R T U New York State Center of

  • Slides: 64
Download presentation
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 Command Control Ontology - Informal Technical Exchange - Use of Ontologies in a World in Flux National Center for Ontological Research (NCOR) Buffalo, NY, USA, January 15 -16, 2009 Werner CEUSTERS, MD Center of Excellence in Bioinformatics and 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 Presentation overview • The SAS-050 approach to Command & Control • How the SAS-050 model relates to Basic Formal Ontology and Referent Tracking • Referent Tracking for C 2 – A battlefield scenario • Technical implementation of Referent Tracking • How Basic Formal Ontology and Referent Tracking meet the wish-list of SAS-050 • Conclusion

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 Background for this presentation EXPLORING NEW COMMAND CONTROL CONCEPTS AND CAPABILITIES Final Report Prepared for NATO January 2006 http: //www. dodccrp. org/files/SAS-050%20 Final%20 Report. pdf

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 SAS-050’s view on C 2 Three major dimensions: 1. allocation of decision rights across an enterprise, 2. permissible interactions among entities within the enterprise and between enterprise entities and others, 3. the way information flows and is disseminated.

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 C 2 in action

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 … in. C 2 a world in action in flux

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 … in. C 2 a world in action in flux ‘Ground truth’

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 Information C 2 in and action ground truth ‘Ground truth’Information

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 Information C 2 in and action ground truth Information ‘Ground truth’ How do they relate ?

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 SAS-050’s view on information and ground truth

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 Characteristics for Inf. Quality and Reality Perception • Accuracy: the degree to which INF quality matches what is needed. • Completeness: extent to which INF relevant to ground truth is collected. • Consistency: extent to which INF is consistent with prior INF and consistent across sources. • Correctness: extent to which INF is consistent with ground truth. • Currency: difference between the current point in time and the time the INF was made available. • Precision: level of measurement detail of INF item. • Relevance: extent to which INF quality is relevant to the task at hand. • Timeliness: extent to which currency of INF is suitable to its use; the relationship between availability of the INF and when it is needed. • Uncertainty: a fundamental attribute of war and pervades the battlefield in the form of unknowns about the enemy, the surroundings, and our own forces. • Sharability: extent to which an element of INF is in a form or format understandable by all nodes in a network. • Source characteristics: the traits of tools used to develop facts, data, or instructions in any form or medium (and all INF sources are reporters). • • • Ambiguity: inability to make sense out of a situation, regardless of available INF. Complexity: situation is being faced with a situation made up of an interrelated set of variables, solutions, and stakeholders, each individually understood but which together exceed the processing capacity of the individual, the team, or organisation to synthesize. Equivocality: having multiple interpretations of the same INF. Uncertainty: not having sufficient INF to describe a current state or to forecast future states, preferred outcomes, or the actions needed to achieve them. Situational familiarity: the characteristic of having encountered or seen, or having knowledge of a situation. Temporal focus: the time into the future of an understanding or plan.

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 Relationships between the SAS-050 objectives and Ontology / Referent Tracking

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 SAS-050 solution • A Conceptual Model (CM) consisting of: – A Reference Model containing over 300 variables and a selected subset of the possible relationships among them that were felt to be important to understand C 2 and the implications of different approaches to C 2. – A Value View which posits links in the value chain that lead from characteristics of the force and its approach to C 2 to measures of mission and policy effectiveness, and finally to agility.

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 SAS-050’s view on ‘models’ • A model is an abstraction of reality for a purpose, consisting of a subset of variables and relationships that represent reality “well enough. ” • The variables found within the model are factors, characteristics, or attributes of an entity that can take on different values. • The variables within the model have a number of relationships that reflect connections between and among other variables.

