Scooner - Revision history

Ugur: /* Overview */

2020-06-23T23:02:06Z

‎Overview

Ugur: /* Introduction */

2020-06-23T23:00:23Z

‎Introduction

Manas: /* Overview */

2020-06-23T20:26:46Z

‎Overview

Manas: /* Overview */

2020-06-23T20:14:15Z

‎Overview

Amelie: /* Architecture and Components */

2018-05-07T16:32:35Z

‎Architecture and Components

Amelie at 16:17, 7 May 2018

2018-05-07T16:17:19Z

W007dhc at 22:29, 15 August 2014

2014-08-15T22:29:52Z

W007dhc: /* Project Team */

2013-09-21T01:42:47Z

‎Project Team

W007dhc: /* Publications */

2013-09-09T02:29:23Z

‎Publications

W007dhc: /* Publications */ Publication removed from the knoesis library

2013-09-06T21:52:07Z

‎Publications: Publication removed from the knoesis library

← Older revision		Revision as of 23:02, 23 June 2020
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	<embedvideo service="youtube">https://youtu.be/brOMCPdH80c</embedvideo>		<embedvideo service="youtube">https://youtu.be/brOMCPdH80c</embedvideo>
−
−	'''NOTE: To use the live version of Scooner, please contact us at ''scooner at knoesis period org'' for access credentials. For a screencast of Scooner in use, please visit: [https://www.dropbox.com/s/uhhkuouaqjh8njv/ScoonerCollab.mp4?dl=0 SCOONER Demo]. Note that this screencast does not include the new MeSH based filtering introduced recently (June 2011)'''.

	==Project Team==		==Project Team==

@@ Line 1: / Line 1: @@
 ==Introduction==
-The limitations of key word based search are well known in the information retrieval field.
+The limitations of keyword-based search are well known in the information retrieval field.
 These are more evident in life sciences where most of the reliable scientific information is spread
 across biomedical literature in the form textual journal articles. Unlike the Web, these journal
-articles are devoid of hyper links and multiple key word based searches need to be performed
+articles are devoid of hyperlinks and multiple keyword-based searches need to be performed
 while aggregating and organizing search results that the user finds interesting. This makes
 literature search a tedious task in life sciences.
 '''Knowledge-based search systems''' are proposed as an improvement over conventional search
-and have gained popularity especially given the availability of many expert curated vocabularies
+and have gained popularity especially given the availability of many expert-curated vocabularies
 and taxonomies in the biomedical domains. The different classes in a given taxonomy are used to
 provide faceted search over articles that contain the instances of these classes.
-These taxonomies and other forms of ontologies are mostly static blocks of well accepted consensual
+These taxonomies and other forms of ontologies are mostly static blocks of well-accepted consensual
 knowledge. Also, most of these standard ontologies have a limited number of predicates (or relationship
 types) such as "''part of''" and "''is a''". We believe the search process can benefit from recently published results that are not well

← Older revision		Revision as of 20:26, 23 June 2020
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	by Aug 2008. Initial evaluations of Scooner by researchers at the AFRL indicate that Scooner does better than NLM's PubMed search tool.		by Aug 2008. Initial evaluations of Scooner by researchers at the AFRL indicate that Scooner does better than NLM's PubMed search tool.

		+	<embedvideo service="youtube">https://youtu.be/brOMCPdH80c</embedvideo>

	'''NOTE: To use the live version of Scooner, please contact us at ''scooner at knoesis period org'' for access credentials. For a screencast of Scooner in use, please visit: [https://www.dropbox.com/s/uhhkuouaqjh8njv/ScoonerCollab.mp4?dl=0 SCOONER Demo]. Note that this screencast does not include the new MeSH based filtering introduced recently (June 2011)'''.		'''NOTE: To use the live version of Scooner, please contact us at ''scooner at knoesis period org'' for access credentials. For a screencast of Scooner in use, please visit: [https://www.dropbox.com/s/uhhkuouaqjh8njv/ScoonerCollab.mp4?dl=0 SCOONER Demo]. Note that this screencast does not include the new MeSH based filtering introduced recently (June 2011)'''.

← Older revision		Revision as of 20:14, 23 June 2020
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−	'''NOTE: To use the live version of Scooner, please contact us at ''scooner at knoesis period org'' for access credentials. For a screencast of Scooner in use, please visit: ~~http~~://~~knoesis~~.~~wright~~.~~edu~~/~~library~~/~~demos/scooner-demo~~/. Note that this screencast does not include the new MeSH based filtering introduced recently (June 2011)'''.	+	'''NOTE: To use the live version of Scooner, please contact us at ''scooner at knoesis period org'' for access credentials. For a screencast of Scooner in use, please visit: [https://www.dropbox.com/s/uhhkuouaqjh8njv/ScoonerCollab.mp4?dl=0 SCOONER Demo]. Note that this screencast does not include the new MeSH based filtering introduced recently (June 2011)'''.

