Ontology-Mediated Query Answering over Temporal Ontologies - PowerPoint PPT Presentation

Ontology-Mediated Query Answering over Temporal Ontologies Alessandro Artale Faculty of Computer Science, Free University of Bozen-Bolzano, Italy Joint work with R. Kontchakov, A. Kovtunova, V. Ryzhikov, F. Wolter, M. Zakharyaschev Hybrid Reasoning for Intelligent Systems – Freiburg, 12-13 November, 2018

Information needs at Siemens’ remote diagnostics centres Siemens remote-diagnostic centres analyse data by querying (aggregated) sensor measurements turbine Id dateTime activePower rotorSpeed mainFlame . . . . . . t03 2015-04-04 12:20:48 2 1550 0 t03 2015-04-04 12:20:49 1.8 1400 null t01 2015-04-04 12:20:52 1.7 1350 1 . . . Typical information needs: find the turbines and the times – when an active power trip occurred: the active power was above 1.5MW for a period of at least 10 seconds, maximum 3 seconds after which there was a period of at least 1 minute where the active power was below 0.15MW Hybrid Reasoning for Intelligent Systems, Freiburg, Germany 12-13/11/18 1

Information needs at Siemens’ remote diagnostics centres Siemens remote-diagnostic centres analyse data by querying (aggregated) sensor measurements turbine Id dateTime activePower rotorSpeed mainFlame . . . . . . t03 2015-04-04 12:20:48 2 1550 0 t03 2015-04-04 12:20:49 1.8 1400 null t01 2015-04-04 12:20:52 1.7 1350 1 . . . Typical information needs: find the turbines and the times – when an active power trip occurred: the active power was above 1.5MW for a period of at least 10 seconds, maximum 3 seconds after which there was a period of at least 1 minute where the active power was below 0.15MW – when a normal restart happened: normal stop followed by a normal start within 1 hour Hybrid Reasoning for Intelligent Systems, Freiburg, Germany 12-13/11/18 1

Information needs at Siemens’ remote diagnostics centres Siemens remote-diagnostic centres analyse data by querying (aggregated) sensor measurements turbine Id dateTime activePower rotorSpeed mainFlame . . . . . . t03 2015-04-04 12:20:48 2 1550 0 t03 2015-04-04 12:20:49 1.8 1400 null t01 2015-04-04 12:20:52 1.7 1350 1 . . . Typical information needs: find the turbines and the times – when an active power trip occurred: the active power was above 1.5MW for a period of at least 10 seconds, maximum 3 seconds after which there was a period of at least 1 minute where the active power was below 0.15MW – when a normal restart happened: normal stop followed by a normal start within 1 hour – normal stop: active power off followed by main flame off within 2 min, followed by coast down from 6600 to 1500rpm within 2 min, followed by coast down from 1500 to 200rpm within 9 min normal start: ... – Hybrid Reasoning for Intelligent Systems, Freiburg, Germany 12-13/11/18 1

Data access in industry (from Norwegian Petroleum Directorate’s FactPages) show me the wellbores completed before 2008 where Statoil as a drilling operator sampled less than 10 meters of cores Hybrid Reasoning for Intelligent Systems, Freiburg, Germany 12-13/11/18 2

Data access in industry (from Norwegian Petroleum Directorate’s FactPages) show me the wellbores completed before 2008 where Statoil as a drilling operator sampled less than 10 meters of cores 5 days later: SELECT DISTINCT cores.wlbName, cores.lenghtM, wellbore.wlbDrillingOperator, wellbore.wlbCompletionYear FROM ( (SELECT wlbName, wlbNpdidWellbore, (wlbTotalCoreLength * 0.3048) AS lenghtM FROM wellbore core WHERE wlbCoreIntervalUom = ’[ft ]’ ) UNION (SELECT wlbName, wlbNpdidWellbore, wlbTotalCoreLength AS lenghtM FROM wellbore core WHERE wlbCoreIntervalUom = ’[m ]’ ) ) as cores, ( (SELECT wlbNpdidWellbore, wlbDrillingOperator, wlbCompletionYear FROM wellbore development all UNION (SELECT wlbNpdidWellbore, wlbDrillingOperator, wlbCompletionYear FROM wellbore exploration all ) UNION (SELECT wlbNpdidWellbore, wlbDrillingOperator, wlbCompletionYear FROM wellbore shallow all ) ) as wellbore WHERE wellbore.wlbNpdidWellbore = cores.wlbNpdidWellbore ... Hybrid Reasoning for Intelligent Systems, Freiburg, Germany 12-13/11/18 2

Data access in industry (from Norwegian Petroleum Directorate’s FactPages) show me the wellbores completed before 2008 where Statoil as a drilling operator sampled less than 10 meters of cores 5 days later: SELECT DISTINCT cores.wlbName, cores.lenghtM, wellbore.wlbDrillingOperator, wellbore.wlbCompletionYear FROM ( (SELECT wlbName, wlbNpdidWellbore, (wlbTotalCoreLength * 0.3048) AS lenghtM FROM wellbore core WHERE wlbCoreIntervalUom = ’[ft ]’ ) UNION (SELECT wlbName, wlbNpdidWellbore, wlbTotalCoreLength AS lenghtM FROM wellbore core WHERE wlbCoreIntervalUom = ’[m ]’ ) In STATOIL: ) as cores, ( (SELECT wlbNpdidWellbore, wlbDrillingOperator, wlbCompletionYear FROM wellbore development all 1,000 TB of relational data UNION (SELECT wlbNpdidWellbore, wlbDrillingOperator, wlbCompletionYear FROM wellbore exploration all ) 2,000 tables UNION (SELECT wlbNpdidWellbore, wlbDrillingOperator, wlbCompletionYear FROM wellbore shallow all ) different schemas ) as wellbore WHERE wellbore.wlbNpdidWellbore = cores.wlbNpdidWellbore ... 30–70% of time on data gathering Hybrid Reasoning for Intelligent Systems, Freiburg, Germany 12-13/11/18 2

