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{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
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)
Information systems - From mediators to information brokering ,[object Object],[object Object],Circa 1992-1996. IH Server Raw Data IH Clients Image Text Video Audio VisualHarness Architecture End User Web Browsers End User Web Browsers End User Web Browsers Internet Information Resources Metadata Database (Metabase) (Oracle) Repository 1 Repository m ..... IH  administrative  tools
Information systems - From mediators to information brokers ,[object Object],[object Object],Circa 1996-2000 INFORMATION CONSUMERS INFORMATION PROVIDERS Corporations Universities People Government Programs User  Query User Query  User Query Information System Data Repository Information System Newswires Universities Corporations Research Labs INFORMATION BROKERING Domain Specific Ontologies
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
Ontology Matching – goals ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Ontology Matching – changing notions ,[object Object],[object Object],[object Object],[object Object]
The process of Ontology Matching ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Top down and bottom up view to ontology matching ,[object Object],[object Object]
Top down and bottom up view to ontology matching ,[object Object]
A step back DB vs. Ontology - Fundamental differences
Schema integration goals – DB vs. Ontology ,[object Object],[object Object],[object Object],[object Object]
Goals are different because of differences in: ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
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
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
The space of heterogeneities in DB schema integration ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Sheth/Kashyap 1992, Kim/Seo 1993, Kashyap/Sheth 1996)
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],The space of heterogeneities in ontology schema integration
Key Observations ,[object Object],[object Object],[object Object]
Schema Integration – DB vs. Ontology Have we advanced the state of art ?
Schema Integration – techniques used ,[object Object],[object Object],[object Object],Schema matching techniques Information exploited DB Ontology ,[object Object],[object Object],Schema level
Schema Integration – techniques used ,[object Object],[object Object],Schema matching  techniques Information exploited ,[object Object],[object Object],DB Ontology Schema level
Schema Integration – techniques used ,[object Object],[object Object],[object Object],[object Object],Schema matching  techniques Information exploited DB Ontology Instance level ,[object Object]
Discovered semantic relationships ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Key Observation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
(Complex) named relationships and Ontology Matching
(Complex) named relationships - example AFFECTS VOLCANO LOCATION ASH RAIN PYROCLASTIC FLOW ENVIRON. LOCATION PEOPLE WEATHER PLANT BUILDING DESTROYS COOLS TEMP DESTROYS KILLS
Discovering such (complex) named relationships ,[object Object],[object Object],[object Object]
Knowledge discovery and validation PubMed etc. Rele-vant docs Query  and update DBs Prediction of  - Pathways - Symptoms of Diseases - Other complex relationship
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
Corpus based schema matching
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
The Method – Identify entities and Relationships in Parse Tree Modifiers Modified entities Composite Entities
Key Observation ,[object Object],[object Object],Current KR frameworks do not model this.  Capturing this might affect the way we think of matching and mapping.
Converting candidate relationships to ontology matches ,[object Object],[object Object],[object Object],[object Object]
Discovery vs. Validation of relationships – two sides of the coin ,[object Object],[object Object],[object Object]
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
From matching to mappings – several challenges ,[object Object],[object Object],[object Object],[object Object],Number of earthquakes with  magnitude > 7 almost constant.  So if at all, then nuclear tests only cause earthquakes with  magnitude < 7 E 1 : Reviewer E 6 : Person E 5 : Person E 2 : Paper E 4 : Paper E 7 : Submission E 3 : Person author _ of author _ of author _ of author _ of author _ of knows knows
The take home message
A world beyond simple matches and mappings ,[object Object],[object Object],[object Object],Need to go beyond  well-mannered schemas and  knowledge representations;  and relatively simpler mappings
For more information ,[object Object],[object Object]

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{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)
  • 3.
  • 4.
  • 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
  • 6.
  • 7.
  • 8.
  • 9.
  • 10.
  • 11. A step back DB vs. Ontology - Fundamental differences
  • 12.
  • 13.
  • 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
  • 16.
  • 17.
  • 18.
  • 19. Schema Integration – DB vs. Ontology Have we advanced the state of art ?
  • 20.
  • 21.
  • 22.
  • 23.
  • 24.
  • 25. (Complex) named relationships and Ontology Matching
  • 26. (Complex) named relationships - example AFFECTS VOLCANO LOCATION ASH RAIN PYROCLASTIC FLOW ENVIRON. LOCATION PEOPLE WEATHER PLANT BUILDING DESTROYS COOLS TEMP DESTROYS KILLS
  • 27.
  • 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
  • 33.
  • 34.
  • 35.
  • 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
  • 37.
  • 38. The take home message
  • 39.
  • 40.

Hinweis der Redaktion

  1. 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.
  2. And with this, the need for and goals of ontology matching have evolved
  3. 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
  4. 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.
  5. Migraine patients experience stress Ca inhibit stress Mag natural channel blocker Does magnesium alleviate effects of migraine in patients
  6. The process of matching needs to support the generation of complex mappings