	==Project Team==		==Project Team==

@@ Line 61: / Line 61: @@
 '''2. Triples Model Interface: ''' The triples in the original HPC knowledge base extracted at the Knoesis center are serialized using Lucene for efficient programmatic access through Java. The other set of triples obtained from NLM's BKR data set are hosted on a Virtuoso RDF triple store and are accessed programmatically using a Java interface to the SPARQL end point that provides triple access. Both the SPARQL end point and Lucene index access are abstracted out using the same interface that takes a set of entity IDs and returns the associated (inverse) triples.
-'''3. Spotter Service:''' Named entities in either of the data sets are spotted using an in-memory prefix tree data structure populated with the entity labels when the server is set up to host Scooner. The prefix tree algorithm spots the longest available label in the set of the entities of the knowledge base. For example when it encounters the phrase "long term memory formation", and if both "memory formation" and "long term memory formation" are valid entities in the data set, it spots longest label that is, "long term memory formation". But the next time in the abstract, it sees the same phrase, "memory formation" is spotted instead because an entity is spotted only once in each abstract.
+'''3. Spotter Service:''' Named entities in either of the data sets are spotted using an in-memory prefix tree data structure populated with the entity labels when the server is set up to host Scooner. The prefix tree algorithm spots the longest available label in the set of the entities of the knowledge base. For example, when it encounters the phrase "long term memory formation", and if both "memory formation" and "long term memory formation" are valid entities in the data set, it spots longest label that is, "long term memory formation". But the next time in the abstract, it sees the same phrase, "memory formation" is spotted instead because an entity is spotted only once in each abstract.
 '''4. Ext JS GUI: ''' Finally, the ExtJS Javascript framework is used to generate the user interface that captures and responds to users' interactions with the system. Search sessions can be broken down by users into various projects depending on their needs. The triples browsed, trails created, searches made, and abstracts added to the workbench of each project: all persist in a MySQL database to be retrieved by users after subsequent logins. Users can create sub-projects, export projects to a publicly visible dashboard and import projects from the dashboard. An option to filter the work bench using the triples and trails is also incorporated. The ExtJS framework provides maintainability to the GUI components of the system and also assists in facilitating cross browser compatibility. Currently, Scooner runs on Tomcat 6 and has been tested in Firefox, IE, and Chrome.

@@ Line 57: / Line 57: @@
 '''0. Knowledge base: ''' We assume the presence of a knowledge base in the form of triples preferably in RDF. Having a good schema for the data really helps with Scooner's functionality. Other formats can also be converted to RDF with some pre-processing. Currently we are hosting two data sets both related to the domain of human performance and cognition. One is built at the Knoesis center using NLP and pattern based extraction techniques and is serialized as a Lucene index. The second data set comes from shallow parsing and rule based extraction techniques developed at the NLM and is stored in a Virtuoso triple store.
-'''1. PubMed full text index: ''' PubMed is NLM's service that facilitates access to article citation information from a number of biomedical journals including those from the MEDLINE database. Every year around December, PubMed releases a consolidated baseline (taking into account deletions and revisions) list of abstracts whose status has been thoroughly verified by officials at the NLM. After that, an update is released every few days to account for new articles that are added as the years progresses. Our full text index consists of abstracts released throguh PubMed until June 2011. We are working on automatically indexing new updates. The IndexWrapper service supports two types of queries. The first query is a traditional query with fields including title, abstract, author, year, and pmid. The second query is a phrasal range query which looks for a co-located presence of a subject, predicate, and object labels in the abstract field to retrieve abstracts that contain (information relevant to) the triple. Indexing is done using Lucene API where the SnowballAnalyzer is used for the abstract and the title fields to incorporate stemming, while KeywordAnalyzer is used for the remaining fields for exact matching. Also, boosting on the year of publication is performed at query time so that relevant articles that are more recent are ranked higher.
+'''1. PubMed full text index: ''' PubMed is NLM's service that facilitates access to article citation information from a number of biomedical journals including those from the MEDLINE database. Every year around December, PubMed releases a consolidated baseline (taking into account deletions and revisions) list of abstracts whose status has been thoroughly verified by officials at the NLM. After that, an update is released every few days to account for new articles that are added as the years progresses. Our full text index consists of abstracts released through PubMed until June 2011. We are working on automatically indexing new updates. The IndexWrapper service supports two types of queries. The first query is a traditional query with fields including title, abstract, author, year, and pmid. The second query is a phrasal range query which looks for a co-located presence of a subject, predicate, and object labels in the abstract field to retrieve abstracts that contain (information relevant to) the triple. Indexing is done using Lucene API where the SnowballAnalyzer is used for the abstract and the title fields to incorporate stemming, while KeywordAnalyzer is used for the remaining fields for exact matching. Also, boosting on the year of publication is performed at query time so that relevant articles that are more recent are ranked higher.
 '''2. Triples Model Interface: ''' The triples in the original HPC knowledge base extracted at the Knoesis center are serialized using Lucene for efficient programmatic access through Java. The other set of triples obtained from NLM's BKR data set are hosted on a Virtuoso RDF triple store and are accessed programmatically using a Java interface to the SPARQL end point that provides triple access. Both the SPARQL end point and Lucene index access are abstracted out using the same interface that takes a set of entity IDs and returns the associated (inverse) triples.