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 The fit with Philosophical Realism • Three levels of reality: 1. 2. 3. ground truth / situations First-order reality: what is on the side of persons, organizations, … Cognitive representations: what cognitive mental models agents assume to observe and know ‘in their mind’ Representational artefacts for information communication, documentation, … • • In SAS-050: Terms, definitions, drawings, images, … Assumption about the quality of an ontology: is at least determined by the accuracy with which its structure mimics the pre-existing structure of reality. … about the model 1. acceptance 2. fit for purpose 3. never complete Smith B, Kusnierczyk W, Schober D, Ceusters W. Towards a Reference Terminology for Ontology Research and Development in the Biomedical Domain. Proceedings of KR-MED 2006, November 8, 2006, Baltimore MD, USA

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 Generic versus Specific entities Generic 3. Representation 2. Beliefs (knowledge) 1. First-order reality ‘weapon’ ‘person’ Specific ‘tank’ GOAL ATTACK STRATEGY building WEAPON TANK PERSON CORPSE SOLDIER Basic Formal Ontology ‘John Doe’s plan John Doe’s platoon ‘Enola Gay’ SACEUR’s strategy Private John Doe’s gun Tank with serial number TH 1280 A 44 V Referent Tracking

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 A simple battlefield ontology Spatial region located-in object Ontology weapon vehicle building person corpse transforms-in mortar submachine gun tank car POW soldier

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 Ontology used for ‘annotating’ a situation Spatial region Ontology mortar Situation located-in weapon object vehicle building person corpse transforms-in submachine gun tank car POW soldier

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 Referent Tracking in action

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 Referent Tracking (RT) for ‘representing’ a situation Ontology #5 Situational model Situation #6 uses #7 #8 uses #1 uses #2 uses #3 #4 #10

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 Advantages of Referent Tracking • Preserves identity

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 Referent Tracking preserves identity Ontology #6 Situational model Situation use the same type of weapon #7 use the same weapon #8 uses #2 uses #3 #4 #10

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 Advantages of Referent Tracking • Preserves identity • Allows to assert relationships amongst entities that are not generically true

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 Specific relations versus generic relations Ontology #5 Situational model Situation #6 uses #7 uses #1 faithful #8 uses #2 uses #3 #4 #10

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 Specific relations versus generic relations Ontology uses Situational model Situation NOT faithful

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 Advantages of Referent Tracking • Preserves identity • Allows to assert relationships amongst entities that are not generically true • Appropriate representation of the time when relationships hold

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 Temporal validity of specific relationships (1) soldier Ontology private Situational model Situation sergeant at t 1 sergeant-major at t 2 at t 3 #3

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 Temporal validity of specific relationships (2) Ontology #5 Situational model Situation #6 uses at t 1 #1 uses at t 1 #2

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 Temporal validity of specific relationships (2) Ontology #5 Situational model Situation uses at t 2 #1 after the death of #1 #2 at t 2

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 Advantages of Referent Tracking • Preserves identity • Allows to assert relationships amongst entities that are not generically true • Appropriate representation of the time when relationships hold • Deals with conflicting representations by keeping track of sources

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 Source of information Ontology corpse asserts at t 2 #5 Situational model Situation #6 uses at t 2 uses at t 1 #1 #2 at t 3

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 Source of information Ontology corpse asserts at t 4 #5 Situational model Situation #6 uses at t 2 uses at t 1 #1 #2 at t 3

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 Advantages of Referent Tracking • Preserves identity • Allows to assert relationships amongst entities that are not generically true • Appropriate representation of the time when relationships hold • Deals with conflicting representations by keeping track of sources • Mimics the structure of reality

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 Referent Tracking and the structure of reality Level 1, 2 or 3 Level 1 unique identifiers

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 Remind the 3 -level distinction • Level 1: – #120: an incident that happened; • Level 2: – #213: the interpretation by some cognitive agent that #120 is an security breach; – #31: the expectation by some cognitive agent that similar incidents might happen in the future; • Level 3: – #402: an entry in and information system concerning #120; – #1503: an entry in some other information system about #31 for mitigation or prevention purposes.

