NEDA ALIPANAH MARIA ADELA GRANDO DBMI nalipanahucsd edu

NEDA ALIPANAH, MARIA ADELA GRANDO DBMI nalipanah@ucsd. edu mgrando@ucsd. edu 11/19/2012

Outline of the talk Use case scenario: Informed Consent Examples of Querying and Reasoning on Ontologies : Semantic Web Rule Language (SWRL) SPARQL Criteria to choose a reasoning language Conclusions

Use Case: Informed Consent Communication process between a patient and investigator that ultimately results in the patient’s agreement to participate in a research study. The process includes documents that the patient must sign to acknowledge the discussion on the reason for the research study, risks, benefits, alternatives, options to withdraw, etc. It constitutes a critical decision-making process: It is crucial that patients are engage in the decision of participating in the study, and understand the consequences.

Research Involving Specimens Because of the developments in genetics and genomics biological specimens (biospecimens) and clinical data for research are currently in high demand, they can be very limited in availability. This concern has boosted the creation of clinical data warehouses and biobanks. Subject’s will to share his biospecimens and clinical data to biobanks and warehouses is expressed through informed consent

Informed Consent Management Vision Permission I authorize U to perform operation O over my data D or sample S under certain constraints C Healthcare Institution Clinical Data Warehouse Permission Repository Bio. Sample Repository Patient I Resource Mediator I consent to share my blood samples for future cancer research Query User U requests data D and sample S to perform operation O on subjects like I under constraints C Results User U receives data D and sample S in compliance with subject’s permission Can I access blood samples from patients with breast cancer for cancer research? Research Institution User U

Permission Ontology A <subject> has <permission> or <obligation> to perform an <operation> over <biological specimens> or <medical records> under constraints

Permission Ontology- Data Repository

Resource Mediator for Cancer Center Biorepository I am a Stanford researcher, can I access laboratory tests and frozen blood samples from patients with ovarian cancer? Clinical Data Warehouse Permission Repository MCC Bio. Sample Repository Reasoning Engine Resource Mediator Interface Healthcare Institution Query User U requests data D and sample S to perform operation O on subjects like I under constraints C Results User U receives data D and sample S in compliance with subject’s permission Research Institution User U

Resource Mediator Interface Query User U requests data D and sample S to perform operation O on subjects like I under constraints C I am a Stanford researcher, can I access laboratory tests and frozen blood samples from patients with ovarian cancer?

Resource Mediator Reasoning Engine I am a Stanford researcher, can I access laboratory tests and frozen blood samples from patients with ovarian cancer? 1. Look for the patients with breast cancer diagnosis who have signed IC documents authorizing sharing his/her blood samples and laboratory tests 2. Check if that the requested resources can be shared in compliance with the patient’s signed consent. Permission I authorize U to perform operation O over my data D or sample S under certain constraints C

Resource Mediator Reasoning Engine I am a Stanford researcher, can I access laboratory tests and frozen blood samples from patients with ovarian cancer? Check that the MCC biorepository and clinical data warehouse has available the requested clinical record entries and biosamples in the requested preservation state.

Where in the Layered Architecture? Resource Mediator Rules/Query SPARQL SWRL RDF/OWL Ontologies XML/XML Schemas URI/UNICODE Other Services

SWRL Resource Mediator I am a Stanford researcher, can I access laboratory tests and frozen blood samples from patients with ovarian cancer? Clinical Data Warehouse Permission Repository MCC Bio. Sample Repository SWRL Reasoning Engine Resource Mediator Interface Healthcare Institution Query User U requests data D and sample S to perform operation O on subjects like I under constraints C Results User U receives data D and sample S in compliance with subject’s permission Research Institution User U

SWRL: Inference in OWL Semantic Web Rule Language (SWRL) Intended for rule-based reasoning in the Semantic Web SWRL is based on OWL: all rules are expressed in terms of OWL concepts (classes, properties, individuals, …) Provides intuitive, easy-to-read specifications Supported by Protégé Requires a reasoner, for instance Jess reasoner

SWRL: Inference in OWL SWRL rules have the form antecedent -> consequent where: both antecedent and consequent are conjunctions of atoms written a 1 ∧. . . ∧ a n, variables are indicated using the standard convention of prefixing them with a question mark (e. g. , ? x). Example: Person(? p) ^ has. Sibling(? p, ? s) ^ Man(? s) -> has. Brother(? p, ? s) How would you define has. Uncle(? x, ? y)?

SWRL Query Example Which informed consents grant me access to deidentified blood samples? Research Institution User U

SWRL Query Example Which informed consents grant me access to deidentified blood samples?

