Invited Talk, International Workshop on Ontology Matching
collocated with the 5th International Semantic Web Conference
ISWC-2006, November 5, 2006, Athens GA
{Ontology: Resource} x {Matching : Mapping} x {Schema : Instance} :: Components of the same challenge?
1. {Ontology: Resource} x {Matching : Mapping} x {Schema : Instance} :: Components of the same challenge? Invited Talk, International Workshop on Ontology Matching collocated with the 5th International Semantic Web Conference ISWC-2006 , November 5, 2006, Athens GA Professor Amit Sheth Special Thanks: Meena Nagarajan Acknowledgment: SemDis project, funded by NSF
2. Information System needs and Ontology Matching goals SemDis, ISIS Semantic Web, some DL-II projects, Semagix SCORE, Applied Semantics VideoAnywhere InfoQuilt OBSERVER Generation III (information brokering) 1997... Semantics (Ontology, Context, Relationships, KB) InfoSleuth, KMed, DL-I projects Infoscopes, HERMES, SIMS, Garlic,TSIMMIS,Harvest, RUFUS,... Generation II (mediators) 1990s VisualHarness InfoHarness Metadata (Domain model) Mermaid DDTS Multibase, MRDSM, ADDS, IISS, Omnibase, ... Generation I (federated DB/ multidatabases) 1980s Data (Schema, “semantic data modeling)
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5. Need for querying across multiple ontologies OBSERVER Circa 1994, 1996-2002 IRM Interontologies Relationships ... Repositories Mappings/ Ontology Server Query Processor ... Repositories Mappings/ Ontology Server Query Processor ... ... Mappings/ Ontology Server Query Processor User Query Ontologies Ontologies Ontologies
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11. A step back DB vs. Ontology - Fundamental differences
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14. Modeling Database vs. Ontology schemas - Fundamental differences Emphasis while modeling is on the semantics of the domain – emphasis on relationships, also facts/knowledge/ground truth Emphasis while modeling is on structure of the tables Structure vs. Semantics Intended to model a domain Intended to model data being used by one or more applications Modeling perspective Ontology schemas Database schemas Axis of comparison
15. Choice of modeling affects the possible space of heterogeneities and therefore the process of matching. In both cases however, the schema is only an abstraction of the real world; the real power/semantics lies at the instance level. Symbolizes agreement of the modeling of a domain possibly used by applications in varying contexts. Limited to a syntactic agreement between applications using the data Agreement More expressive modeling paradigm Limited expressivity in capturing instance level metadata due to static schemas Instance metadata modeling / expressiveness Modeling of a domain irrespective of applications Well defined by applications using the data Context of modeling
26. (Complex) named relationships - example AFFECTS VOLCANO LOCATION ASH RAIN PYROCLASTIC FLOW ENVIRON. LOCATION PEOPLE WEATHER PLANT BUILDING DESTROYS COOLS TEMP DESTROYS KILLS
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28. Knowledge discovery and validation PubMed etc. Rele-vant docs Query and update DBs Prediction of - Pathways - Symptoms of Diseases - Other complex relationship
29. A Vision for Ontology Matching : Discovering simple to complex matches – from schema, instances and corpus SIMPLE TO COMPLEX MATCHES Possible identifiable matches: equivalence / inclusion / overlap / disjointness Possible to identify more complex relationships from the corpus. Ontologies Heterogeneous data Today , the Food and Drug Administration ( FDA ) is announcing that it has asked Pfizer , Inc . to voluntarily withdraw Bextra from the market . Pfizer has agreed to suspend sales and marketing of Bextra in the , pending further discussions with the agency . Semantic metadata
31. The Intuition 9284 documents 4733 documents Disease or Syndrome Biologically active substance causes affects causes complicates Fish Oils Raynaud’s Disease ??????? instance_of instance_of 5 documents UMLS MeSH PubMed Lipid affects
32. The Method – Identify entities and Relationships in Parse Tree Modifiers Modified entities Composite Entities
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36. Corpus based Hypothesis validation PubMed Does magnesium alleviate effects of migraine in patients? One possible hypothesized connection between magnesium and migraine…. isa Magnesium Migraine Stress Calcium Channel Blockers Patient affectedBy inhibit Complex Query Supporting Document sets retrieved
With time information systems and the use of semantic metadata and ontologies has evolved – from structured data exchange to integration, capturing semantic metadata, to using 1 ontology for mediating between sources to using multiple ontologies for information integration, to analysis and discovery in distributed multi-ontology, mutli-domain heterogeneous Web resoure environments.
And with this, the need for and goals of ontology matching have evolved
Christopher 11/3/2006 can maybe mention the static nature of databases that require large efforts to extend the schema vs. the extensible nature of ontologies due to the use of semi-structured data
Predictor can predict a pathway by a gene sequence. But we don’t know if the predicted pathway is actually possible. Need to verify in the literature, if the patway is not already in the ontology or actually not allowed according to the ontology Ontology – literature – dbs, prediction systems etc Predictor depends on application. For hypothesis verification, a human feeds available knowledge, for discovery it can be an HMM or other machine learning technique When the system is e.g. asked to predict or verify a pathway or some other complex relationship, the predicted result is then verified by the ontology management system. If the predicted pathway/complex relationship is not in the ontology, the literature and DBs are queried for concepts involved in the predicted pathway/complex relationship and correlated with known concepts in the ontology. Output are relevant publications,, DB entries and maybe a predicted likelihood of the patway/complex relationship being true, according to the found literature.
Migraine patients experience stress Ca inhibit stress Mag natural channel blocker Does magnesium alleviate effects of migraine in patients
The process of matching needs to support the generation of complex mappings