INCMAP: A JOURNEY TOWARDS ONTOLOGY-BASED DATA INTEGRATION

Transcription

1 INCMAP: A JOURNEY TOWARDS ONTOLOGY-BASED DATA INTEGRATION CHRISTOPH PINKEL (MAIN AUTHOR), CARSTEN BINNIG, ERNESTO JIMENEZ-RUIZ, EVGENY KARMALOV, ET AL.

2 EXPLORING DATABASES CAN BE TEDIOUS Author of paper with title IncMap? SQL 2 SQL 1 SQL 3 DBLP CMT EASYCHAIR Schema 2 Schema 1 Schema 3

3 PROBLEM 1: TOO MANY TABLES Author of paper with title IncMap? A typical SAP schema has more than tables Id Name Id Name Id Name Id Name Name Id Name Id Id Name Id Name Id Name Id Name

4 PROBLEM 2: LIMITED EXPRESSIVENESS Ontology Person domain sub-class Author Reviewer domain domain area name Relational Schema (Option 1) pid name area type 1 Lennon a@b - author 2 Harrison - Onto reviewer aid name 1 Lennon a@b Author Person Relational Schema (Option 2) rid name area 1 Harrison Onto Reviewer pid 1 a@b Author Relational Schema (Option 3) pid name 1 Lennon 2 Harrison Person pid area 2 Onto Reviewer Modeling generalization is messy

5 PROBLEM 3: TECHNICAL DESIGN BDC_IXN_FACT_MA BDC_ACCOUNT_DIM BDC_DEMOGRAPHICS_DIM BDC_IXN_FACT_WA Other issues: De-normalization (i.e., merge tables) No foreign keys! Performance optimizations (horizontal, vertical fragmentation, )

6 ONTOLOGY-BASED DATA ACCESS Author of paper with title IncMap? Minimal Ontology Ontology (in OWL QL) Author Person sub-class domain Reviewer name HIGH-LEVEL QUERY domain domain area ONTOLOGY-BASED DATA ACCESS SQL 2 SQL 1 SQL 3 DBLP CMT EASYCHAIR

7 ONTOLOGY-BASED DATA ACCESS Relational Schema Ontology Person sub-class domain name Mapping? Author domain Reviewer domain area IncMap: A Mapping Tool for Relational-To-Ontology Data Integration

8 THE JOURNEY OF INCMAP First version of IncMap Incremental mapping Leverage lexicographical and structural similarity Christoph Pinkel, et al.: Pay as you go Matching of Relational Schemata to OWL Ontologies with IncMap. International Semantic Web Conference 2013

9 THE JOURNEY OF INCMAP First version of IncMap Incremental mapping Leverage lexicographical and structural similarity Second version of IncMap Consider typical design patterns Leverage reasoning (open vs. closed-world) Bootstrap mappings (fully automatic) Christoph Pinkel, Carsten Binnig, Ernesto Jiménez-Ruiz, Evgeny Kharlamov, Andriy Nikolov, Andreas Schwarte, Christian Heupel, Tim Kraska: IncMap: A Journey towards Ontology-based Data Integration. BTW 2017

10 STEP 1: MAPPING TO INCGRAPHS Relational Schema R ID'...'?tle' PersID' (FK)'...' subclassof' Author' domain' Class' Object' Property' writes' Ontology O range' Data Property' hastitle' domain' varchar'?tle' string' hastitle' ID' PersID' PersID' subclassof' Author' writes' IncGraph(R) IncGraph(O) Main Reason: Mitigate structural differences

11 STEP 2: REASONING AND PATTERNS pid name area type 1 Lennon a@b - author 2 Harrison IncGraph(R) - Onto reviewer IncGraph(O) Person varchar'?tle' string' hastitle' ID' PersID' PersID' subclassof' Author' writes' Pattern: Inheritance Reasoning mul?e varchar' PersID'?tle' subclassof' Author' string' writes' hastitle' ID' PersID' IncGraph+(R) IncGraph+(O)

12 REASONING: TWO OPTIONS Option 1: Full reasoning 1. Reasoning on the base ontology using OWL QL 2. Add all derivable elements to IncGraph(O) Option 2: Custom reasoning (to close modeling gaps ) 1. Reasoning on the IncGraph(O) Generalization hierarchies Additional domain and range information 2. Add selected elements to IncGraph(O) set weights (see next slides)

13 STEP 3: PAIRWISE MATCHING mul?e varchar' PersID'?tle' subclassof' Author' string' writes' hastitle' ID' PersID' ' Target' Author' writes' Possible' Matches' Source' ' PersID' ' Pairwise Connectivity Graph 0.2$ 0.1$ 1.0$ Author' writes' PersID' 0.2$ 0.1$ 0.5$ Author' writes' PersID' 0.5$ 0.1$ 0.2$ writes' Author'

14 STEP 4: FIXPOINT COMPUTATION 0.9 Subclass Pairwise Connectivity Graph $ 0.1$ 1.0$ Author' writes' 1.0 PersID' $ 0.1$ 0.5$ Author' writes' PersID' 0.5$ 0.1$ 0.2$ writes' Author' PersID' Human Input (Acceptance and Rejection of Mappings) Weights for Patterns (Probability of Pattern) Deactivation of Edges (based on Patterns) subclassof' string' hastitle' Fixpoint Computation (Ext. Similarity Flooding) Author' writes'

15 Christoph Pinkel, Carsten Binnig, Ernesto Jiménez-Ruiz, Wolfgang May, Dominique Ritze, Martin G. Skjæveland, Alessandro Solimando, Evgeny Kharlamov: RODI: A Benchmark for Automatic Mapping Generation in Relational-to-Ontology Data Integration. ESWC 2015 EVALUATION: RODI BENCHMARK Conference ontology 1 Conference ontology 2 Geodata ontology Oil & gas ontology Target Ontologies (Schema) Mapping Rules? Mapping Rules? Mapping Rules? Mapping Rules? Source Databases (Schema+Data) CMT Canon. CMT CMT Variant Conf. Canon. Conf. Variant Conference Mond. Rel. Mond. Variant SIGKDD Single, large real-world schema Real-World Variants: 1. Adjusted Naming 2. Structural Adjustments (e.g., hierarchies) 3. Removed foreign keys 4. Merging / Splitting of tables 5. Combined cases

16 EVALUATION: RODI BENCHMARK Evaluation queries: Queries simulate information need Can be additional input for mapping 56 queries from simple to complex Metric: per-query F- measure

17 EVALUATION: COMPETITORS Relational-to-Ontology Mapping Systems Ontop: (Free University of Bozen- Bolzano) Bootox: (University of Oxford) General Mapping Systems (Baseline) COMA++: (University of Leipzig)

18 EVALUATION: RESULTS

19 EVALUATION: RESULTS

20 CONCLUSIONS Incremental Mapping Generation for Relational-to- Ontology Mappings Most benefits from domain knowledge (patterns, reasoning) Integrated into real-world platform at fluidops Possible future directions: Patterns, other graph similarity metrics,