Principles of Referent Tracking BMI714 Course – Spring 2019

Slides:



Advertisements
Similar presentations
ECO R European Centre for Ontological Research Realist Ontology for Electronic Health Records Dr. Werner Ceusters ECOR: European Centre for Ontological.
Advertisements

ECO R European Centre for Ontological Research Strategies for Referent Tracking in Electronic Health Records Dr. W. Ceusters European Centre for Ontological.
Catalina Martínez-Costa, Stefan Schulz: Ontology-based reinterpretation of the SNOMED CT context model Ontology-based reinterpretation of the SNOMED CT.
Division of Biomedical Informatics Beyond Interoperability: What Ontology Can Do for the EHR William R. Hogan, MD, MS July 30 th, 2011 International Conference.
New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U.
Strategies for Referent Tracking in Electronic Health Records. Arguments to Werner Ceusters presentation Imia WG6 Workshop on Ontology and Biomedical Informatics.
Referent Tracking: Towards Semantic Interoperability and Knowledge Sharing Barry Smith Ontology Research Group Center of Excellence in Bioinformatics and.
Werner Ceusters Language & Computing nv Ontologies for the medical domain: current deficiencies in light of the needs of medical natural language.
1 The Ontologically Privileged Status of the Past Barry Smith.
New York State Center of Excellence in Bioinformatics & Life Sciences R T U Referent Tracking: The New Paradigm Dagstuhl May 23th, 2006 Werner Ceusters,
1/24 An ontology-based methodology for the migration of biomedical terminologies to the EHR Barry Smith and Werner Ceusters.
Copyright © 2011 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Chapter 3 The Relational Data Model and Relational Database Constraints.
Bringing the technology of FedEx parcel tracking to the Electronic Health Record (EHR) 1/2 0.
THE RELATIONAL DATA MODEL CHAPTER 3 (6/E) CHAPTER 5 (5/E) 1.
New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U.
This material was developed by Duke University, funded by the Department of Health and Human Services, Office of the National Coordinator for Health Information.
New York State Center of Excellence in Bioinformatics & Life Sciences R T U Referent Tracking Unit R T U Guest Lecture for Ontological Engineering PHI.
LOGIC AND ONTOLOGY Both logic and ontology are important areas of philosophy covering large, diverse, and active research projects. These two areas overlap.
CSE314 Database Systems Lecture 3 The Relational Data Model and Relational Database Constraints Doç. Dr. Mehmet Göktürk src: Elmasri & Navanthe 6E Pearson.
Chapter – 8 Software Tools.
1 Ontological investigations into medical diagnoses Grand Round of the Department of Biomedical Informatics August 26, 2015 – Buffalo, NY, USA Werner CEUSTERS,
New York State Center of Excellence in Bioinformatics & Life Sciences R T U Institute for Healthcare Informatics Ontology and Imaging Informatics Third.
1 Biomarkers in the Ontology for General Medical Science Medical Informatics Europe (MIE) 2015 May 28, 2015 – Madrid, Spain Werner CEUSTERS 2, MD and Barry.
1 An ontological analysis of diagnostic assertions in electronic healthcare records International Conference on Biomedical Ontology July 27-30, 2015 –
New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U.
New York State Center of Excellence in Bioinformatics & Life Sciences R T U MIE Tutorial Biomedical Ontologies: The State of the Art (Part 2) Introduction.
Ontological Foundations for Tracking Data Quality through the Internet of Things. EFMI STC2016: Transforming Healthcare with the Internet of Things Paris,
1 The Relational Data Model David J. Stucki. Relational Model Concepts 2 Fundamental concept: the relation  The Relational Model represents an entire.
New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U.
New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U.
New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U.
New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U.
Artificial Intelligence Logical Agents Chapter 7.
New York State Center of Excellence in Bioinformatics & Life Sciences R T U Discovery Seminar /UE 141 MMM – Spring 2008 Solving Crimes using Referent.
New York State Center of Excellence in Bioinformatics & Life Sciences R T U Referent Tracking: Research Topics and Applications Center for Cognitive Science,
New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U.
New York State Center of Excellence in Bioinformatics & Life Sciences R T U 1 MIE 2006 Workshop Semantic Challenge for Interoperable EHR Architectures.
New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U.
Hele-Mai Haav: CSC210-Spring*01 CSC230-Spring*03 Database Design.
W. Ceusters1, M. Capolupo2, B. Smith1, G. De Moor3
Department of Psychiatry, University at Buffalo, NY, USA
SNOMED CT’s RF2: Werner CEUSTERS1 , MD
“Ontology Measurement and evaluation" mini-series Realism-based Change Management for Quality Assurance in Ontologies and Data Repositories NIST-Ontolog-NCOR,
Center of Excellence in Bioinformatics and Life Sciences
Biomedical Ontology PHI 548 / BMI 508
European Centre for Ontological Research
Achieving Semantic Interoperability of Cancer Registries
Towards the Information Artifact Ontology 2
Hypothesis testing Chapter S12 Learning Objectives
Ontologies of Dynamical Systems and Verifiable Ontology-based Computation: Towards a Haskell-based Implementation of Referent Tracking 9th International.
Werner CEUSTERS a, Peter ELKIN b and Barry SMITH a, c
Research Process №5.
Structured Electronic Health Records and Patient Data Analysis: Pitfalls and Possibilities. January 7, 2013 Farber Hal G-26, University at Buffalo, South.
Werner Ceusters & Shahid Manzoor
Advanced Topics in Biomedical Ontology PHI 637 SEM / BMI 708 SEM
ICBO Tutorial Introduction to Referent Tracking July 22, Norton Hall, UB North Campus Werner CEUSTERS Center of Excellence in Bioinformatics.
ONTOLOGY FOR THE INTELLIGENCE COMMUNITY: Towards Effective Exploitation and Integration of Intelligence Resources Tracking Referents Columbia, MD.
Code Generation.
Code Generation Part I Chapter 8 (1st ed. Ch.9)
MIS2502: Data Analytics Relational Data Modeling
Referent Tracking and Ontology with Applications to Demographics +1
Spreadsheets, Modelling & Databases
ece 720 intelligent web: ontology and beyond
Center of Excellence in Bioinformatics and Life Sciences
Werner CEUSTERS, Barry SMITH
Implementation of Learning Systems
Representations & Reasoning Systems (RRS) (2.2)
Requirements for MFI Part6: Registration procedure
Werner CEUSTERS1,2,3 and Jonathan BLAISURE1,3
Logical Agents Prof. Dr. Widodo Budiharto 2018
Presentation transcript:

Principles of Referent Tracking BMI714 Course 22630 – Spring 2019 Class 8 – March 26, 2019 Referent Tracking Tuples Werner CEUSTERS

Essentials of Referent Tracking Deciding what particulars should receive a universally unique identifier (IUI); Finding out whether or not a particular has already been assigned a IUI (each particular should receive maximally one IUI); Generating an IUI; Using IUIs in information systems, i.e. issues concerning the syntax and semantics of statements containing IUIs; Determining the truth values of statements in which IUIs are used; Correcting errors in the assignment of IUIs and in other assertions in tuples..

Referent Tracking System Components Referent Tracking Software: Manipulation of assertions about particulars. Referent Tracking Datastore: IUI repository: A collection of: A-tuples, each one representing the assignment of a globally unique singular identifier to some particular. N-tuples, each one providing a name for a particular. Referent Tracking Database: A collection of assertions in the form of tuples of various sorts about the particulars denoted through A-tuples in the IUI repository. Belief assertions and revisions: A collection of D-tuples. Manzoor S, Ceusters W, Rudnicki R. Implementation of a Referent Tracking System. International Journal of Healthcare Information Systems and Informatics 2007;2(4):41-58.

Referent Tracking System Environment Manzoor S, Ceusters W, Rudnicki R. Implementation of a Referent Tracking System. International Journal of Healthcare Information Systems and Informatics 2007;2(4):41-58.

Scenario Dr Jones, a GP, and Dr. Doe, an internist, have distinct medical practices and use each since a while a distinct EHR system, both, however, connected to the same RT system. At time t1, Dr. Jones (GP) registers that Mr. Smith, a new patient, has Diabetes Type 1 and refers him to Dr. Doe. At time t2, Dr. Doe registers that Mr. Smith has Diabetes Type II. What are the minimal changes in the RT system?

Assumptions Jones and Doe are already registered in the RTS They execute all assertions through the EHR system which translates them into RT-tuples All required relationships and concepts or universals are taken from concept systems resp. ontologies available for the RT system

A-tuples: assertion of assignments IUI assignment is an act represented through an A-tuple IUIa = <pa, pp, tap> pa IUI of the author of the assertion pp IUI of the particular tap time of the assignment The A-tuple is registered through a D-tuple: <IUId, IUIa, td, E, C, S >, for which (for now): IUId IUI of the registering agent IUIa the IUI of the A-tuple td time of registering IUIa in the IUI-repository Neither td or tap give any information about when #pp started to exist ! That might be asserted in statements providing information about #pp .

< IUIa, ta, ntj, ni, IUIp, tr, IUIc> PtoN-statements (1) < IUIa, ta, ntj, ni, IUIp, tr, IUIc> The person referred to by IUIa asserts at time ta that ni is the name of the nametype ntj that designates in the context IUIC in the real world the particular referred to by IUIp at tr. This template will further be referred to as PtoN template. Requires: an ontology of name types, Identification of communities within which the names are accepted. Ceusters W, Manzoor S. How to track absolutely everything? In: Obrst L, Janssen T, Ceusters W (eds.) Ontologies and Semantic Technologies for the Intelligence Community. Frontiers in Artificial Intelligence and Applications. IOS Press Amsterdam, 2010;:13-36.

< IUIa, ta, ntj, ni, IUIp, tr, IUIc> PtoN-statements (2) < IUIa, ta, ntj, ni, IUIp, tr, IUIc> IUIa is the IUI of the author asserting that n is a name of type nt used by IUIc to denote IUIp; ta is a time-stamp indicating when the assertion was made; IUIc is the IUI for the particular that uses the name n (this can be a person, a community of persons, an organization, an information system, ...); IUIp is the IUI referring to the particular which the author associates with n; ni is the name which the author associates with IUIp; ntj is the nametype (examples being first name, last name, nick name, medical record number, and so forth); and tr is a time-stamp representing a time at which the author considers the association appropriate.

Tuple registrations for Mr. Smith Repository + Data store tuples D-tuples: <IUId, IUIa, td, E, C, S > IUIa #100= <IUI-Jones, IUI-Smith, t1> <IUI-Jones, #100, t1+, E, C, S > #101=< IUI-Jones, t1+, U-LastName, Smith, IUI-Smith, t-2000, , IUI-person-namecontext> <IUI-Jones, #101, t1+, E, C, S > #102=< IUI-Jones, t1+, U-ISO-time-designation, “1959:03:01:2 / ” , t-2000, , t1+, IUI-worldcontext> <IUI-Jones, #102, t1+, E, C, S >

D-tuples: Validity and availability of information < IUId, IUIT, td, E, C, S >, where: IUId: is the IUI of the entity annotating IUIT by means of this D-tuple, IUIT is the IUI of the tuple about which the D-tuple contains information, E is any of the configuration symbols ‘P+1’, ‘P-1’, … C is indication for the reason of change td is the time the tuple denoted by IUIT is inserted or ‘retired’, and S is a list of IUIs denoting the tuples, if any, that replace the retired one. A D-tuple is inserted: to resolve mistakes in RTS, and whenever a new tuple other than a D-tuple is inserted in the RTS. Corrected from: Ceusters W. Dealing with Mistakes in a Referent Tracking System. In: Hornsby KS (eds.) Proceedings of Ontology for the Intelligence Community 2007 (OIC-2007), Columbia MA, 28-29 November 2007;:5-8.

Error types for representations in RTS configuration reality representation ME authors' belief encoding OE OR BE BR IE TR (1) (2) (3) (4) (5) (6) (7) (8) Reality: OE: Objective existence OR: Objective relevance Representation: BE: Author’s belief in existence BR: Author’s belief in relevance IE: Author’s intended encoding TR: Type of reference ME: Magnitude of error Seppãlã S, Smith B, Ceusters W. Applying the realism-based ontology versioning method for tracking changes in the Basic Formal Ontology. Formal Ontology in Information Systems. Proceedings of the Eight International Conference (FOIS 2014), Amsterdam: IOS Press, 2014;:227-240.

