Query Translation for Data Sources with Heterogeneous Content Semantics
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The document discusses query translation for data sources with heterogeneous content semantics. It proposes using ontology-extended data sources to make explicit the implicit ontologies associated with data. The key aspects covered include translating queries between different data content ontologies using conversion functions and interoperation constraints to ensure sound, complete, or exact translations.
![Query Translation for Data Sources with Heterogeneous Content Semantics Jie Bao Department of Computer Science Iowa State University [email_address] May 5, 2006](https://image.slidesharecdn.com/2006-05-03query1-100706172534-phpapp01/85/Query-Translation-for-Data-Sources-with-Heterogeneous-Content-Semantics-1-320.jpg)
![Data Content Ontologies Jane’s ontology Classes:Duration : Minutes Data Users’ Ontologies Bob’s ontology Classes:Duration : Hours Data Provider’s Ontology [ AVH (Attribute Value Hierarchy) ] Classes:Duration : Minutes [ Unit Scale ]](https://image.slidesharecdn.com/2006-05-03query1-100706172534-phpapp01/85/Query-Translation-for-Data-Sources-with-Heterogeneous-Content-Semantics-6-320.jpg)
![Related work Extensive work on semantic data integration, see survey papers [Hull 1997; Wache, et al. 2001; Levy, 2000] Query translation with schema ontologies OBSERVER: [Mena et al . , 2000] SIRUP: [Ziegler and Dittrich, 2004] Query translation with data content ontologies BUSTER: [Wache and Stuckenschmidt, 2001] COIN: [Goh et al., 1999] Both only address term substitution, i.e. translation with conversion functions. HOME & Ontology-extended relational algebra : [Bonatti et al . , 2003] It allows data types to be hierarchies, but only with “below”(<=) operations on hierarchies.](https://image.slidesharecdn.com/2006-05-03query1-100706172534-phpapp01/85/Query-Translation-for-Data-Sources-with-Heterogeneous-Content-Semantics-26-320.jpg)
Query Translation for Data Sources with Heterogeneous Content Semantics
- 1. Query Translation for Data Sources with Heterogeneous Content Semantics Jie Bao Department of Computer Science Iowa State University [email_address] May 5, 2006
- 2. Outline Ontology-Extended Data Sources (OEDS) Query Translation for OEDS with Heterogeneous Data Content Semantics The INDUS Implementation Summary
- 3. Data Semantics Even you have the data, do you really understand it? From Health database for Lorises Environmental Stress Tiredness Unwellness Normal Hear Something Fear Social Stress Social Play
- 4. Bridging the Semantic Gap Explanations of data are always context-specific, therefore semantic gaps are common. Ontologies can make explicit the usually implicit assumptions about the “meaning” of data. Between data sources of the same domain Between the data provider and a data user Between different data users of the same data source
- 5. Example: Academic Department Student RegisterFor Classes OfferedBy Instructors Schema Data Set Ontological Commitment Students and Instrutors are People Classes:Duration 's values are time in minutes Student status “2ndYear” implies “Undergrad” Data Schema Ontology Data Content Ontologies We will focus on data content ontologies in this work
- 6. Data Content Ontologies Jane’s ontology Classes:Duration : Minutes Data Users’ Ontologies Bob’s ontology Classes:Duration : Hours Data Provider’s Ontology [ AVH (Attribute Value Hierarchy) ] Classes:Duration : Minutes [ Unit Scale ]
- 7. Ontology-Extended Data Sources Ontology-extended data sources (OEDS) make explicit, the otherwise implicit ontologies associated with the data sources. ontologies can be specified by data providers or data users representing their local points of view. D O S S Schema Data Set Data Schema Ontology O D Data Content Ontology Data Sources (Relational, RDF…) Ontologies
- 8. Data Content Ontology as Data Type Common data types: String , Integer , Float … Unit Scales e.g. MinuteDuration , HourDuration Hierarchies as Partial-Order Ontologies (PO) Partial-ordering ( ): are transitive, self-reflexive and anti-symmetric relations. PO operators: =(equal to), <(below), >(above), (above or equal to), (below or equal to), ≠(not equal to) e.g. StudentStatus Undergrad StudentStatus Undergrad 1 st_Year 2 nd_Y ear Undergrad … They can be easily implemented as extensions to many RDBMSs: Oracle, PostgreSQL…
- 9. Outline Ontology-Extended Data Sources (OEDS) Query Translation for OEDS with Heterogeneous Data Content Semantics The INDUS Implementation Summary
- 10. Ontology-Extended Query Bob’s query : How many regular classes (classes longer than half an hour) duration (in hours) are taken by students with status `Masters'? However, this query cannot be directly understood by the data source due to semantic gaps Data Provider’s ontology has not equivalent concept for “Masters” Class duration as recorded in the data source is in minutes
- 11. Query Translation Query translation is a process to transform a query using one ontology to a query using another ontology usually from a user ontology to the data provider’s ontology The tuples that match a given query q: {q(t)} A translation q-> q’ is Sound, if {q’(t)} {q(t)} (all retrieved results are needed) Complete, if {q(t)} {q’(t)} (all needed results are retrieved) Exact, if {q(t)} = {q’(t)} (sound and complete)
- 12. Translation with Conversion Function A conversion function f:O 1 ->O 2 establishes one-to-one correspondences between terms in the two ontologies O 1 :t and O 2 :f(O 1 :t) are semantically equivalent Example: State2Code: {Iowa->IA, Delaware->DE,…} H2M: y=x*60 (HourDuration to MinuteDuration) With conversion functions, exact translation can be made by term substitution Duration HourDuration:0.5 -> Duration MinuteDuration:30
- 13. Translation with Interoperation Constraints (1) In many cases, one-to-one term correspondence is not existent Float:3.5 has no correspondence in Integer GradStatus:Masters has no correspondence in StudentStatus Therefore, exact translation is not always possible. However, we may still build sound or complete translation with the help of Interoperation Constraints (IC) ?
