1. cse@buffalo Knowledge Representation and Reasoning in SNePS for Bioinformatics Stuart C. Shapiro Department of Computer Science and Engineering, and Center for Cognitive Science University at Buffalo, The State University of New York 201 Bell Hall, Buffalo, NY 14260-2000 shapiro@cse.buffalo.edu http://www.cse.buffalo.edu/~shapiro/ http://www.cse.buffalo.edu/sneps/

2. cse@buffalo November, 2003S. C. Shapiro2 SNePS  A logic- and network-based  Knowledge representation  Reasoning  and Acting  System

3. cse@buffalo November, 2003S. C. Shapiro3 Examples of SNePS for Bioinformatics  Neurological Diagnostic Expert System  System for Understanding NF1 Literature

4. cse@buffalo November, 2003S. C. Shapiro4 NEUREX: A Neurological Diagnostic Expert System  Ca. 1983 – 1986  See: Z. Xiang, J. G. Chutkow, S. C. Shapiro, and S. N. Srihari, Computerized neurological diagnosis: a paradigm of modeling and reasoning, Health Care Instrumentation 1, 3 (1986), 90-105.

5. cse@buffalo November, 2003S. C. Shapiro5 Strengths of SNePS for This Task  Integrated Analogical, Propositional, Functional Knowledge  Access to Procedural Knowledge via Acting System

6. cse@buffalo November, 2003S. C. Shapiro6 Analogical/Propositional Representation Proposition, Ventral root is proximal to spinal nerve. is represented by { } also have { }

7. cse@buffalo November, 2003S. C. Shapiro7 Analogical/Propositional From Z. Xiang, S. N. Srihari, S. C. Shapiro, and J. G. Chutkow, A modeling scheme for diagnosis, Expert Systems in Government Symposium, IEEE Computer Society Press, Silver Spring, MD, 1985, 538-547. Propositions form a relational graph.

8. cse@buffalo November, 2003S. C. Shapiro8 A Knowledge-Based Approach to Understanding the NF1 Literature  A proposal to DoD  PI: Gary R. Skuse, Director of Bioinformatics, RIT  CoPIs: Debra T. Burhans, Director, Bioinformatics, Canisius College Alistair E. R. Campbell, Computer Science, Hamilton College Stuart C. Shapiro, CSE, UB

9. cse@buffalo November, 2003S. C. Shapiro9 Goals Discover new linkages in information across Medline abstracts of research literature about Neurofibromatosis 1 (NF1, or von Recklinghausen disease)

10. cse@buffalo November, 2003S. C. Shapiro10 Strengths of SNePS for This Task  Inconsistency Tolerance/Discovery/Repair  Contexts  Multiple Sources  Amenable to Backend DB Storage

11. cse@buffalo November, 2003S. C. Shapiro11 Inconsistent Knowledge Base wff3: free(Willy) and whale(Willy) wff10: all(x)(whale(x) => mammal(x)) wff19: all(x)(whale(x) => fish(x)) wff20: all(x)(andor(0,1){mammal(x), fish(x)}) wff21: all(x)(fish(x) has(x,scales))

12. cse@buffalo November, 2003S. C. Shapiro12 Finding the Contradiction During Query Answering : has(Willy, scales)? I infer fish(Willy) I infer it is not the case that wff23: fish(Willy)

13. cse@buffalo November, 2003S. C. Shapiro13 BR Advice In order to make the context consistent you must delete at least one hypothesis from the set listed below. 1 : wff20: all(x)(andor(0,1){mammal(x),fish(x)}) (1 dependent proposition: (wff24)) 2 : wff19: all(x)(whale(x) => fish(x)) (2 dependent propositions: (wff23 wff22)) 3 : wff10: all(x)(whale(x) => mammal(x)) (3 dependent propositions: (wff24 wff15 wff11)) 4 : wff3: free(Willy) and whale(Willy) (8 dependent propositions: (wff24 wff23 wff22 wff11 wff9 wff5 wff2 wff1)) User deletes #2: wff19.

