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!!!