1. Temporal Data Management: Semantic Web Engineering Discussion Leader: Cui Tao Assistant Professor in Medical Informatics Mayo Clinic Temporal Reasoning Journal Club December 1, 2011

2. Articles to Discuss Time-Oriented Question Answering from Clinical Narratives Using Semantic-Web Techniques. Tao C, Solbrig HR, Deepak S, Wei W-Q, Savova G, Chute CG. International Semantic Web Conference, Lecture Note of Computer Science. 2010; 6497:241-56. http://www.springerlink.com/content/67623p256743w v4u/ http://www.springerlink.com/content/67623p256743w v4u/ Representing Complex Temporal Phenomena for the Semantic Web and Natural Language. Feng Pan, 2007 PhD thesis University of Southern California http://www.isi.edu/~hobbs/time/pub/pan-phdthesis.pdf http://www.isi.edu/~hobbs/time/pub/pan-phdthesis.pdf

3. Why Semantic Web? The Semantic Web provides a suitable environment for temporal data representation and reasoning: Standard mechanism with explicit and formal semantic definition OWL DL SWRL Reasoning tools, querying and storage mechanisms

4. Ontologies Available Online Clinical Narrative Temporal Relation Ontology (CNTRO): http://cntro.org/index.html http://cntro.org/index.html Time-OWL: http://www.w3.org/TR/owl- time/http://www.w3.org/TR/owl- time/

5. Goals CNTRO For representing events, time information, and temporal relations in clinical narratives Can also represent structured data Presumably can be generalizable Time-OWL For describing the temporal content of Web pages and the temporal properties of Web services Can also represent structured data

6. Classes CNTRO ValidTime TimeInstant TimeInterval TimePeriod TimePhase Duration DurationUnit Granularity Event Time-OWL TemporalEntity Instant Interval ProperInterval DateTimeInterval DurationDescription DateTimeDescription TemporalUnit DayOfWeek

7. Properties (Time-OWL) Temporal Relations Duration Description DateTime Description

8. Properties (CNTRO) PropertyDomainRange AfterEventEvent/ValidTime BeforeEventEvent/ValidTime DuringEventEvent/ValidTime EqualEventEvent/ValidTime ContainEventEvent/ValidTime FinishEventEvent/ValidTime StartEventEvent/ValidTime hasDurationTimeIntervalDuration hasDurationUnitDurationDurationUnit hasDurationValueDurationdecimal hasStartTimeTimeIntervalTimeInstant hasEndTimeTimeIntervalTimeInstant hasValidTimeEventValidTime hasPeriodTimePhaseTimePeriod hasModalityValidTimeboolean hasNormalizedTimeTimeInstantdateTime hasOrigTimeTimeInstantstring

9. CNTRO Overview

10. Temporal Relations Both adapted Allen's interval algebra AfterEventEvent/ValidTime BeforeEventEvent/ValidTime DuringEventEvent/ValidTime EqualEventEvent/ValidTime ContainEventEvent/ValidTime FinishEventEvent/ValidTime StartEventEvent/ValidTime Time_OW L CNTRO

11. Duration Time_OW L CNTRO

12. Time Description Time_OW L CNTRO

13. Time-OWL Features Relations for intervals/instants Time Zones Day of Week, Day of Year Specific definition of months, weekdays, etc Temporal sequence

14. Time-OWL

16. Every other week on Monday, Wednesday and Friday until December 24, 1997, but starting on Tuesday, September 2, 1997. EveryOtherWeek: hasTemporalUnit = unitWeek hasGap = 2 MWFEveryOtherWeek: hasStart = 09/02/1997 hasEnd = 12/24/2007 hasithTemporalUnit =1,3,5 hasTemporalUnit=unitDay hasContextTemporalUnit=unitWeek

17. CNTRO Features Periodic Time Interval Relation between Two Events Time Offset Relative Time Uncertainty

18. CNTRO Representation (Period & Phase) Example Sentence: take antibiotics every 8 hours for 10 days starting from today (note date:2004-06- 01)

19. Operators for Offsets Was e1 before e4? inverse operatorsαand β β(3 days) α(2days) = β (1day)

20. SWRL RuleML Rule-based Definition for Consistency

21. SWRL RuleML Rule-based Definition for properties

22. SWRL RuleML Rule-based Definition for concepts “premature labor after 22 weeks but before 37 completed weeks of gestation without delivery”

23. CNTRO API findEvent(searchText) returns a list of events that match the searching criteria. Currently we look for events based on text search. GetEventFeature(event, featureflag) returns a specific time feature for a given event. Sample query: When was the patient diagnosed with diabetes? When was the patient started his chemotherapy?

24. CNTRO API getDurantionBetweenEvents(event1, event2) returns the time interval between two events. Sample query: How long after the patient was diagnosed colon cancer did he start the chemotherapy? getDuration(event) returns the duration of a given event. Sample query: How long did the symptoms of rectal bleeding last?

25. CNTRO API getTemporalRelationType(event1, event2) returns the temporal relations between two events if it can be retrieved or inferred. Sample query: Was the PT scan after the colonoscopy? getTemporalRelationType(event1, time) returns the temporal relations between an event and a specific time if it can be inferred or retrieved. Sample query: Is there any behavior change within a week of the test?

26. CNTRO API sortEventsByTemporalRelationsOrTimeline(event s) returns the order (timeline) of a set of events. sample query: What is the tumor status timeline as indicated in the patient’s radiology note? What is the treatment timeline as recorded in oncology notes? When was the first colonoscopy done? When was the most recent glucose test?

27. Discussion Semantic harmonization Time-related classes harmonization Relation harmonization

28. Discussion Instances vs. Intervals Time instant: a time interval with a very short duration Time interval: a time instant on a coarse level of granularity “patient’s last cycle of chemotherapy was on Jan. 19” Process or occurrence? Interval or instant?

29. Discussion Temporal Uncertainties Approximated temporal expression: In approximately 2 weeks About 3 hours In the AM Late last year Insufficient level of granularity: Duration between Jan. and Jun.? Event A: Jan. and Event B: Jan. 16; what was the temporal relation?

30. Discussion Temporal Relation on Granularity “PT WENT INTO CARDIAC ARREST AND THEY WERE UNABLE TO KEEP HEART BEATING FOR MORE THAN A COUPLE HOURS. PT PASSED AWAY THAT NIGHT.” Cardiac arrest before death (granularity: hour) Cardiac arrest equal death (granularity: day)

31. Discussion Vague Event Duration Missing durations is one of the most common sources of incomplete information for temporal reasoning in natural language applications Empirical approach? Long, short Coarse-grained duration information Set up ranges of durations

32. Discussion Temporal Uncertainties Coarse temporal notion: Early next week, middle of last year, short after 11PM, before breakfast Short after 11:30PM on the 16 th, before or on the 17 th ?

33. Discussion Temporal Uncertainties Ambiguities: Last cycle of chemotherapy was on Jan. 16. 1) patient’s last cycle of chemotherapy STARTED on Jan. 19; 2) patient’s last cycle of chemotherapy ENDED on Jan. 19; 3) patient’s last cycle of chemotherapy STARTED and ENDED on Jan. 19.

34. Questions