Your SlideShare is downloading. ×
0
Semantic Integration for Heterogeneous Domain-specific Information: The NIF Case
Semantic Integration for Heterogeneous Domain-specific Information: The NIF Case
Semantic Integration for Heterogeneous Domain-specific Information: The NIF Case
Semantic Integration for Heterogeneous Domain-specific Information: The NIF Case
Semantic Integration for Heterogeneous Domain-specific Information: The NIF Case
Semantic Integration for Heterogeneous Domain-specific Information: The NIF Case
Semantic Integration for Heterogeneous Domain-specific Information: The NIF Case
Semantic Integration for Heterogeneous Domain-specific Information: The NIF Case
Semantic Integration for Heterogeneous Domain-specific Information: The NIF Case
Semantic Integration for Heterogeneous Domain-specific Information: The NIF Case
Semantic Integration for Heterogeneous Domain-specific Information: The NIF Case
Semantic Integration for Heterogeneous Domain-specific Information: The NIF Case
Semantic Integration for Heterogeneous Domain-specific Information: The NIF Case
Semantic Integration for Heterogeneous Domain-specific Information: The NIF Case
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×
Saving this for later? Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime – even offline.
Text the download link to your phone
Standard text messaging rates apply

Semantic Integration for Heterogeneous Domain-specific Information: The NIF Case

974

Published on

Amarnath Gupta …

Amarnath Gupta
August 10, 2010
TCS Innovation Research Labs, New Delhi, India

Published in: Education
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
974
On Slideshare
0
From Embeds
0
Number of Embeds
4
Actions
Shares
0
Downloads
0
Comments
0
Likes
0
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide

Transcript

  • 1. Amarnath GuptaUniv. of California San Diego
  • 2. An Abstract QuestionThere is no concrete answer …but …
  • 3. publicationsHub sources
  • 4. What happened to“organizes the answers”and helping more informeddecisions? !!!
  • 5. Recognized entities“semantic equivalence”
  • 6. Indexing property chains for fast query expansionSchema mapping when possible
  • 7. Bicycle as in bi-cyclicBicycle as a therapeutic aidOntological ResourceAnnotation
  • 8. Data Ingestion andTransformationOntologyIngestion andTransformationRelationalQueryProcessorTreeQueryProcessorGraphQueryProcessorOntoQuestIndexStructuresType-PartitionedData StoreOntologyRepositoryUser Query ParserKeywordQueryProcessorQuery PlannerDataReaderDataReaderDataReaderExecution EngineOWLReaderOBOReaderRDFSReaderSemantic & Assn.Catalogs...•How to store, indexand query ontologiesefficiently?•What aboutdifferent forms ofontology?•What about multipleinter-mappedontologies?
  • 9. Q1. A single term ontological query synonyms(Hippocampus)Q2. transcription AND gene AND pathwayQ3. (gene) AND (pathway) AND (regulation OR "biological regulation") AND (transcription) AND(recombinant)Q4. synonyms(zebrafish AND descendants(promoter,subclassOf))Q5. synonyms(descendants(Hippocampus,partOf))Q6. synonyms(Hippocampus) AND equivalent(synonyms(memory))Q7. synonyms(x:descendants(neuron,subclassOf)where x.neurotransmitter=GABA) AND synonyms(gene where gene name=IGF)Q8. synonyms(x:descendants(neuron,subclassOf) wherex.soma.location=descendants(Hippocampus,partOf))
  • 10.  Given n data sources (n of the order of hundreds) Structured (relational) Semi-structured (XML, RDF) Un-structured (text) With specialized data semantics (pathway graphs, social nets, annotatedimages, …) A domain specified by an ontology with known entailment rules(preferably less expressive than full MSO logic) A set of mappings from the data to the ontology Construct An information system such that The ontology is the effective target schema Its query language has an enhanced keyword model (or anyassociative query language) User queries are transformed into “intentionally equivalent” sourcequeries Results are ranked by relevance The system is responsive, robust and scalable•Bootstrappingfrom a seedontology•Creating afeature-derivedontology
  • 11.  We can view the data problem as a “constrained”graph integration exercise where Every data/knowledge resource can be considered as a graph that isgoverned by a set of (Description Logic) axioms about its structureand component relationships Connections between individual resources can be defined both atthe level of the instance or at the level of the concepts The connections themselves can be defined in terms of asserted orinferred Description Logic statements The ontology’s role is to provide the bridges that can be considered“general knowledge” that is modularized under a well formedupper ontology.
  • 12.  What’s the best way to implement ontologies withconcrete domains through a graph-based approach? Graphs with Colored DAG backbones? Balancing Materialized vs. Computed edges for best time-spacetradeoffs What is an appropriate result model for an associativegraph query? What is the query language and result model of a story? Combining result presentation and navigation options? Ranking Models? Contextual Query Interpretation and Ranking? Oh! Scalability!!!

×