← Older revision		Revision as of 22:29, 15 August 2014
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	# [http://knoesis.wright.edu/amit Amit Sheth], [http://knoesis.wright.edu/researchers/topher Christopher Thomas], Pankaj Mehra, [http://knoesis.org/library/resource.php?id=863 Continuous Semantics to Analyze Real-Time Data], IEEE Internet Computing, vol. 14, no. 6, pp. 84-89, Nov./Dec. 2010, doi:10.1109/MIC.2010.137		# [http://knoesis.wright.edu/amit Amit Sheth], [http://knoesis.wright.edu/researchers/topher Christopher Thomas], Pankaj Mehra, [http://knoesis.org/library/resource.php?id=863 Continuous Semantics to Analyze Real-Time Data], IEEE Internet Computing, vol. 14, no. 6, pp. 84-89, Nov./Dec. 2010, doi:10.1109/MIC.2010.137
	# [http://knoesis.wright.edu/researchers/topher Christopher J. Thomas], Pankaj Mehra, [http://knoesis.wright.edu/researchers/wenbo Wenbo Wang], [http://knoesis.wright.edu/amit Amit P. Sheth], Gerhard Weikum, and Victor Chan. Automatic Domain Model Creation Using Pattern-Based Fact Extraction, Kno.e.sis Center Technical Report 2010		# [http://knoesis.wright.edu/researchers/topher Christopher J. Thomas], Pankaj Mehra, [http://knoesis.wright.edu/researchers/wenbo Wenbo Wang], [http://knoesis.wright.edu/amit Amit P. Sheth], Gerhard Weikum, and Victor Chan. Automatic Domain Model Creation Using Pattern-Based Fact Extraction, Kno.e.sis Center Technical Report 2010
		+
		+	[[Category:Information Exploration]]
		+	[[Category:Literature based discovery]]

@@ Line 44: / Line 44: @@
 ==Project Team==
-Undergraduate Students: Alan Smith, Paul Fultz <br>
+Undergraduate Students: Alan Smith, Paul Fultz III<br>
 Graduate Students: [http://knoesis.wright.edu/students/delroy/ Delroy Cameron], [http://knoesis.wright.edu/students/topher/ Christopher Thomas], [http://knoesis.wright.edu/students/wenbo/ Wenbo Wang]<br>
 Postdocs: [http://knoesis.wright.edu/researchers/rama/ Ramakanth Kavuluru]<br>

@@ Line 70: / Line 70: @@
 # [http://knoesis.wright.edu/researchers/delroy D. Cameron], [http://knoesis.wright.edu/aboutus/people?first=Ramakanth&last=Kavuluru R. Kavuluru], O. Bodenreider, [http://pablomendes.com/ P. N. Mendes], [http://knoesis.wright.edu/amit A. P. Sheth], [http://knoesis.wright.edu/tkprasad/ K. Thirunarayan], [http://knoesis.org/library/resource.php?id=1577 Semantic Predications for Complex Information Needs in Biomedical Literature], 5th International Conference on Bioinformatics and Biomedicine BIBM11, Atlanta GA, November 12-15, 2011
 # [http://knoesis.wright.edu/researchers/delroy D. Cameron], [http://pablomendes.com/ P. N. Mendes], [http://knoesis.wright.edu/amit A. P. Sheth], V. Chan, [http://knoesis.org/library/resource.php?id=796 Semantics-Empowered Text Exploration for Knowledge Discovery], 48th ACM Southeast Conference, ACMSE2010, Oxford Mississippi, April 15-17, 2010.
 # [http://knoesis.wright.edu/researchers/wenbo Wenbo Wang], [http://knoesis.wright.edu/researchers/topher Christopher Thomas], [http://knoesis.wright.edu/amit Amit Sheth], Victor Chan, [http://knoesis.org/library/resource.php?id=799 Pattern-Based Synonym and Antonym Extraction], 48th ACM Southeast Conference, ACMSE2010, Oxford Mississippi, April 15-17, 2010.
 # [http://knoesis.wright.edu/amit  Amit Sheth], [http://knoesis.wright.edu/researchers/topher Christopher Thomas], Pankaj Mehra, [http://knoesis.org/library/resource.php?id=863 Continuous Semantics to Analyze Real-Time Data], IEEE Internet Computing, vol. 14, no. 6, pp. 84-89, Nov./Dec. 2010, doi:10.1109/MIC.2010.137
 # [http://knoesis.wright.edu/researchers/topher Christopher J. Thomas], Pankaj Mehra, [http://knoesis.wright.edu/researchers/wenbo Wenbo Wang], [http://knoesis.wright.edu/amit Amit P. Sheth], Gerhard Weikum, and Victor Chan. Automatic Domain Model Creation Using Pattern-Based Fact Extraction, Kno.e.sis Center Technical Report 2010