{Ontology: Resource} x {Matching : Mapping} x {Schema : Instance} :: Components of the Same Challenge

Transcription

1 Wright State University CORE Scholar Kno.e.sis Publications The Ohio Center of Excellence in Knowledge- Enabled Computing (Kno.e.sis) {Ontology: Resource} x {Matching : Mapping} x {Schema : Instance} :: Components of the Same Challenge Amit P. Sheth Wright State University - Main Campus, amit.sheth@wright.edu Follow this and additional works at: Part of the Bioinformatics Commons, Communication Technology and New Media Commons, Databases and Information Systems Commons, OS and Networks Commons, and the Science and Technology Studies Commons Repository Citation Sheth, A. P. (2006). {Ontology: Resource} x {Matching : Mapping} x {Schema : Instance} :: Components of the Same Challenge.. This Presentation is brought to you for free and open access by the The Ohio Center of Excellence in Knowledge-Enabled Computing (Kno.e.sis) at CORE Scholar. It has been accepted for inclusion in Kno.e.sis Publications by an authorized administrator of CORE Scholar. For more information, please contact corescholar@

2 {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

3 Information System needs and Ontology Matching goals Generation III (information brokering) Generation II (mediators) 1990s Generation I (federated DB/ multidatabases) 1980s SemDis, ISIS Semantics (Ontology, Context, Relationships, KB) VideoAnywhere Semantic Web, some DL-II projects, InfoQuilt Semagix SCORE, Applied Semantics OBSERVER VisualHarness InfoHarness Mermaid DDTS Metadata (Domain model) InfoSleuth, KMed, DL-I projects Infoscopes, HERMES, SIMS, Garlic,TSIMMIS,Harvest, RUFUS,... Data (Schema, semantic data modeling) Multibase, MRDSM, ADDS, IISS, Omnibase,...

4 Information systems - From mediators to information brokering Mediators between heterogeneous information sources InfoHarness, VisualHarness, InfoSleuth, SIMS, Garlic etc. Circa End User Web Browsers End User Web Browsers End User Web Browsers IH Clients Internet Repository... 1 IH Server Repository m Metadata Database (Metabase) (Oracle) Image VisualHarness Architecture IH administrative tools Raw Data Audio Text Video Information Resources

5 Information systems - From mediators to information brokers Information brokers InfoQuilt, OBSERVER etc. INFORMATION CONSUMERS Corporations Universities User Query People Programs Government User Query User Query INFORMATION BROKERING Domain Specific Ontologies Information System Data Repository Information System Circa Newswires Corporations Universities Research Labs INFORMATION PROVIDERS

6 Need for querying across multiple ontologies OBSERVER IRM Interontologies Relationships... Ontologies Mappings/ Ontology Server Query Processor User Query Query Mappings/ Processor Ontology Server Mappings/ Ontology Server Query Processor... Repositories Circa 1994, Ontologies Repositories Ontologies

7 Ontology Matching goals Goals of ontology matching (and mapping, or integration) Shallow analysis to identify dependencies for integration Deeper analysis to create mappings for query based transformations / integration Integrate schemas to create a global schema Integrate instance bases Sheth, Review of a real world experience in database schema integration (Bellcore, ca. 1993)

8 Ontology Matching changing notions Given the distributed nature of modeling domains and metadata, the need for matching advanced to Information Integration Now Query processing not limited to multiple databases or ontologies, but multiple domains and sources of information Exploiting structured, semi-structured and unstructured data sources, multi-model Web sources

9 The process of Ontology Matching Different for purposes of merging / aligning ontologies Type of relationships that suffice to be discovered are limited to equivalence / inclusion / disjointness / overlap mappings Different for purposes of information integration to analytics to discovery Need for discovering more Complex mappings Named relationships / associations Graph based / numerical mappings

10 Top down and bottom up view to ontology matching Top Down: schema + instance integration to provide information integration

11 Top down and bottom up view to ontology matching Bottom up: exploit external data sources to drive schema matching

12 A step back DB vs. Ontology - Fundamental differences

13 Schema integration goals DB vs. Ontology DB schema integration goal Defining an integrated view of the data for all applications using the data. Ontology schema integration goal Defining an agreement between multiple ontology schemas modeled for the same domain.

14 Goals are different because of differences in: The modeling paradigms A database schema is a model for the data that one more applications intend to use. An ontology is a model of knowledge for a bounded region of interest (also known as a domain) Data vs. Knowledge : A DB instance base is not the same as an ontology instance base A database models data to be used by one or more applications An ontology models knowledge about a domain, independent of the application

15 Modeling Database vs. Ontology schemas - Fundamental differences Axis of comparison Modeling perspective Structure vs. Semantics Database schemas Intended to model data being used by one or more applications Emphasis while modeling is on structure of the tables Ontology schemas Intended to model a domain Emphasis while modeling is on the semantics of the domain emphasis on relationships, also facts/knowledge/ground truth

16 Agreement Instance metadata modeling / expressiveness Limited to a syntactic agreement between applications using the data Limited expressivity in capturing instance level metadata due to static schemas Symbolizes agreement of the modeling of a domain possibly used by applications in varying contexts. In both Choice cases of modeling however, affects the schema the possible is only an More expressive abstraction space of heterogeneities of the real world; modeling and paradigm the real power/semantics lies at the therefore the process of matching. instance level. Context of modeling Well defined by applications using the data Modeling of a domain irrespective of applications

17 The space of heterogeneities in DB schema integration Conflicts/Heterogeneities in DB schema integration Model / representation : relational vs. network vs. hierarchical models Structural / schematic : Domain Incompatibilities Entity Definition Incompatibilities Data Value Incompatibilities Abstraction level Incompatibilities Largely syntactic and structural; relatively few semantic conflicts Sheth/Kashyap 1992, Kim/Seo 1993, Kashyap/Sheth 1996)

18 The space of heterogeneities in ontology schema integration Conflicts/Heterogeneities in ontology schema integration Significant conflicts in perception of a domain semantic conflicts Other heterogeneities are similar to those in the DB world Model / representation : OWL/RDF ; topic maps etc. Structural : modeling as an entity vs. an attribute/property; generalization vs. abstraction etc. Largely semantic conflicts; comparable syntactic conflicts

19 Key Observations There are significant philosophical differences in how a DB schema and an Ontology schema are modeled In spite of these distinctions, many schema matching techniques overlap significantly. Have we advanced the state of art in ontology schema matching?