Ontology-based data access (OBDA) (the Romans ≈ 2007 ) SPARQL query SELECT DISTINCT ?unit ?well WHERE { [] npdv:stratumForWellbore ?wellboreURI ; npdv:inLithostratigraphicUnit [ npdv:name ?unit ] . ?wellboreURI npdv:name ?well . ?core a npdv:WellboreCore ; ProductionWellbore npdv:coreForWellbore ?wellboreURI . ∪ } Wellbore coreForWellbore WellboreCore [] rdf:type rr:TriplesMap; stratumForWellbore rr:logicalTable "select * from wellbore core"; rr:subjectMap [ a rr:TermMap; rr:template "&npd-v2;wellbore/ { wlbNpdidWellbore } /";]; ontology rr:propertyObjectMap [ rr:property npdv:coreIntervalBottom; WellboreStratum rr:column "wlbCoreIntervalBottom" ]; mappings ... A B C D 1 2 3 CREATE TABLE wellbore core ( 4 wlbName varchar(60) NOT NULL, 5 wlbCoreNumber int(11) NOT NULL, wlbCoreIntervalTop decimal(13,6), data sources ... ) Ontology – gives a high-level conceptual view of the data – provides a convenient & natural vocabulary for user queries – facilitates data integration via a global schema Hybrid Reasoning for Intelligent Systems, Freiburg, Germany 12-13/11/18 3

Ontology-based data access (OBDA) (the Romans ≈ 2007 ) SPARQL query SELECT DISTINCT ?unit ?well WHERE { [] npdv:stratumForWellbore ?wellboreURI ; npdv:inLithostratigraphicUnit [ npdv:name ?unit ] . ?wellboreURI npdv:name ?well . ?core a npdv:WellboreCore ; ProductionWellbore npdv:coreForWellbore ?wellboreURI . ∪ } Wellbore coreForWellbore WellboreCore [] rdf:type rr:TriplesMap; stratumForWellbore rr:logicalTable "select * from wellbore core"; rr:subjectMap [ a rr:TermMap; rr:template "&npd-v2;wellbore/ { wlbNpdidWellbore } /";]; ontology rr:propertyObjectMap [ rr:property npdv:coreIntervalBottom; WellboreStratum rr:column "wlbCoreIntervalBottom" ]; mappings ... A B C D 1 2 3 CREATE TABLE wellbore core ( 4 wlbName varchar(60) NOT NULL, 5 wlbCoreNumber int(11) NOT NULL, wlbCoreIntervalTop decimal(13,6), data sources ... ) Ontology – gives a high-level conceptual view of the data – provides a convenient & natural vocabulary for user queries – facilitates data integration via a global schema OWL 2 QL ontology-mediated queries ( O , q ( � x )) are FO-rewritable ∃ q ′ ∀A ,� = q ′ ( � O , A | a ) ⇐ ⇒ A | a = q ( � a ) reduction to DB query evaluation Hybrid Reasoning for Intelligent Systems, Freiburg, Germany 12-13/11/18 3

Temporal OBDA: Siemens case Siemens remote-diagnostic centres analyse data by querying sensor measurements turbine Id dateTime activePower rotorSpeed mainFlame . . . . . . t03 2015-04-04 12:20:48 2 1550 0 t03 2015-04-04 12:20:49 1.8 1400 null t01 2015-04-04 12:20:52 1.7 1350 1 . . . Typical query when an active power trip occurred: the active power was above 1.5MW for a period of at least 10 seconds, 3 seconds after which there was a period of at least one minute where active power was below 0.15MW We need a temporal ontology O with definitions such that we can formulate temporal queries as ActivePowerTrip ( x ) ← Turbine ( x ) ∧ ⊟ [0 , 1 m ] ActivePowerBelow0 . 15 ( x ) ∧ [60 s, 63 s ] ⊟ [0 , 10 s ] ActivePowerAbove1 . 5 ( x ) ( O , ActivePowerTrip ( x, t )) Ontology-mediated query: find turbines x and moments t when x had an active power trip Hybrid Reasoning for Intelligent Systems, Freiburg, Germany 12-13/11/18 4

Research problems 1 Which temporal logics and query languages are suitable for temporal OBDA as far as their expressive power is concerned? 2 Investigate logical and computational properties of the resulting fragments (Satisfiability, Logical Implication, etc.) 3 Classify their fragments by the type of rewritability (FO, datalog, etc.) and data Vs. combined complexity of OMQ answering 4 Algorithms, implementations, experiments, use cases 5 ... Hybrid Reasoning for Intelligent Systems, Freiburg, Germany 12-13/11/18 5