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 Advantages of Referent Tracking • Preserves identity • Allows to assert relationships amongst entities that are not generically true • Appropriate representation of the time when relationships hold • Deals with conflicting representations by keeping track of sources • Mimics the structure of reality • Allows for corrections without distorting what was originally believed

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 Mismatches between reality and representations • Some possibilities: 1. #120 with unjustified absence of #213 : • #120 was not perceived at all, or not assessed as being a security breach 2. Unjustified presence of #213 : • There was no #120 at all, or #120 was not a security breach 3. Unjustified absence of #402 • Same reasons as under (1) above • Justified presence of #213 but not reported in the information system – … Ceusters W, Smith B. A Realism-Based Approach to the Evolution of Biomedical Ontologies. Proceedings of AMIA 2006, Washington DC, 2006; : 121 -125.

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 Multiple scenarios of co-existence Case 1 2 3 4 5 6 7 8 9 10 11 12 13 14 breach happened Past incident related incident perception Mitigation related Interpreted Registered Information system entry #120 #213 #402 #31 #1503 + + + + - + + + + + + + + Only cases 7 and 8 are faithful, justified presence and absence respectively

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 Need for change and belief management • Distinct sensors may hold different beliefs about whether a specific incident (e. g. #1) – really happened, – is of a specific sort, – counts as a security breach • depending on what definition or rules they apply. • They may differ in beliefs about – what caused the incident, – how to prevent future happenings of incidents of the same sort. • They may change their beliefs over time.

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 Keep in mind • Whether an incident is a security breach (under one or more definitions) – is a matter of objective fact, – is not a matter of consensus. • What are matters of consensus, are: – definitions for what should be counted security breaches • but, – they can be applied wrongly, – they can be themselves in error; – policies about registration, – policies about mitigation and prevention, • although, whether they are effective, is again a matter of objective fact.

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 Advantages of Referent Tracking • Preserves identity • Allows to assert relationships amongst entities that are not generically true • Appropriate representation of the time when relationships hold • Deals with conflicting representations by keeping track of sources • Mimics the structure of reality • Allows for corrections without distorting what was originally believed • Fully compatible with semantic web technologies

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 Implementing Referent Tracking

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 Explicit referential semantics through RT-tuples (1) Situational model tuples Tuple name A-tuple Attributes Description < IUIa, IUIp, tap> Act of assignment of IUIp to a particular at time tap by the particular referred to by author IUIa Pto. P-tuple <IUIa, ta, r, IUIo, P, tr> The particular referred to by IUIa asserts at time ta that the relationship r from ontology IUIo obtains between the particulars referred to in the set of IUIs P at time tr. Pto. N < IUIa, ta, ntj, ni, IUIp, tr, IUIc> The particular referred to by IUIa asserts at time ta that ni is the name of the nametype ntj used by IUIc to denote the particular referred to by IUIp at tr.

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 Explicit referential semantics through RT-tuples (2) Linking situational models with ontologies and terminologies Tuple name Pto. U-tuple Attributes Description <IUIa, ta, inst, IUIo, IUIp, UUI, tr> The particular referred to by author IUIa asserts at time ta that the particular referred to by IUIp instantiates – by means of the inst relation defined in ontology IUIo – the universal UUI at time tr. Pto. C-tuple <IUIa, ta, IUIc, IUIp, CUI, tr> The particular referred to by IUIa asserts at time ta that at time tr concept code CUI from terminology system IUIc is an accurate term for IUIp Pto. U(-) -tuple <IUIa, ta, r, IUIo, IUIp, UUI, tr> The particular referred to by IUIa asserts at time ta that the relation r of ontology IUIo does not obtain at time tr between the particular referred to by IUIp and any of the instances of the universal denoted by UUI at time tr.

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 Explicit referential semantics through RT-tuples (3) Validity and availability of information Tuple name D-tuple Attributes Description < IUId, IUIA, td, E, C, S > The particular referred to by IUId registers the particular referred to by IUIA (the IUI for the corresponding A-tuple) at time td. E is either the symbol ‘I’ (for insertion) or any of the error type symbols as defined in [1]. C is the reason for inserting the A-tuple. S is a list of IUIs denoting the tuples, if any, that replace the retired one. A D-tuple is inserted: (1) to resolve mistakes in RTS, and (2) whenever a new tuple other than a D-tuple is inserted in the RTS. [1] 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.

