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![Page 1: Knowledge Enabled Information and Services Science Ontology supported Knowledge Discovery in the field of Human Performance and Cognition Kno.e.sis Center.](https://reader035.fdocuments.net/reader035/viewer/2022062801/56649e305503460f94b21225/html5/thumbnails/1.jpg)
Knowledge Enabled Information and Services Science
Ontology supported Knowledge Discovery in the field of Human Performance and Cognition
Kno.e.sis Center http://knoesis.org
Wright State University
C. Thomas, P. Mendes, D. Cameron, A. Sheth, TK Prasad
Thanks, C. Ramakrishnan
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Knowledge Enabled Information and Services Science
Motivation
• Speed up the “search” part of research• Develop a system that helps in focused
exploration of a domain
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Knowledge Enabled Information and Services Science
Cognition-related concepts
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Knowledge Enabled Information and Services Science
Subject Predicate Object search
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Knowledge Enabled Information and Services Science
Subject Predicate ? search
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Knowledge Enabled Information and Services Science
Demo
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Knowledge Enabled Information and Services Science
Motivation
Traditionally the creation of domain models is a long and expensive process
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Knowledge Enabled Information and Services Science
Motivation
• For this reason we propose a largely automatic domain model generation on the basis of existing knowledge repositories and text corpora, namely MeSH, MedLine and Wikipedia.
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Knowledge Enabled Information and Services Science
Results
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Knowledge Enabled Information and Services Science
Full Class Hierarchy
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Knowledge Enabled Information and Services Science
Top Levels by importance (#Concepts)
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Knowledge Enabled Information and Services Science
Selective Hierarchy – CogSci/NeuroSci
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Knowledge Enabled Information and Services Science
Zooming in on Cognition-related concepts
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Knowledge Enabled Information and Services Science
Biomolecules-related concepts
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Knowledge Enabled Information and Services Science
Technologies
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Knowledge Enabled Information and Services Science
Steps
1. Carve a focused domain model out of Wikipedia
2. Identify mentions of entities and relationships in the relevant scientific literature (Pubmed abstracts) to support non-hierarchical guidance.
3. Map extracted entity mentions to concepts and extracted predicates to relationships
4. Applications to access research literature guided by the domain model.
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Knowledge Enabled Information and Services Science
Steps
1. Carve a focused domain model out of Wikipedia
2. Identify mentions of entities and relationships in the relevant scientific literature (Pubmed abstracts) to support non-hierarchical guidance.
3. Map extracted entity mentions to concepts and extracted predicates to relationships
4. Applications to access research literature guided by the domain model.
![Page 18: Knowledge Enabled Information and Services Science Ontology supported Knowledge Discovery in the field of Human Performance and Cognition Kno.e.sis Center.](https://reader035.fdocuments.net/reader035/viewer/2022062801/56649e305503460f94b21225/html5/thumbnails/18.jpg)
Knowledge Enabled Information and Services Science
Step 1: DoozerAn Expand and Reduce approach to Automatic domain model creation
Christopher Thomas, Pankaj Mehra, Roger Brooks, Amit Sheth
Knowledge Enabled Information and Services Science
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Knowledge Enabled Information and Services Science
Wisdom of the Crowds
• The most comprehensive and up to date account of the present state of knowledge is given by
Everybody= The Web in general= Blogs= Wikipedia
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Knowledge Enabled Information and Services Science
Goal:Harness the Wisdom of the
Crowds to Automatically define a domain with up-to-date
concepts
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Knowledge Enabled Information and Services Science
Overview - conceptual
• Expand and Reduce approach–Start with ‘high recall’ methods
• Exploration - Full text search• Exploitation – Node Similarity Method• Category growth
–End with “high precision” methods• Apply restrictions on the concepts found• Remove unwanted terms and categories
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Knowledge Enabled Information and Services Science
Expand - conceptually
Full text search on Article texts
Delete results with low Lucene score
Graph-based expansion
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Knowledge Enabled Information and Services Science
Node Similarity computation
s(a,b)
avg w N i(a) ,w N j (b) i, j N i (a ),N j (b ) M
|N (a )| ,|N (b )|
avg w N i(a) ,i
|N (a )|
w N j (b) j
|N (b )|
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Knowledge Enabled Information and Services Science
Collecting Instances
Seed Query
BWikipedia
Fulltext Concept Search (Somnath)
Graph Search
Graph Search(Denis)
Graph Search(Denis)
Graph Search(Denis)
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Knowledge Enabled Information and Services Science
Creating a Hierarchy
Seed Query
BWikipedia
Fulltext Concept Search (Somnath)
Graph Search
Graph Search(Denis)
Graph Search(Denis)
Graph Search(Denis)
