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

1. Amarnath Gupta
Univ. of California San Diego

2. An Abstract Question
There is no concrete answer …but …

5. publications
Hub sources

6. What happened to
“organizes the answers”
and helping more informed
decisions? !!!

7. Recognized entities
“semantic equivalence”

8. Indexing property chains for fast query expansion
Schema mapping when possible

9. Bicycle as in bi-cyclic
Bicycle as a therapeutic aid
Ontological Resource
Annotation

10. Data Ingestion and
Transformation
Ontology
Ingestion and
Transformation
RelationalQuery
Processor
TreeQuery
Processor
GraphQuery
Processor
OntoQuest
Index
Structures
Type-Partitioned
Data Store
Ontology
Repository
User Query Parser
Keyword
Query
Processor
Query Planner
Data
Reader
Data
Reader
Data
Reader
Execution Engine
OWL
Reader
OBO
Reader
RDFS
Reader
Semantic & Assn.
Catalogs
...
•How to store, index
and query ontologies
efficiently?
•What about
different forms of
ontology?
•What about multiple
inter-mapped
ontologies?

11. Q1. A single term ontological query synonyms(Hippocampus)
Q2. transcription AND gene AND pathway
Q3. (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) where
x.soma.location=descendants(Hippocampus,partOf))

12.  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, annotated
images, …)
 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 any
associative query language)
 User queries are transformed into “intentionally equivalent” source
queries
 Results are ranked by relevance
 The system is responsive, robust and scalable
•Bootstrapping
from a seed
ontology
•Creating a
feature-derived
ontology

13.  We can view the data problem as a “constrained”
graph integration exercise where
 Every data/knowledge resource can be considered as a graph that is
governed by a set of (Description Logic) axioms about its structure
and component relationships
 Connections between individual resources can be defined both at
the level of the instance or at the level of the concepts
 The connections themselves can be defined in terms of asserted or
inferred Description Logic statements
 The ontology’s role is to provide the bridges that can be considered
“general knowledge” that is modularized under a well formed
upper ontology.

14.  What’s the best way to implement ontologies with
concrete domains through a graph-based approach?
 Graphs with Colored DAG backbones?
 Balancing Materialized vs. Computed edges for best time-space
tradeoffs
 What is an appropriate result model for an associative
graph 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!!!