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 Referent Tracking System Components • Referent Tracking Software Manipulation of statements about facts and beliefs • Referent Tracking Datastore: • IUI repository A collection of globally unique singular identifiers denoting particulars • Referent Tracking Database A collection of facts and beliefs about the particulars denoted in the IUI repository 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.

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 Referent Tracking System Environment

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 Networks of Referent Tracking systems

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 An example: Tracking a Request to View a Web Page

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 Tuple insertions A-tuples n IUIp IUIa tap Key 1 #24 #2 (EVENT("#24 assignment") has-occ AT TP(time-18)) #25 3 #27 #2 (EVENT("#27 assignment") has-occ AT TP(time-20)) #28 9 #34 #2 (EVENT("#34 assignment") has-occ AT TP(time-26)) #35 D-tuples n IUId IUIA td E C S Key 2 #2 #25 (EVENT("#25 inserted") has-occ AT TP(time-19)) I CE #26 4 #2 #28 (EVENT("#28 inserted") has-occ AT TP(time-21)) I CE #29 6 #2 #30 (EVENT("#30 inserted") has-occ AT TP(time-23)) I CE #31 8 #2 #32 (EVENT("#32 inserted") has-occ AT TP(time-25)) I CE #33 10 #2 #35 (EVENT("#35 inserted") has-occ AT TP(time-27)) I CE #36 12 #2 #37 (EVENT("#37 inserted") has-occ AT TP(time-29)) I CE #38 Pto. P-tuples n IUIa ta r IUIo 5 #2 (EVENT("#30 is asserted") has-occ AT TP(time-22)) Main. Content. Copy. Of #022 7 #2 (EVENT("#32 is asserted") has-occ AT TP(time-24)) Instigator. Of 11 #2 (EVENT("#37 is asserted") has-occ AT TP(time-28)) Checksum. Of P tr Key #27, #12 (EPISODE("#30 is true") has-occ SINCE TI(time-20)) #30 #022 #24, #27 (EVENT ("#32 is true") has-occ AT TP(time-18)) #32 #022 #34, #27 (EPISODE("#37 is true") has-occ SINCE TI(time-26)) #37

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 Another example: domotics and RFID systems • Avoiding adverse events in a hospital because of insufficient day/night illumination: – Light sensors and motion detectors in rooms and corridors • and representations thereof in an Adverse Event Management System (AEMS) – What are ‘sufficient’ illumination levels for specific sites is expressed in defined classes, – Each change in a detector is registered in real time in the AEMS, – Action-logic implemented in a rule-base system, f. i. to generate alerts.

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 RT-based representation (1): IUI assignment Reality level 1 #1: that corridor #2: that lamp #3: that motion detector #4: that light detector #5: that RFID reader #6: that patient with RFID #7 #8: that RFID reader #9: this elevator #10: 2 nd floor of clinic B

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 RT-based representation (2): relationships • (Semi-)stable relationships: – – – – #1 instance-of Re. M: Corridor since t 1 #2 instance-of Re. M: Lamp since t 2 #2 contained-in #1 since t 3 #6 member-of Re. M: Patient since t 4 #6 adjacent-to #7 since t 4 #18 instance-of Re. M: Illumination since t 1 #18 inheres-in #1 since t 1 … • Semi-stable because of: – lamps may be replaced – persons are not patients all the time – … • keeping track of these changes provides a history for each tracked entity

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 RT-based representation (3): rule base * • Setting illumination requirements for lamp #2: – #18 member-of Re. M: Insufficient illumination during ty • if – tx part-of Re. M: Daytime – #y 1 instance-of Re. M: Motion-detection – #y 1 has-agent #3 – ty part-of tx – #y 2 instance-of Re. M: Illumination measurement – #y 2 has-agent #4 – #y 2 has-participant #18 – #y 2 has-result imrz – imrz less-than 30 lumen at ty • else – tx – … part-of Re. M: Night time • endif * Exact format to be discussed with Re. MINE partners

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 RT-based representation of events • Imagine #6 (with RFID #7) walking through #1 – – – #2345 instance-of Re. M: Motion-detection #2345 has-agent #3 at t 4 #2346 instance-of Re. M: RFID-detection #2346 has-agent #5 at t 4 #2346 has-participant #7 at t 4 … • Here, the happening of #2345 fires the rule explained on the previous slide. • If imrz turns out to be too low, that might invoke another rule which sends an alert to the ward that lamp #2 might be broken. • #2346 might trigger yet another rule, namely an alert for imminent danger for AE with respect to patient #6 • …

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 Suitability of Basic Formal Ontology and Referent Tracking for a SAS-050 implementation (Focus on Information Quality)

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 RT and SAS-050 INF quality: everything is computable! • Accuracy: the degree to which INF quality matches what is needed. • Completeness: extent to which INF relevant to ground truth is collected. • Consistency: extent to which INF is consistent with prior INF and consistent across sources. • Correctness: extent to which INF is consistent with ground truth. • Currency: difference between the current point in time and the time the INF was made available. • Precision: level of measurement detail of INF item. • Relevance: extent to which INF quality is relevant to the task at hand. • Timeliness: extent to which currency of INF is suitable to its use; the relationship between availability of the INF and when it is needed. • Uncertainty: a fundamental attribute of war and pervades the battlefield in the form of unknowns about the enemy, the surroundings, and our own forces. • Sharability: extent to which an element of INF is in a form or format understandable by all nodes in a network. • Source characteristics: the traits of tools used to develop facts, data, or instructions in any form or medium (and all INF sources are reporters). • Representation of need and relevance in ontologies, plans and policies, • INF accumulates in RTtuples, • Accuracy and relevance computable over the difference between the former and the latter.

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 RT and SAS-050 INF quality: everything is computable! • Accuracy: the degree to which INF quality matches what is needed. • Completeness: extent to which INF relevant to ground truth is collected. • Consistency: extent to which INF is consistent with prior INF and consistent across sources. • Correctness: extent to which INF is consistent with ground truth. • Currency: difference between the current point in time and the time the INF was made available. • Precision: level of measurement detail of INF item. • Relevance: extent to which INF quality is relevant to the task at hand. • Timeliness: extent to which currency of INF is suitable to its use; the relationship between availability of the INF and when it is needed. • Uncertainty: a fundamental attribute of war and pervades the battlefield in the form of unknowns about the enemy, the surroundings, and our own forces. • Sharability: extent to which an element of INF is in a form or format understandable by all nodes in a network. • Source characteristics: the traits of tools used to develop facts, data, or instructions in any form or medium (and all INF sources are reporters). • Remain essentially unknown at T 0 , • Can for the past be calculated using: • Can be forecasted using: Ceusters W. Applying Evolutionary Terminology Auditing to the Gene Ontology. Journal of Biomedical Informatics 2009 (in press).

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 RT and SAS-050 INF quality: everything is computable! • Accuracy: the degree to which INF quality matches what is needed. • Completeness: extent to which INF relevant to ground truth is collected. • Consistency: extent to which INF is consistent with prior INF and consistent across sources. • Correctness: extent to which INF is consistent with ground truth. • Currency: difference between the current point in time and the time the INF was made available. • Precision: level of measurement detail of INF item. • Relevance: extent to which INF quality is relevant to the task at hand. • Timeliness: extent to which currency of INF is suitable to its use; the relationship between availability of the INF and when it is needed. • Uncertainty: a fundamental attribute of war and pervades the battlefield in the form of unknowns about the enemy, the surroundings, and our own forces. • Sharability: extent to which an element of INF is in a form or format understandable by all nodes in a network. • Source characteristics: the traits of tools used to develop facts, data, or instructions in any form or medium (and all INF sources are reporters). • Can be computed using the author-attributes of the RT-tuples and the presence of D-tuples using corrections, • Allows even to compute the quality of sources.

