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Module 16
Semantic Search
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Module 16 schedule
9.45-11.00• xxx
• Xxx
11.00-11.15 Coffee break
11.15-12.30 • xxx
• Xxx
12.30-14.00Lunch Break
14.00-16.00• xxx
• xxx
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Module 16 outline
• Traditional approaches to search and retrieval
• Semantic annotation & search
• Overview of KIM and LifeSKIM platforms
• Demos
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Traditional approaches to search and
retrieval
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IR models
• Boolean (set-theoretic)
• Documents and queries are represented as sets (of
terms/keywords)
• Retrieval is based on set intersection
• Advantages
• Easy to implement
• Disadvantages
• Difficult to rank results
• no term weighting
#5
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IR models (2)
• Algebraic
• Documents and queries are represented as vectors in
a multidimensional space (one dimension per
term/keyword)
• Retrieval is based on vector similarities
• Cosine similarity
• Advantages
• Simple model
• Ranking & Term weights
• Disadvantages
• Documents with similar topic but different vocabulary are not
associated#6
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Precision & Recall
• Precision
• Measure of the quality of results
• What % of the retrieved documents are relevant to the
query?
• Recall
• Measure of the completeness of results
• What % of the documents which are relevant to the
query are retrieved?
#7
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Classical IR limitations
• Example
• Query – “Documents about a telecom companies in
Europe related to John Smith from Q1 or Q2/2010”
• Document containing “At its meeting on the 10th of
May, the board of Vodafone appointed John G. Smith
as CTO” will not match
• Classical IR will fail to recognise that
• Vodafone is a mobile operator, and mobile operator is a type of
telecom
• Vodafone is in the UK , which is part of Europe => Vodafone is
a “telecom company in Europe”
• 5th of May is in Q2 and John G. Smith may be the same as
John Smith #8
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Semantic Annotation & Search
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Semantic Annotation
• Semantic annotation (of text)
• The process of linking text fragments to structured
information
• Organisations, Places, Products, Human Genes, Diseases,
Drugs, etc.
• Combines Text Mining (Information Extraction) with
Semantic Technologies
• Benefits of semantic annotations
• Improves the text analysis process
• by employing Ontologies and knowledge from external
Knowledge Bases / structured data sources
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Semantic Annotation (2)
• Benefits of semantic annotations (cont.)
• Provides unambiguous (global) references for
entities discovered in text
• Different from tagging
• Provide the means for semantic search
• Together or independently of the original text
• Improved data integration
• Documents from different data sources can share the same
semantic concepts
#11
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Example
#12
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Example (2)
• Demo of a GATE annotated document about
“Asthma and chronic obstructive pulmonary
disease”
• Annotations of Genes
• Each annotation is linked to an ontology class
• Each annotation is linked to an ontology instance
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Semantic Annotations
Document
Annotation1inDoc
Annotation2inDoc
Entity1about
start end
5 15
Class1type
Entity2about
Class2type
Annotation3
inDoc about
Annotation4
inDoc
Entity3about
type
#14
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Semantic Search
• Semantic Search
• In addition to the terms/keywords, explore the entity
descriptions found in text
• Make use of the semantic relations that exist between
these entities
• Example
• Query – “Documents about a telecom companies in
Europe related to John Smith from Q1 or Q2/2010”
• Document containing “At its meeting on the 10th of
May, the board of Vodafone appointed John G. Smith
as CTO” will not match
#15
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Semantic Search (2)
• Classical IR will fail to recognise that
• Vodafone is a mobile operator, and mobile operator is a
type of telecom
• Vodafone is in the UK , which is part of Europe
• => Vodafone is a “telecom company in Europe”
• 5th of May is in Q2
• John G. Smith may be the same as John Smith
#16
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Types of Semantic Search
• What semantics?
• Lexical semantics
• Named entities
• Factual knowledge
• Ontologies / taxonomies
• Hybrid approaches
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Types of Semantic Search (2)
• Types of queries
• Occurrence
• Co-occurrence
• Structured queries
• Faceted search
• Pattern-matching
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Types of Semantic Search (2)
• Structured queries
• Query entities in the Knowledge Base
• Very expressive and flexible
• Pattern queries
• A set of predefined structured queries where some search
criteria is already pre-specified
• Faceted search & navigation
• Extracted entities are organised into facets (intelligent
columns)
• Easy to find documents that contain information about
specific types of entities#19
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Ontologies for semantic search
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Structured query in KIM
Show me all people who were mentioned as spokesmen in IBM
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Structured query example
• Demo of a structured query with KIM
• Go to http://ln.ontotext.com
• Select STRUCTURE
• Build a query for:
• Persons (unspecified name)
• … who have a Position of type Job Position (unspecified name)
• … within an Organisation
• … which is a Company
• … which name starts with “IBM”
• Select
• Entities
• Documents mentioning the entities #22
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Pattern query example (2)
• Demo of a structured query with KIM
• Go to http://ln.ontotext.com
• Select PATTERNS
• Build a query for:
• Organisations (unspecified name) located in Montreal
• Select
• Entities
• Documents mentioning the entities
#23
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Faceted search in KIM
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Faceted search in KIM – document results
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Faceted search example
• Demo of a faceted navigation with KIM
• Go to http://ln.ontotext.com
• Select “Facets”
• Restrict “Organisations” to “McGill University”
• Restrict “Locations” to “Montreal”
• Select “researcher” from “Related Entities”
• (document results displayed on bottom of page)
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Overview of KIM and LifeSKIM
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The KIM Platform
• A platform offering services and infrastructure for:
• Automatic semantic annotation of text
• Text-mining and ontology population
• Semantic indexing and retrieval of content
• Query and navigation across heterogeneous text and
data
• Based on an Information Extraction technology
• built on top of GATE
• Offers unparalleled heterogeneous querying
facilities
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KIM platform (2)
Document &
Metadata
Aggregator
or Crawler
Population
Service
Semantic
Annotation
Semantic
Indexing &
Storing
Semantic
Index
Multi-paradigm
Search/Retrieval
Visual
Interface
3rd party
App
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LifeSKIM & Linked Data
#30
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LifeSKIM / Linked Data ETL
#31
Data Source
Identification
Flat files OBO files XML RDBMS RDF
Special tailored
transformer
OBO to SKOS
converterCustom XSLT
RDBMS to
RDF formatter
RDF warehouse
ReasonerInstance
Mappings
Semantic
Annotations
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Timelines for entity popularity in KIM
• Timelines for entity occurrences over some period
of time
• Can be used & extended for sentiment analysis
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Timelines in KIM
#33
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Timelines example
• Demo of timeline with KIM
• Go to http://ln.ontotext.com
• Select “Timelines”
• Build a monthly timeline comparing mentions of
Concordia, McGill and University of Montreal
• Time period: max
• Granularity: month
• Based on: occurences
#34