search projects, write comments on abstracts they find relevant, and share the (sub) projects with other users on a public dashboard. The Scooner application has been tested by a group of 5 researchers at the AFRL who reported satisfaction with its current functionality and provided critical suggestions to provide finer control over projects shared on the dashboard. The revised version that addresses these suggestions is being deployed in AFRL/RHPB and is currently in use. For a screencast of Scooner in use, please visit: http://knoesis.wright.edu/library/demos/scooner-demo/