20 Schema Integration DB vs. Ontology Have we advanced the state of art?

21 Schema Integration techniques used Schema matching techniques Schema level DB Information exploited Ontology Syntactic Linguistic: Matching names, descriptions, namespaces etc. Constraint-based: Constraint matches on data types, value ranges, uniqueness, cardinalities etc. Matching Matching Table and class, column properties/ level relationship, names and attribute level constraints names and constraints

22 Schema Integration techniques used Schema matching techniques Schema level DB Information exploited Ontology Structural Constraint-based: Tree / Graph structure matching Matching structures of relational tables Matching class hierarchies and structures

23 Schema Integration techniques used Schema matching techniques Instance level DB Information exploited Ontology Linguistic IR techniques, word frequencies, key terms, combination of key terms etc. Constraint based Numerical value patterns, ranges useful for recognizing phone numbers etc. Hybrid approaches use a combination of all techniques

24 Discovered semantic relationships State of the art in DBs and Ontologies Relationships with set semantics: overlap / disjointness / exclusion / equivalence / subsumption Their logical encodings are what they mean Of more interest is discovering arbitrary named relationships Relationships such as works_for or causes have real-world semantics. Their encoding in first order logic lacks semantic grounding. Matching and mapping closely tied. Ability to capture complex mapping (e.g., semantic proximity) puts significantly different demand on matching

25 Key Observation DB and Ontology schema matching techniques overlap significantly Not much advancement since DB schema integration efforts Ontologies formalize the semantics of a domain, but matching is still primarily syntactic / structural. The semantics of named relationships is largely unexploited The real semantics lies in the relationships connecting entities Modeled as first class objects in Ontologies In DB, they are not explicit and have to be inferred

26 (Complex) named relationships and Ontology Matching

27 (Complex) named relationships - example VOLCANO ENVIRON. LOCATION PYROCLASTIC FLOW ASH RAIN BUILDING DESTROYS WEATHER COOLS TEMP PLANT PEOPLE LOCATION DESTROYS KILLS

28 Discovering such (complex) named relationships Matching techniques have exhausted Schema + Instance properties Ontology modeling de couples schema + instance base Tremendous opportunity to exploit knowledge present outside the ontology knowledge base (External structured, semi-structured and unstructured data sources)

29 Knowledge discovery and validation Query and update Relevant docs Prediction of - Pathways - Symptoms of Diseases - Other complex relationship PubMe d etc. DBs

30 A Vision for Ontology Matching : Discovering simple to complex matches from schema, instances and corpus SIMPLE TO COMPLEX MATCHES Ontologies Possible identifiable matches: equivalence / inclusion / overlap / disjointness Semantic metadata Possible to identify more complex relationships from the corpus. 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. Heterogeneous data

31 Corpus based schema matching

32 The Intuition Biologically active substance Lipid instance_of Fish Oils affects causes causes affects??????? UMLS complicates Disease or Syndrome instance_of Raynaud s Disease MeSH 9284 documents 5 documents 4733 documents PubMed

33 The Method Identify entities and Relationships in Parse Tree Modifiers Modified entities Composite Entities

34 Key Observation What is interesting is not the entity estrogen or endometrium Current KR frameworks do not model this. Capturing this might affect the way we The real think knowledge of matching lies and in mapping. the complex and modified entities an excessive endogeneous stimulation by estrogen

35 Converting candidate relationships to ontology matches Linguistic and statistical challenges: Variations of entities, relationships and associations Translating instance level findings to the schema level GOING FROM several discovered relationships like Deficiency in migraine causes Migraine TO substance X causes condition Y

36 Discovery vs. Validation of relationships two sides of the coin Discovering complex relationships from text is a hard problem Natural Language challenges (not all sentences are well formed) Validating complex relationships / hypothesis is relatively simpler

37 Corpus based Hypothesis validation Migraine Patient affectedby Does magnesium alleviate effects of migraine in patients? Magnesium One possible Stress hypothesized inhibit connection isa between magnesium and migraine. Calcium Channel Blockers Complex Query PubMed Supporting Document sets retrieved

38 From matching to mappings several challenges Mappings are not always simple mathematical / string transformations Examples of complex mappings Associations / paths between classes Graph based / form fitting functions author _ of E 2 : Paper author _ of E 6 : Person author _ of E 1 : Reviewer author _ of E 7 : Submission E 4 : Paper knows author _ of E 3 : Person knows E 5 : Person Number of earthquakes with magnitude > 7 almost constant. So if at all, then nuclear tests only cause earthquakes with magnitude < 7

39 The take home message

40 A world beyond simple matches and mappings The distinction between schema and instances is slowly disappearing Need to go beyond Integrating well-mannered new and external schemas data and sources, mining and analyzing them is gaining importance. knowledge representations; and relatively simpler mappings Tremendous opportunities and challenges in using more information than what is modeled in a schema and captured in an instance base.

41 For more information LSDIS Lab: Kno.e.sis Center: