Temporal Data Management: Semantic Web Engineering
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Temporal Data Management: Semantic Web Engineering
Discussion Leader: Cui Tao
Assistant Professor in Medical InformaticsMayo Clinic
Temporal Reasoning Journal ClubDecember 1, 2011
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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/67623p256743wv4u/
• 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
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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
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Ontologies Available Online
• Clinical Narrative Temporal Relation Ontology (CNTRO): http://cntro.org/index.html
• Time-OWL: http://www.w3.org/TR/owl-time/
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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
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Classes
CNTRO• ValidTime
• TimeInstant• TimeInterval• TimePeriod• TimePhase
• Duration• DurationUnit• Granularity• Event
Time-OWL• TemporalEntity
• Instant• Interval
• ProperInterval• DateTimeInterval
• DurationDescription• DateTimeDescription• TemporalUnit• DayOfWeek
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Properties (Time-OWL)
Temporal Relations
Duration Description
DateTimeDescription
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Properties (CNTRO)Property Domain RangeAfter Event Event/ValidTime
Before Event Event/ValidTime
During Event Event/ValidTime
Equal Event Event/ValidTime
Contain Event Event/ValidTime
Finish Event Event/ValidTime
Start Event Event/ValidTime
hasDuration TimeInterval Duration
hasDurationUnit Duration DurationUnit
hasDurationValue Duration decimal
hasStartTime TimeInterval TimeInstant
hasEndTime TimeInterval TimeInstant
hasValidTime Event ValidTime
hasPeriod TimePhase TimePeriod
hasModality ValidTime boolean
hasNormalizedTime TimeInstant dateTime
hasOrigTime TimeInstant string
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CNTRO Overview
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Temporal RelationsBoth adapted Allen's interval algebra
After Event Event/ValidTime
Before Event Event/ValidTime
During Event Event/ValidTime
Equal Event Event/ValidTime
Contain Event Event/ValidTime
Finish Event Event/ValidTime
Start Event Event/ValidTime
Time_OWL
CNTRO
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Duration
Time_OWL
CNTRO
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Time Description
Time_OWL
CNTRO
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Time-OWLFeatures
• Relations for intervals/instants• Time Zones• Day of Week, Day of Year• Specific definition of months, weekdays,
etc• Temporal sequence
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Time-OWL
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Time-OWL
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Time-OWL
Every other week on Monday, Wednesday and Friday until December 24, 1997, but starting on Tuesday, September 2, 1997.
EveryOtherWeek:hasTemporalUnit = unitWeekhasGap = 2MWFEveryOtherWeek:hasStart = 09/02/1997hasEnd = 12/24/2007hasithTemporalUnit =1,3,5hasTemporalUnit=unitDayhasContextTemporalUnit=unitWeek
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CNTRO Features
• Periodic Time Interval• Relation between Two Events• Time Offset• Relative Time• Uncertainty
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CNTRORepresentation (Period & Phase)
• Example Sentence: take antibiotics every 8 hours for 10 days starting from today (note date:2004-06-01)
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Operators for Offsets
Was e1 before e4?inverse operatorsαand ββ(3 days) α(2days) = β (1day)
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SWRL RuleML
• Rule-based Definition for Consistency
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SWRL RuleML
• Rule-based Definition for properties
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SWRL RuleML
• Rule-based Definition for concepts
“premature labor after 22 weeks but before 37 completed weeks of gestation without delivery”
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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?
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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?
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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?
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CNTRO API
• sortEventsByTemporalRelationsOrTimeline(events) • 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?
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DiscussionSemantic harmonization
Time-related classes harmonization
Relation harmonization
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DiscussionInstances 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?
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DiscussionTemporal 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?
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DiscussionTemporal 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)
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DiscussionVague 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
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DiscussionTemporal Uncertainties
Coarse temporal notion:• Early next week, middle of last year,
short after 11PM, before breakfast• Short after 11:30PM on the 16th,
before or on the 17th?
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DiscussionTemporal 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.
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Questions