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Amarnath Gupta Univ. of California San Diego. An Abstract Question There is no concrete answer …but …

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Presentation on theme: "Amarnath Gupta Univ. of California San Diego. An Abstract Question There is no concrete answer …but …"— Presentation transcript:

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 Relational Query Processor Tree Query Processor Graph Query Processor OntoQuest Index Structures Type-Partitioned Data Store Ontology Repository User Query Parser Keyword Query Processor Query Planner 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!!!

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