14. cse@buffalo November, 2003S. C. Shapiro14 Continuing with Repaired KB I infer it is not the case that wff22: has(Willy,scales) wff26: ~has(Willy,scales)

15. cse@buffalo November, 2003S. C. Shapiro15 Multiple Sources wff1: all(x)(andor(0,1){mammal(x),fish(x)}) wff2: all(x)(fish(x) has(x,scales)) wff4: all(x)(whale(x) => fish(x)) wff5: Source(Melville,all(x)(whale(x) => fish(x))) wff6: all(x)(whale(x) => mammal(x)) wff7: Source(Darwin,all(x)(whale(x) => mammal(x))) wff8: Sgreater(Darwin,Melville) wff11: free(Willy) and whale(Willy) Note: Source & Sgreater props are regular object-language props.

16. cse@buffalo November, 2003S. C. Shapiro16 : has(Willy, scales)? I infer fish(Willy) I infer has(Willy,scales) I infer mammal(Willy) I infer it is not the case that wff14: fish(Willy) Finding the Contradiction

17. cse@buffalo November, 2003S. C. Shapiro17 Using Source Credibility A contradiction was detected within context default-defaultct. The contradiction involves the newly derived proposition: wff17: ~fish(Willy) { } and the previously existing proposition: wff14: fish(Willy) { } The least believed hypothesis: (wff4) The most common hypothesis: (nil) The hypothesis supporting the fewest wffs: (wff1) I removed the following belief: wff4: all(x)(whale(x) => fish(x)) I no longer believe the following 2 propositions: wff14: fish(Willy) wff13: has(Willy,scales)

18. cse@buffalo November, 2003S. C. Shapiro18 Backend DB Storage  A proposal  All propositions represented with keyword arguments  Each keyword set a Relation in an RDB  Or in XML

19. cse@buffalo November, 2003S. C. Shapiro19 NEUREX Data as Relation PProximalDistal m1dorsal rootganglion m2ganglionspinal nerve m3ventral rootspinal nerve m4spinal nervedorsal ramus m5dorsal ramusmedial branch of d.r. m6medial branch of d.r.medial cutaneous branch m7medial branch of d.r.lateral cutaneous branch m8dorsal ramuslateral branch of d.r. m9spinal nerveventral ramus-1 m10ventral ramus-1ventral ramus-2 m11ventral ramus-1lateral cutaneous branch of v.r. m12cutaneous branch of v.r.posterior branch lateral m13cutaneous branch of v.r.anterior branch lateral

20. cse@buffalo November, 2003S. C. Shapiro20 Reasoning in Different Contexts  A context is a set of hypotheses and all propositions derived from them.  Reasoning is performed within a context.  A conclusion is available in every context that is a superset of its origin set.  Contradictions across contexts are not noticed.

21. cse@buffalo November, 2003S. C. Shapiro21 Darwin Context : set-context Darwin () : set-default-context Darwin wff1: all(x)(andor(0,1){mammal(x),fish(x)}) wff2: all(x)(fish(x) has(x,scales)) wff3: all(x)(orca(x) => whale(x)) wff4: all(x)(whale(x) => mammal(x)) wff7: free(Willy) and whale(Willy)

22. cse@buffalo November, 2003S. C. Shapiro22 Melville Context : set-context Melville (wff8 wff7 wff3 wff2 wff1) : set-default-context Melville wff9: all(x)(whale(x) => fish(x))

23. cse@buffalo November, 2003S. C. Shapiro23 Melville: Willy has scales : has(Willy, scales)? I infer fish(Willy) I infer has(Willy,scales) wff10: has(Willy,scales)

24. cse@buffalo November, 2003S. C. Shapiro24 Darwin: No scales : set-default-context Darwin : has(Willy, scales)? I infer mammal(Willy) I infer it is not the case that wff11: fish(Willy) I infer it is not the case that wff10: has(Willy,scales) wff15: ~has(Willy,scales)

25. cse@buffalo November, 2003S. C. Shapiro25 Summary  SNePS is useful for Bioinformatics reasoning.  Analogical/Propositional/DB Representation.  Notifies user about inconsistencies.  Resolves inconsistencies from differently credible sources.  Can reason in one context, even if another is inconsistent with it.