- 14. Translation with Interoperation Constraints(2) IC between Float and Integer Float:x <= Integer: x (ceiling) Float:x >= Integer: x (floor) Translation rules Sound translation: A < Float:x -> A < Integer: x , A > Float:x -> A > Integer: x Complete translation: A < Float:x -> A < Integer: x , A > Float:x -> A > Integer : x Example Sound translation: A< Float:3.5 -> A < Integer:3 A> Float:3.5 -> A > Integer:4 Complete translation: A< Float:3.5 -> A < Integer:4 A> Float:3.5 -> A > Integer:3 The translation is dependent on both the terms and the operators in question
- 15. Translation with Interoperation Constraints(3) IC between Partial-order Ontologies INTO (<=): GradStatus: " Masters" <= StudentStatus: "Grad" ONTO (>=): GradStatus: "Masters" >= StudentStatus: "Master of Science" EQUIV (=): GradStatus: "Ph.D" = StudentStatus: " Doctor of Philosophy" = <= >=
- 16. Translation Rules for PO Example Sound translation : Status GradStatus: "Masters" -> Status StudentStatus:"Master of Science“ (IC : GradStatus: "Masters" >= StudentStatus:"Master of Science“) Complete translation : Status GradStatus: "Masters" -> Status StudentStatus:“Grad“ (IC : GradStatus: "Masters" <= StudentStatus:"Master of Grad“)
- 17. A Query Translation Algorithm
- 18. Outline Ontology-Extended Data Sources (OEDS) Query Translation for OEDS with Heterogeneous Data Content Semantics The INDUS Implementation Summary
- 19. Ontology-based information integration in INDUS
- 20. Query processing in INDUS Query Formulation Handling both schema heterogeneity and data content heterogeneity
- 25. Outline Ontology-Extended Data Sources (OEDS) Query Translation for OEDS with Heterogeneous Data Content Semantics The INDUS Implementation Summary
- 26. Related work Extensive work on semantic data integration, see survey papers [Hull 1997; Wache, et al. 2001; Levy, 2000] Query translation with schema ontologies OBSERVER: [Mena et al . , 2000] SIRUP: [Ziegler and Dittrich, 2004] Query translation with data content ontologies BUSTER: [Wache and Stuckenschmidt, 2001] COIN: [Goh et al., 1999] Both only address term substitution, i.e. translation with conversion functions. HOME & Ontology-extended relational algebra : [Bonatti et al . , 2003] It allows data types to be hierarchies, but only with “below”(<=) operations on hierarchies.
- 27. Conclusions In this study, we: Argued for the need for making explicit the ontological commitments behind data content semantics , in addition to data schema semantics Formulated the problem of translating queries w.r.t. context-specific data content ontologies. Described an algorithm for semantic-preserving translation of an ontology-extended query. Future Work: Improve the scaleability of the translation process Improve the expressiveness of supported ontologies
- 28. Thank you! Questions ?
Editor's Notes
- #4: http://www.loris-conservation.org/database/disease/2-1_facial_expressions.html Judgement of wellbeing: meaning of facial expression in Loris a, b : Normal expression c, d : Fur gaps in the corners of the mouth, sometimes extending up to the ears ( d ), often become visible when something unfamiliar is noticed; they probably indicate a certain amount of environmental stress. e, f : The ears can be moved as a reaction to acoustic or other stimuli ( e ), and ear movements can cause a slight change of ear shape, but it seems doubtful whether ear movements may serve as a signal in communication. Ears laid back can regularly be seen in the initial phase of the prey-catching movement, probably a protective behaviour ( f ). Ears laid back may also be seen in animals caught and handled ( g ). In lorises suffering from severe social stress, the ears may be drawn down to the sides of the head, making the face look broader and narrower than usual; expression of social stress may include tense lips (see also k ), narrow eyes if the animal is already suffering from unwellbeing caused by dangerous distress, a crouched posture (see figures showing signs of social stress), stay in the low parts of the cage, quiet staring upwards towards the aggressive conspecific or running with apparent flight intention. During severe environmental distress, often wide-open, protruding eyes are shown ( g ); the animals, however, may also show a rather normal-looking face in spite of apparent distress. i : The lip cleft usually is s-shaped. In stress situations however, particularly during social stress, lips may look tense, pressed together and rather straight ( k ). l : Open-mouth play face. Such facial expression with the mouth opened more or less widely, sometimes in connection with playful biting, occurs during vivid solitary or social play. Open mouth faces also occur in other contexts, usually in connection with vocalization (see Loris behaviour paper, Schulze and Meier 1995). m, n : narrow eyes may indicate tiredness (in animals disturbed during day); when shown during night, they may indicate weakness or unwellbeing (examples: animals before death due to old age and kidney disease)
- #20: INDUS – a federated, query centric approach to the problem of knowledge acquisition from distributed, semantically heterogeneous, autonomous data sources Learning algorithms that can be decomposed into information gathering (obtained by answering queries) and hypothesis generation can be easily linked to INDUS INDUS makes possible the exchange of data and findings between scientists or institutions working on related problems (e.g., bioinformatics)