SWRL Query Example has. Policy(? Inf. Consent, ? ppolicy) ^ Permission(? ppolicy) ^ can. Perform. Operation(? ppolicy, ? operation) ^ Share(? operation) ^ Blood. Sample(? sample) ^ operates. On(? operation, ? sample) ^ is. Deintified. Data(? sample, 1) -> sqwrl: select. Distinct(? Inf. Consent)

SPARQL: Inference on RDF/RDFS SPARQL Query on Linked Data Directly (Graph) Use Triple Patterns in the Graph SPARQL Syntax Select Command CONSTRUCT Command (for reasoning ) OPTIONAL Command

SPARQL Query • Ontology Triple Pattern <http: //www. owlontologies. com/Ontology 1331852106. owl#Patient. Na me_6> <http: //www. w 3. org/1999/02/22 -rdf-syntax-ns#type> <http: //www. owlontologies. com/Ontology 1331852106. owl#Patient. Na me>

Simple SPARQL Query • Single Triple Matching (Use the Select Structure of SQL) Select Variable name (? a) Where {Subject Predicate ? a} • Multiple Triple Matching Select Variable name (? a), (? b) Where {Subject 1 Predicate 1 ? a. Subject 2 Predicate 2 ? b }

SPARQL Query • SPARQL Triple Pattern Different parts of triple patterns can be variables. SELECT DISTINCT ? a WHERE { ? a rdf: type <http: //www. owlontologies. com/Ontology 1331852106. owl#Patient. Na me>. } LIMIT 32

SPARQL for Inferencing What is inferencing (Reasoning)? An inference is the creation of a fact from existing facts. In RDF, this means adding a triple. : Patient 6 rdf: type : Patient rdfs: sub. Class. Of : Person Example of Inferencing in SPARQL Subclass (Subsumption) Inference Transitive Inference

Subclass Inferencing Subclass Inference (Subsumption) : Patient 6 rdf: type : Person � I. e. all members of the subclass are also members of the superclass. Command SPARQL CONSTRUCT for adding triples: Returns a graph (set of triples) that is the result of applying the CONSTRUCT graph pattern to each match in the WHERE clause. CONSTRUCT {? rsc rdf: type : Person} WHERE { ? rsc rdf: type : Patient. . Patient rdfs: sub. Class. Of : Person. }

Subclass Inferencing CONSTRUCT {? rsc rdf: type : Person} WHERE { ? rsc rdf: type : Patient. . Patient rdfs: sub. Class. Of : Person. } Check the Triples with Where clause Condition. ? rsc rdf: type : Patient rdfs: sub. Class. Of : Person …then the CONSTRUCT query will return the relevant triple for them and add to the graph. {? rsc rdf: type : Person}

Transitive Inferencing Example PREFIX o: http: //www. owlontologies. com/Ontology 1331852106. owl SELECT ? policy WHERE { ? policy rdf: type o: Permission; OPTIONAL {? policy o: haspolicy ? n} Filter(? n="MCCInf. Consent") } Permission(? ppolicy) ^ has. Policy(? ppolicy, “MCCInf. Cons ent”)

Inference Result The results are returned in RDF graph form. It can be directly inserted into an existing graph. Following inference protocol, Add this to the inferred graph, Saved to the asserted graph.

Inference Result SPARQL 1. 1: Data directly asserted into your data through the INSERT/INSERT INTO syntax INSERT INTO <graph-name> {? x rdf: type ? c 2} WHERE { ? c 1 rdfs: sub. Classf ? c 2. ? x rdf: type ? c 1. }

SPARQL for Inferencing V SPARQL 1. 1: INSERT is part of the general SPARQL update syntax that includes DELETE/DELETE FROM. This means that one can do updates on existing triples. An example: MODIFY GRAPH <graph-name> INSERT – DELETE Syntax INSERT {? rsc ? someprop ? newvalue} DELETE {? rsc ? someprop ? value} WHERE { ? rsc ? someprop ? value. . ? newvalue. }

SPARQL vs. SQL SPARQL is to RDF is like SQL to relational models. Relational Model RDF Model (Linked data) Many of SQL commands have equivalent in SPARQL (e. g. OPTIONAL can be seen as a left-outer join).

SPARQL OPTIONAL COMMAND SPARQL has a the ability to query for data but not to fail query when that data does not exist. SELECT ? Patient-name ? Patient-age WHERE { } ? person has. First. Name ? Patient-name. OPTIONAL { ? person age ? Patient-age | name | age | ============ | "Becky Smith" | 23 | | "Sarah Jones" | | | "John Smith" | 25 | | "Matt Jones" | |

How to choose a reasoning language? What is happening in the Back-End? RDF vs. OWL Querying Application Level Rules/Queries SPARQL RDF/RDFS Ontologies SWRL OWL Ontologies

How to choose a reasoning language? Criteria Protégé SWRL SPARQL Ontology Type OWL RDF/RDFS Ease of Use/ Intuitiveness Easy Difficult User level Non-programmer Performance NP Complete Not good Good Flexibility to improve Performance Less Flexible More Flexible

Conclusion Need inference to get more information from ontologies. SWRL is upper level reasoning language on OWL Ontologies. SPARQL is lower level language for querying/reasoning RDF Ontologies. SWRL is more intuitive and SPARQL is more technical (no semantic).