Configuration types P: present in the ontology P+: justifiably present reality representation ME authors' belief encoding OE OR BE BR IE TR (1) (2) (3) (4) (5) (6) (7) (8) P+1 A+1 A+2 A+3 A+4 P-1 P-2 P-3 P-4 P-5 P-6 P-7 P-8 P-9 P-10 P-11 P-12 A-1 A-2 A-3 A-4 A-5 Configuration types P: present in the ontology P+: justifiably present P–: unjustifiably present A: absent from the ontology A+: justifiably absent A–: unjustifiably absent

configuration reality representation ME authors' belief encoding OE OR BE BR IE TR (1) (2) (3) (4) (5) (6) (7) (8) P+1 A+1 A+2 A+3 A+4 P-1 P-2 P-3 P-4 P-5 P-6 P-7 P-8 P-9 P-10 P-11 P-12 A-1 A-2 A-3 A-4 A-5 Configuration types 1+4+12+5=22 possible configurations based on (mis)matches between reality, beliefs, and encodings

Y/Y: correct assertion of the existence of a POR; configuration reality representation ME authors' belief encoding OE OR BE BR IE TR (1) (2) (3) (4) (5) (6) (7) (8) P+1 Y R+ A+1 N na A+2 A+3 NC A+4 P-1 ¬R 3 P-2 4 P-3 R– 5 P-4 1 P-5 R- 2 P-6 P-7 P-8 P-9 R++ P-10 P-11 Ra P-12 A-1 A-2 A-3 A-4 A-5 OE/BE value pairs Y/Y: correct assertion of the existence of a POR; Y/N: lack of awareness of a POR, reflecting an assertion error; N/N: correct assertion that some putative POR does not exist; N/Y: the false belief that some putative POR exists; Y/NC: not considering that some POR exists; N/NC: not considering that some putative POR does not exist.

configuration reality representation ME authors' belief encoding OE OR BE BR IE TR (1) (2) (3) (4) (5) (6) (7) (8) P+1 Y R+ A+1 N na A+2 A+3 NC A+4 P-1 ¬R 3 P-2 4 P-3 R– 5 P-4 1 P-5 R- 2 P-6 P-7 P-8 P-9 R++ P-10 P-11 Ra P-12 A-1 A-2 A-3 A-4 A-5 2 4 1 3 ‘na’ = not applicable If there is no POR of a specific sort, relevance is not applicable. If an author does not believe in some POR, believed relevance is not applicable. If an author did not consider (‘nc’) existence of some POR, believed relevance is not applicable. If believed relevance is either negative or not applicable, encoding is not applicable.

: faithful reference to correct referent configuration reality representation ME authors' belief encoding OE OR BE BR IE TR (1) (2) (3) (4) (5) (6) (7) (8) P+1 Y R+ A+1 N na A+2 A+3 NC A+4 P-1 ¬R 3 P-2 4 P-3 R– 5 P-4 1 P-5 R- 2 P-6 P-7 P-8 P-9 R++ P-10 P-11 Ra P-12 A-1 A-2 A-3 A-4 A-5 Modes of reference: R+ : faithful reference to correct referent ¬R : no referent exist R– : reference to wrong referent R++ : redundant reference Ra : ambiguous reference

Magnitude of error configuration reality representation ME authors' belief encoding OE OR BE BR IE TR (1) (2) (3) (4) (5) (6) (7) (8) P+1 Y R+ A+1 N na A+2 A+3 NC A+4 P-1 ¬R 3 P-2 4 P-3 R– 5 P-4 1 P-5 R- 2 P-6 P-7 P-8 P-9 R++ P-10 P-11 Ra P-12 A-1 A-2 A-3 A-4 A-5 Magnitude of error

Tuple registrations for Mr. Smith Repository + Data store tuples D-tuples: <IUId, IUIa, td, E, C, S > IUIa #100= <IUI-Jones, IUI-Smith, t1> <IUI-Jones, #100, t1+, ‘P+1’ , ‘’, ‘’ > #101=< IUI-Jones, t1+, U-LastName, Smith, IUI-Smith, t-2000, , IUI-person-namecontext> <IUI-Jones, #101, t1+, ‘P+1’ , ‘’ ,‘’ > #102=< IUI-Jones, t1+, U-ISO-time-designation, “1959:03:01:2 / ” , t-2000, , t1+, IUI-worldcontext> <IUI-Jones, #102, t1+, ‘P+1’ , ‘’ , ‘’ >