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Knowledge Enabled Information and Services Science
Creating a Hierarchy
Seed Query
BWikipedia
Fulltext Concept Search (Somnath)
Graph Search
Graph Search(Denis)
Graph Search(Denis)
Graph Search(Denis)
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Knowledge Enabled Information and Services Science
Hierarchy Creation - summary
Seed Query
BWikipedia
Fulltext Concept Search (Somnath)
Graph Search
Graph Search(Denis)
Graph Search(Denis)
Graph Search(Denis)
Hierarchy Creation
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Knowledge Enabled Information and Services Science
Reduce steps
• Remove all terms that have low pertinence to the domain
• Intersect hierarchy with broader focus domain
• Reduce hierarchy depth
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Knowledge Enabled Information and Services Science
Remove unwanted individuals
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Knowledge Enabled Information and Services Science
Remove unwanted categories
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Knowledge Enabled Information and Services Science
Flatten categories
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Knowledge Enabled Information and Services Science
Snapshot of final Topic Hierarchy
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Knowledge Enabled Information and Services Science
Evaluation wrt. expert model
Evaluation wrt. MeSH versions of 2004 (04) and 2008 (08) for both the restricted and the full set of MeSH term
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Knowledge Enabled Information and Services Science
Steps
1. Carve a focused domain model out of Wikipedia
2. Identify mentions of entities and relationships in the relevant scientific literature (Pubmed abstracts) to support non-hierarchical guidance.
3. Map extracted entity mentions to concepts and extracted predicates to relationships
4. Applications to access research literature guided by the domain model.
![Page 35: Knowledge Enabled Information and Services Science Ontology supported Knowledge Discovery in the field of Human Performance and Cognition Kno.e.sis Center.](https://reader035.fdocuments.net/reader035/viewer/2022062801/56649e305503460f94b21225/html5/thumbnails/35.jpg)
Knowledge Enabled Information and Services Science
Challenges and Opportunities
• Vocabularies, Thesauri, Ontologies, exist in several fields– How can we use them?
• lexicon: Fish Oils, Raynaud’s Disease, etc.• types/labels: Fish Oils instance of Lipid• relationships between types: Lipid affects Disease
• Identification of simple ontology terms in text is not enough– Compound Entities– Complex Relationships
35
But
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Knowledge Enabled Information and Services Science
Challenge: Compound Entities
36
• Entities (MeSH terms) in sentences occur in modified forms• “adenomatous” modifies “hyperplasia”• “An excessive endogenous or exogenous stimulation” modifies “estrogen”
• Entities can also occur as composites of 2 or more other entities• “adenomatous hyperplasia” and “endometrium” occur as “adenomatous hyperplasia of the endometrium”
MeSH terms Modifier
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Knowledge Enabled Information and Services Science37
Extraction Algorithm
Relationship head
Subject head
Object head Object head
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Knowledge Enabled Information and Services Science
Extraction Approach
● Parse sentences with a dependency parser● Use a few domain-independent rules to
segment sentences into SubjPredObject
● Subjects and Objects represent compound entities
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Knowledge Enabled Information and Services Science39
Preliminary results
Subject types, inferred from the HEAD of the compound
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Knowledge Enabled Information and Services Science40
Extracted Triples
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Knowledge Enabled Information and Services Science
41
Representation – Resulting RDF
ModifiersModified entitiesComposite Entities
estrogen
An excessive endogenous or
exogenous stimulation
modified_entity1composite_entity1
modified_entity2
adenomatous hyperplasia
endometrium
hasModifier
hasPart
induces
hasPart
hasPart
hasModifier
hasPart
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Knowledge Enabled Information and Services Science
Using the generated RDF
platelet(D001792)
collagen(D003094)
migraine(D008881)
magnesium(D008274)
me_3142by_a_primary_abnormality_of_platelet_behavior
me_2286_13%_and_17%_adp_and_collagen_induced_platelet_aggregation
caused_by
hasPart
hasPart
stimulated
stimulatedhasPart
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Knowledge Enabled Information and Services Science
Steps
1. Carve a focused domain model out of Wikipedia
2. Identify mentions of entities and relationships in the research literature.
3. Map extracted entity mentions to concepts and extracted predicates to relationships
4. Applications to access research literature guided by the domain model.
![Page 44: Knowledge Enabled Information and Services Science Ontology supported Knowledge Discovery in the field of Human Performance and Cognition Kno.e.sis Center.](https://reader035.fdocuments.net/reader035/viewer/2022062801/56649e305503460f94b21225/html5/thumbnails/44.jpg)
Knowledge Enabled Information and Services Science
Step 3: Identifying Synonymy and Antonymy between Verbs for Relationship Matching
Christopher Thomas, Wenbo Wang
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Knowledge Enabled Information and Services Science
Motivation
• An ontology has clearly demarcated, formal relationships whereas natural language uses multiple ways of representing these relationships
• Focus on verbs expressing relationships• NLP-extracted triples use verbs to indicate
relationships, but are not aligned with the relationships yet.