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 RT and SAS-050 INF quality: everything is computable! • Accuracy: the degree to which INF quality matches what is needed. • Completeness: extent to which INF relevant to ground truth is collected. • Consistency: extent to which INF is consistent with prior INF and consistent across sources. • Correctness: extent to which INF is consistent with ground truth. • Currency: difference between the current point in time and the time the INF was made available. • Precision: level of measurement detail of INF item. • Relevance: extent to which INF quality is relevant to the task at hand. • Timeliness: extent to which currency of INF is suitable to its use; the relationship between availability of the INF and when it is needed. • Uncertainty: a fundamental attribute of war and pervades the battlefield in the form of unknowns about the enemy, the surroundings, and our own forces. • Sharability: extent to which an element of INF is in a form or format understandable by all nodes in a network. • Source characteristics: the traits of tools used to develop facts, data, or instructions in any form or medium (and all INF sources are reporters). • Can be computed using the various temporal attributes of the RTtuples: – D-tuples specify when INF was entered, – Other tuples specify when relationships hold or when entities come and go.

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 RT and SAS-050 INF quality: everything is computable! • Accuracy: the degree to which INF quality matches what is needed. • Completeness: extent to which INF relevant to ground truth is collected. • Consistency: extent to which INF is consistent with prior INF and consistent across sources. • Correctness: extent to which INF is consistent with ground truth. • Currency: difference between the current point in time and the time the INF was made available. • Precision: level of measurement detail of INF item. • Relevance: extent to which INF quality is relevant to the task at hand. • Timeliness: extent to which currency of INF is suitable to its use; the relationship between availability of the INF and when it is needed. • Uncertainty: a fundamental attribute of war and pervades the battlefield in the form of unknowns about the enemy, the surroundings, and our own forces. • Sharability: extent to which an element of INF is in a form or format understandable by all nodes in a network. • Source characteristics: the traits of tools used to develop facts, data, or instructions in any form or medium (and all INF sources are reporters). • Determined by: – The sensor which identifies an entity as a distinct being, – The ontology used to characterize the entity as being of a specific type.

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 RT and SAS-050 INF quality: everything is computable! • Accuracy: the degree to which INF quality matches what is needed. • Completeness: extent to which INF relevant to ground truth is collected. • Consistency: extent to which INF is consistent with prior INF and consistent across sources. • Correctness: extent to which INF is consistent with ground truth. • Currency: difference between the current point in time and the time the INF was made available. • Precision: level of measurement detail of INF item. • Relevance: extent to which INF quality is relevant to the task at hand. • Timeliness: extent to which currency of INF is suitable to its use; the relationship between availability of the INF and when it is needed. • Uncertainty: a fundamental attribute of war and pervades the battlefield in the form of unknowns about the enemy, the surroundings, and our own forces. • Sharability: extent to which an element of INF is in a form or format understandable by all nodes in a network. • Source characteristics: the traits of tools used to develop facts, data, or instructions in any form or medium (and all INF sources are reporters). • Guaranteed through: – The standard syntax and referential semantics of RTtuples, – The P 2 P and service oriented architecture of the RT system.

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 Summary and Conclusion

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 Summary and Conclusion • SAS-050, perhaps unknowingly, follows a realist agenda to achieve specific goals, • Basic Formal Ontology (BFO) and Referent Tracking (RT), on purpose, follow this agenda with broader objectives in mind: – BFO: to represent what is generic in reality – RT: to represent what is specific and relevant • Implementations of BFO and RT do not replace C 2 -systems, but, when integrated with them, provide added value in terms of, for example: – Enhanced sharability and semantic interoperability, – Unambiguous understanding of data using reality as benchmark, – Complete history of what happened, what was believed about it, and what communicated.