PtoP statements - particular to particular ordered sextuples of the form Ri = <IUIa, ta, r, o, P, tr> IUIa is the IUI of the author of the statement, ta a reference to the time when the statement is made, r a reference to a relationship (available in o) obtaining between the particulars referred to in P, o a reference to the ontology from which r is taken, P an ordered list of IUIs referring to the particulars between which r obtains, and, tr a reference to the time at which the relationship obtains. P contains as much IUIs as required by the arity of r. In most cases, P will be an ordered pair such that r obtains between the particular represented by the first IUI and the one referred to by the second IUI. As with A statements, these statements must also be accompanied by a meta-statement (D-tuple) capturing when the sextuple became available to the referent tracking system. Ceusters W, Manzoor S. How to track absolutely everything? In: Obrst L, Janssen T, Ceusters W (eds.) Ontologies and Semantic Technologies for the Intelligence Community. Frontiers in Artificial Intelligence and Applications. IOS Press Amsterdam, 2010;:13-36.

Tuple registrations for Mr. Smith Repository + Data store tuples D-tuples: <IUId, IUIa, td, E, C, S > IUIa #100= <IUI-Jones, IUI-Smith, t1> <IUI-Jones, #100, t1+, ‘P+1’ , ‘’ , ‘’ > #101=< IUI-Jones, t1+, U-LastName, Smith, IUI-Smith, t-2000, , IUI-person-namecontext> <IUI-Jones, #101, t1+, ‘P+1’ , ‘’ , ‘’ > #102=< IUI-Jones, t1+, U-ISO-time-designation, “1959:03:01:2 / ” , t-2000, , t1+, IUI-worldcontext> <IUI-Jones, #102, t1+, ‘P+1’ , ‘’ , ‘’ > #103=<IUI-Jones, t1+, patient-of, IUI-OGMS, <IUI-Smith, IUI-Jones>, t-20001 /or: ‘since t1’> <IUI-Jones, #103, t1+, ‘P+1’ , ‘’ , ‘’ >

Tuple registrations for Mr. Smith Repository + Data store tuples D-tuples: <IUId, IUIa, td, E, C, S > IUIa #100= <IUI-Jones, IUI-Smith, t1> <IUI-Jones, #100, t1+, ‘P+1’ , ‘’ , ‘’ > #101=< IUI-Jones, t1+, U-LastName, Smith, IUI-Smith, t-2000, , IUI-person-namecontext> <IUI-Jones, #101, t1+, ‘P+1’ , ‘’ , ‘’ > #102=< IUI-Jones, t1+, U-ISO-time-designation, “1959:03:01:2 / ” , t-2000, , t1+, IUI-worldcontext> <IUI-Jones, #102, t1+, ‘P+1’ , ‘’ , ‘’ > #103=<IUI-Jones, t1+, patient-of, IUI-OGMS, <IUI-Smith, IUI-Jones>, t-20001 /or: ‘since t1’> <IUI-Jones, #103, t1+, ‘P+1’ , ‘’ , ‘’ > IUIa #104= <IUI-Jones, #-123, t1+> <IUI-Jones, #104, t1+, ‘P+1’ , ‘’ , ‘’ >

PtoU statements – particular to universal Ui = <IUIa, ta, inst, o, IUIp, u, tr> IUIa is the IUI of the author of the statement, ta a reference to the time when the statement is made, inst a reference to an instance relationship available in o obtaining between p and cl, o a reference to the ontology from which inst and u are taken, IUIp the IUI referring to the particular whose inst relationship with u is asserted, u the universal in o to which p enjoys the inst relationship, and, tr a reference to the time at which the relationship obtains.

