ANSWERING CONTROLLED NATURAL LANGUAGE QUERIES USING ANSWER SET PROGRAMMING Syeed Ibn Faiz

Outline  Overview  Controlled Natural Language  Answer Set Programming  Transforming Queries into Programs  Producing Answers  Related Works  Conclusion

Overview  People need to write queries to extract information from biomedical ontologies.  Problem: Formal query languages are not suitable for many of them.  Solution: Natural language query?  Further problem: Ambiguities, Complexities.  Solution: Controlled natural language!

How will it work?  Controlled natural language is unambiguous.  Therefore, a query can be easily and unambiguously translated into a logical form.  Then one can do reasoning with it! Which drug cures Asthma? which_drug(A) ← drug_cure_disease(A,asthma). Formoterol

Controlled Natural Language  Subset of a natural language.  It has a restricted grammar and vocabulary.  It overcomes the ambiguity and complexity of natural language.  Example:  Attempto Controlled English (University of Zurich)

Attempto Controlled English (ACE)  Subset of standard English with a restricted syntax and restricted semantics described by a small set of  Construction rules – Grammar  Interpretation rules – remove ambiguities A customer who enters a card manually types a code.  APE translates ACE text into DRS.

Discourse Representation Structure (DRS)  DRS derived from ACE text is returned as:  drs ( Domain, Conditions)  Uses a fixed number of predefined predicates:  object, predicate, property, relation, modifier_pp, modifier_adv, query.  An example:  A man is mortal.

DRS Contd.  Query: What are the symptoms of the diseases that are related to ADRB1 or that are treated by Epinephrine?

Answer Set Programming (ASP)  ASP is a form of declarative programming oriented towards difficult search problems  In ASP a problem is posed as a logic program and solution is computed by its model or answer set.  It allows to automate reasoning with incomplete information.  Answer set solvers: Smodel, Clasp, DLV etc.

An Example  ide_drive :- hard_drive, not scsi_drive.  scsi_drive :- hard-drive, not ide_drive.  scsi_controller :- scsi_drive.  hard_drive.  M1 = {hard_drive, ide_drive}  M2 = {hard_drive, scsi_drive, scsi_controller}

Converting Query to Program  Three step process  Obtaining DRS produced by APE  Parsing DRS  Generating answer set program

Obtaining DRS  Using APE Webservice  URL:  Example: +man+is+mortal.&solo=drs +man+is+mortal.&solo=drs  Running APE locally

Parsing DRS  Grammar for DRS:  DRS drs( Domain, Conditions )  Domain [] | [ Referent {,Referent}* ]  Conditions [ Condition {,Condition}* ]  Condition Predicate | ComplexStructure  Predicate Object | Property | Relation | Predicate  | Modifier_pp | Modifier_adv | Query  ComplexStructure Question | Negation | Disjunction .....  A Recursive Descent Parser DRSParser Internal Structure

Generating Answer Set Programs  A program consists of rules.  A rule has two parts:  Head :- Body  Generating rules:  Constructing Head atom  Constructing Bodies

Constructing Head Atom  Which drug cures Asthma?  query(A,which)  object(A,drug,countabl e,na,eq,1)  Which_drug(A)  What is the drug that cures Asthma?  query(A,what)  predicate(B,be,A,C)  object(C,drug,countabl e,na,eq,1)  What_be_drug(C) Which QueryWhat Query

Generating Bodies What are the symptoms of the diseases THAT Are related to ADRB1 OR that are treated by Epinephrine?

Generating Bodies Contd.  Depth First Traversal of internal DRS representation  Generate a new body for each leaf  Add an atom to the body for  Each predicate-predicate predicate(D,cure,C,named(Asthma)) drug_cure_disease(C, asthma)  Each relation-predicate relation(A,of,B) Symptom_of_disease(A, B)

Examples  What are the symptoms of the diseases that are related to ADRB1 or that are treated by Epinephrine?  what_be_symptom(C) :- symptom_of_disease(C,D), disease_be_related_to_gene(D,adrb1)  what_be_symptom(C) :- symptom_of_disease(C,D), drug_treat_disease(epinephrine,D)  Which gene is related to a disease that causes Insomnia?  which_gene(A) :- disease_cause_symptom(B,insomnia), gene_be_related_to_disease(A,B)

Producing Answers  We need biomedical knowledge  Knowledge must be encoded  Answer Set Solver  Clasp  We need an interface.

Biomedical Knowledge  Concepts  Gene  Drug  Disease  Symptom  PharmGKB database: Relationships between gene, drug and disease.  MedicineNet.com: Disease and symptom database.

Encoding Knowledge  Facts:  disease_symptom(asthma,cough).  gene_disease(adra1b,asthma).  gene_drug(adra1b,norepinephrine).  drug_disease(norepinephrine,hypertension).  …..  Rules:  drug_symptom(X,Y) :- drug_disease(X,Z), disease_symptom(Z, Y).

System Architecture User Interface Query Pre- processor APE Parser DRS Translator Post Processing Clasp Interface Clasp User Interface

Post Processing  disease_be_related_to_ge ne(D,adrb1)  drug_treat_disease(epine phrine,D)  disease_cause_symptom(B, insomnia)  gene_disease(adrb1,D)  drug_disease(epinephrine,D)  disease_symptom(B,insomn ia) BeforeAfters

Related Works  A preliminary report on answering complex queries related to drug discovery using answer set programming by Oliver Bodenreider et al.  Transforming Controlled Natural Language Biomedical Queries into Answer Set Programs by Esra Erdem et al.

Conclusion  Limitations  Data  Language  Future Directions

Questions?

Thank You!