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Knowledge Representationand Reasoning

in SNePSfor 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/

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SNePS

A logic- and network-basedKnowledge representationReasoningand ActingSystem

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Examples of SNePSfor Bioinformatics

Neurological Diagnostic Expert SystemSystem for Understanding NF1 Literature

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NEUREX: A Neurological Diagnostic Expert System

Ca. 1983 – 1986See: 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.

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Strengths of SNePSfor This Task

Integrated Analogical, Propositional, Functional Knowledge

Access to Procedural Knowledge via Acting System

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Analogical/PropositionalRepresentation

Proposition,

Ventral root is proximal to spinal nerve.

is represented by{<p “ventral root”> <d “spinal nerve”>}

also have{<p “ganglion”> <d “spinal nerve”>}

{<p “spinal nerve”> <d “dorsal ramus”>}

{<p “spinal nerve”> <d “ventral ramus-1”>}

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alo 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.

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A Knowledge-Based Approach to Understanding

the NF1 LiteratureA 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

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Goals

Discover new linkages in information across Medline abstracts of research literature

about Neurofibromatosis 1(NF1, or von Recklinghausen disease)

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Strengths of SNePSfor This Task

Inconsistency Tolerance/Discovery/RepairContextsMultiple SourcesAmenable to Backend DB Storage

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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))

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Finding the ContradictionDuring Query Answering

: has(Willy, scales)?

I infer fish(Willy)

I infer it is not the case that

wff23: fish(Willy)

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BR AdviceIn 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.

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Continuing with Repaired KB

I infer it is not the case that

wff22: has(Willy,scales)

wff26: ~has(Willy,scales)

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Multiple Sourceswff1: 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.

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: 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

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Using Source CredibilityA contradiction was detected within context default-defaultct.The contradiction involves the newly derived proposition:

wff17: ~fish(Willy) {<der,{wff1,wff6,wff11}>}

and the previously existing proposition: wff14: fish(Willy) {<der,{wff4,wff11}>}

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)

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Backend DB Storage

A proposalAll propositions represented with keyword

argumentsEach keyword set a Relation in an RDBOr in XML

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aloNEUREX Data as RelationP Proximal Distalm1 dorsal root ganglionm2 ganglion spinal nervem3 ventral root spinal nervem4 spinal nerve dorsal ramusm5 dorsal ramus medial branch of d.r.m6 medial branch of d.r. medial cutaneous branchm7 medial branch of d.r. lateral cutaneous branchm8 dorsal ramus lateral branch of d.r.m9 spinal nerve ventral ramus-1m10 ventral ramus-1 ventral ramus-2m11 ventral ramus-1 lateral cutaneous branch of v.r.m12 cutaneous branch of v.r. posterior branch lateralm13 cutaneous branch of v.r. anterior branch lateral

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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.

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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)

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Melville Context

: set-context Melville (wff8 wff7 wff3 wff2 wff1)

: set-default-context Melville

wff9: all(x)(whale(x) => fish(x))

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Melville: Willy has scales: has(Willy, scales)?

I infer fish(Willy)

I infer has(Willy,scales)

wff10: has(Willy,scales)

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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)

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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.