Focusing on the disease Repository + Data store tuples D-tuples: <IUId, IUIa, td, E, C, S > #103=<IUI-Jones, t1+, patient-of, IUI-OGMS, <IUI-Smith, IUI-Jones>, t-20001 /or: ‘since t1’> <IUI-Jones, #103, t1+, ‘P+1’ , ‘’ , ‘’ > #104= <IUI-Jones, #-123, t1+> <IUI-Jones, #104, t1+, ‘P+1’ , ‘’ , ‘’ > #105 = <IUI-Jones, t+1, inst, IUI-OGMS, #123, UUI-for-DM1, t5000> #106=<IUI-Jones, t1+, part-of, IUI-BFO, <t1, t5000>,’’>

U--tuples: “negative findings” Ui = <IUIa, ta, r, o, IUIp, u, tr> For saying that a particular is not an instance of some universal, ‘r’ should denote the identity-relation in ontology o. Do not confuse with retiring erroneous statements. The particular referred to by IUIa asserts at time ta that the relation r of ontology o does not obtain at time tr between the particular referred to by IUIp and any of the instances of the universal u at time tr Ceusters W, Elkin P, Smith B. Negative Findings in Electronic Health Records and Biomedical Ontologies: A Realist Approach. International Journal of Medical Informatics 2007;76:326-333

PtoCO statements: particular to concept code Coi = <IUIa, ta, cbs, IUIp, co, tr> IUIa is the IUI of the author of the statement, ta a reference to the time when the statement is made, cbs a reference to the concept-based system from which co is taken, IUIp the IUI referring to the particular which the author associates with co, co the concept-code in cbs which the author associates with p, and, tr a reference to the time at which the author considers the association appropriate, Ceusters W, Manzoor S. How to track absolutely everything? In: Obrst L, Janssen T, Ceusters W (eds.) Ontologies and Semantic Technologies for the Intelligence Community. Frontiers in Artificial Intelligence and Applications. IOS Press Amsterdam, 2010;:13-36.

Interpretation of PtoCO statements must be interpreted as simple indexes to terms in a dictionary. All that such a statement tells us, is that within the linguistic and scientific community in which cbs is used, the terms associated with co may - i.e. are acceptable to - be used to denote p in their determinative version.

A SNOMED-CT example <IUI-0945, 18/04/2005, SNOMED-CT v0301, IUI-1921, 367720001, forever> #IUI-0945: author of the statement #IUI-1921: the left testicle of patient #IUI-78127 367720001: the SNOMED concept-code to which “left testis” is (in SNOMED) attached as term So we can denote #IUI-1921 by means of that left testis that entire left testis that testicle, that male gonad, that testis that genital structure that physical anatomical entity BUT NOT: that SNOMED-CT concept

Specifying times in RT tuples General format: <temporal operator, temporal region> E.g.: ‘at IUIt’ where IUIt is the IUI denoting the temporal region Temporal operators come from ISO/DIS 12381(en)

Use of the CEN Time Standard for HIT ISO/DIS 12381(en) Health informatics — Time standards for healthcare specific problems

Specifying times in RT tuples General format: <temporal operator, temporal region> E.g.: ‘at IUIt’ where IUIt is the IUI denoting the specific instance of BFO:temporal-region. Temporal operators come from ISO/DIS 12381(en). Temporal specification of temporal regions through: Relationship to other temporal regions using the temporal operators as relationships in PtoP tuples where the relata are temporal regions or temporal boundaries, PtoN-tuples using a time notation standard (e.g. GMT).

Focusing on the disease Repository + Data store tuples D-tuples: <IUId, IUIa, td, E, C, S > #103=<IUI-Jones, t1+, patient-of, IUI-OGMS, <IUI-Smith, IUI-Jones>, t-20001 /or: ‘since t1’> <IUI-Jones, #103, t1+, ‘P+1’ , ‘’ , ‘’ > #104= <IUI-Jones, #-123, t1+> <IUI-Jones, #104, t1+, ‘P+1’ , ‘’ , ‘’ > #105 = <IUI-Jones, t+1, inst, IUI-OGMS, #123, UUI-for-DM1, t5000> <IUI-Jones, #105, t1+, ‘P+1’ , ‘’ , ‘’ > <IUI-Doe, #105, t2+, ‘P-1’ , C, ‘’ , ‘#110’ > #106=<IUI-Jones, t1+, part-of, IUI-BFO, <t1, t5000>,’’> #110= <IUI-Doe, t+2, inst, IUI-OGMS, #123, UUI-for-DM2, t6000> <IUI-Doe, #110, t2+, ‘P+1’ , ‘’ , ‘’ > <IUI-Jones, #110, t3+, ‘P-1’ , C, ‘#105’ >