• Align similar verbs Align extracted predicates to formal relationships
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Knowledge Enabled Information and Services Science
Examples of relationships to merge
• Dilation of the fourth ventricle indicated atrophy of the middle cerebellar peduncle. – E.g. < Dilation of the fourth ventricle >
indicate
<atrophy of the middle cerebellar peduncle>
• The lesions in the rolipram-treated group also showed increased astrogliosis and increased CREB phosphorylation in the activated microglia and astrocytes. – E.g. < lesions in the rolipram-treated group >
showed
< increased astrogliosis and increased CREB phosphorylation in the activated microglia and astrocytes >
GOOD
BAD indicate GOOD
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Knowledge Enabled Information and Services Science
Short Review – training set creation
• Normalization
• POS Tagging
• Synonyms & antonyms extraction
• Pattern extraction
• Pattern2Relationship matrix
• VerbPair2Pattern matrix
Remove phrases containing less than 2 verbs
Eating -> <VBG>, eats -> <VBZ>
ANT: Like <-> HateSYN: calculate <-> compute
He learns and memorizes materials
What is the correlation of this pattern and that relationship?
What is the correlation of this verb pair and that pattern?
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Knowledge Enabled Information and Services Science
Design
• Synonyms & antonyms extraction from Wordnet– A single verb has multiple meanings
• contract, take, get (be stricken by an illness, fall victim to an illness) "He got AIDS"; "She came down with pneumonia"; "She took a chill“
• film#1, shoot#4, take#16 (make a film or photograph of something) "take a scene"; "shoot a movie"
• Take has 42 meanings in Wordnet
– Some meanings of a verb are less frequently used
– If the number of meanings of a verb exceeds threshold, we will abandon this verb
– If the frequency of a specific meaning of a verb is low, we will abandon this meaning
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Knowledge Enabled Information and Services Science
Solution
• Do pattern-based text analysis to find predicates that are used in similar contexts and map them to upper-level relationships, e.g. those found in UMLS
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Knowledge Enabled Information and Services Science
Steps
1. Carve a focused domain model out of Wikipedia
2. Identify mentions of entities and relationships in the research literature.
3. Map extracted entity mentions to concepts and extracted predicates to relationships
4. Applications to access research literature guided by the domain model.
![Page 51: Knowledge Enabled Information and Services Science Ontology supported Knowledge Discovery in the field of Human Performance and Cognition Kno.e.sis Center.](https://reader035.fdocuments.net/reader035/viewer/2022062801/56649e305503460f94b21225/html5/thumbnails/51.jpg)
Trellis: Knowledge-Driven Web Browsing
Pablo N. Mendes, Cartic Ramakrishnan, Delroy Cameron and Amit P. Sheth
Knoesis Center, May 27 2009.
Step 4
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Introduction Background System Overview Demo Future Work
61
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Background Web Browsing
Search Keyword search
Point of access to related content
Sift Retrieve documents
Hyperlinks
Query Refinement
Search
Sift
62
and
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Background Observations
63
Human Cognition
Model
Information Need
(Documents)
Keyword
Entity
e1
e2
Relationship
e1
en
Pathway
Graduate
StudentResearch
Assistant
Turkey
San Diego
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System Overview New Browsing Paradigm
Search Navigate Context Organize results
Trellis Intricate support structure for vines Vines as information need/context through
navigation64
SearchNavigate/
StitchShare
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System Overview
65
Knowledge Base
{Pubmed Abstracts}
Trellis Interfac
e
SpotterIn
de
x
Workbench
Save/Publish
Ontology {NLM hand annotated
triples}
Fig.1 Trellis Architecture
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Magnesium -> isa -> Calcium Channel Blocker
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Calcium Channel Blockers -> prevent -> Migraine
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Magnesium -> isa -> Calcium Channel Blocker
Migraine
-> prevent -> Migraine
Magnesium -> prevent -> Migraine
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Future Work Ranking - Boosting Precision/Recall
User feedback Corpus Statistics
Scalability Information Extraction from natural language
Named Entity Recognition User driven
Knowledge Discovery
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Knowledge Enabled Information and Services Science
Thank you