1 Aligning the Parasite Experiment Ontology and the Ontology for Biomedical Investigations Using AgreementMaker Valerie Cross, Cosmin Stroe Xueheng Hu, Pramit Silwal, Maryam Panahiazar, Isabel F. Cruz, Priti Parikh, Amit Sheth July 29, 2011 Buffalo NY

2 Outline  Task: Align PEO and OBI Ontologies  OAEI Investigation  AgreementMaker Overview  Enhancements to AgreementMaker  Experimental Results  Conclusions and Future Work

Parasite Experiment Ontology (PEO) models provenance metadata associated with experiment protocols used in parasite research. extends the upper-level Provenir ontology ( PEO (v 1.0) includes Proteome, Microarray, Gene Knockout, and Strain Creation experiment terms along with other terms that are used in pathway. 110 classes & 27 properties, uses concepts in Parasite Life Cycle ontology 3 Snapshot of PEO

Ontology for Biomedical Investigations (OBI) describes biological and clinical investigations. includes a set of 'universal' terms applicable across various biological and technological domains, and domain-specific terms relevant only to a given domain. support the consistent annotation of biomedical investigations, regardless of the particular field of study. represent the design of an investigation, the protocols and instrumentation used, the material used, the data generated and the type analysis performed on it. being built under the Basic Formal Ontology (BFO).(BFO) 4

Ontology Alignment Evaluation Initiative (OAEI) Annual international competition to evaluate ontology alignment techniques with multiple tracks  Benchmark tests  Biomedical track (Mouse and NCI Human Anatomies)  Conference track (15 ontologies) “Side effect” of the competition are published ontology sets consists of two ontologies and correct mappings as determined by experts Results measured by  Recall, precision, and F-measure (combines recall and precision)  Runtime  Other 5

OAEI Competition Researchers can participate with only one ontology matching technique (per track) Alignment technique must be fully automatic, it cannot be manual or semi-automatic Competition provides an API User interface not evaluated Results measured by  Recall, precision, and F-measure (combines recall and precision)  Runtime  Other 6

OA Quality Measures 7

OAEI

OAEI Benchmark Tests 9 ASMOV, RiMOM and AgrMaker, seem to perform these tests at the highest level of quality

OAEI Anatomy Track #1 The matcher has to be applied with its standard settings. #2 An alignment has to be generated that favors precision over recall. #3 An alignment has to be generated that favors recall over precision. #4 A partial reference alignment has to be used as additional input. 10

AgreementMaker - OA System Univ. of Illinois Chicago, ADVIS Lab, Dr. Isabel F. Cruz and Cosmin Stroe 11 Motivation Automatic methods are required to match large ontologies Several features of the ontologies have to be considered Users need to trust the mappings and to be directly involved in the loop System’s capabilities Wide range of matching methods Capability to smartly combine multiple strategies Multi-purpose user interface to allow evaluation and manual interaction with the matchings Extensible architecture to allow reuse and composition of the matching modules

Architecture of a Matcher 12

Existing Matchers 13

Existing Matchers First layer (conceptual)  BSM (Basic Similarity Matcher)  PSM (Parametric String-Based Matcher)  ASM (Advanced Similarity Matcher)  VMM (Vector-based Multi-term Matcher) Second layer (structural)  DSI Descendent Similarity Inheritance  SSC Sibling Similarity Contribution Third Layer (aggregation)  LWC Linear Weighted Combination 14

PSM 15

VMM 16

LWC 17

Lexicon Extensions to Matchers AgreementMaker version 0.22 extended these string-based matchers by integrating two lexicons (2010 OAEI):  the Ontology Lexicon, built from synonym and definition annotations existing in the ontologies themselves, and  the WordNet Lexicon, created by starting with the ontology lexicon and adding any non- duplicated synonyms/definitions found in WordNet Result: BSM lex, PSM lex, and VMM lex. 18

Initial Experiments AgreementMaker (ver. 0.22) with the OAEI 2010 anatomy configuration resulted in only two mappings Found inconsistency in entity descriptions of PEO and OBI.  Identifiers: PEO URIs use a textual fragment identifier ( owl#transfection), while OBI's entities use numerical identifiers (e.g., purl.obolibrary.org/obo/OBI_ ).  Labels: PEO's use of the rdfs:label field (on 19.1% of classes) does not follow the specification guidelines since it contains a PLO identifier. OBI uses the rdfs:label field to contain a descriptive string on almost 100% of its classes.  Comments: PEO uses on 99% of its classes and provides a definition. OBI only uses the comment field on about 4% of its classes.  Some common annotations exist between PEO and OBI BUT either PEO or OBI has low coverage OBI has high coverage for label annotations PEO has high coverage for comment annotations.  This heterogeneity and matchers matching the same annotations to each other (i.e., class ID with class ID, label with label, etc.) resulted in almost no alignment. 19

Annotation Property Coverage 20

Annotation Profiling allow the user to select and combine different annotations of the source or target ontology to be used in the alignment process. 21

Provenance Information Added 22

Customization of Lexicon Matchers The lexicon builders for BSM lex, PSM lex, and VMM lex lexicon use a fixed name for the synonym and definition annotations (hasSynonym and hasDefinition). Lexicon builder modified to exploit the synonym annotations in PEO and OBI by having the user choose the annotation names used to create the lexicons.  OBI does not use hasSynonym but uses IAO annotation properties IAO (“editor preferred term") and IAO (“alternative term") which serve the same function as synonyms for the OBI.  The PEO does not use synonyms but uses the comment annotation for a definition in most cases. Result: BSM lex+, PSM lex+, and VMM lex+. 23

Experts’ Mapping (Portion) Maryam & Priti 24

BioPortal Mappings p/Parasite_Experiment_ontology 25

Selection of OBI Sample Results 26 Those mapped by Knoesis using AgreementMaker Ontology Alignment system

Experimental Results 27

Overlapping of Matchers 28

Conclusions and Future Work Experimental results in the biomedical domain demonstrate the problem of heterogeneous annotations of ontologies. Validated past approach of extending matching algorithms using lexicons, showing the best results produced by matchers that use lexicons BSMl ex+  Investigate including more lexicons such as UMLS to achieve better result Heterogeneity managed by increasing the flexibility of state of the art matching algorithms, i.e.,, annotation profiling, mapping provenance information and custom lexicons which supports a domain expert in this process  relies on the user to select relevant annotations to be used in the matching process.  More work needs to be done specifically to automatically identify semantically compatible annotations by applying established ontology evaluation metrics Already have added a wide variety of semantic similarity measures to AgreementMaker for future use in semantic matching, not just lexical matching of concepts between ontologies. . 29

THANK YOU! QUESTIONS? 30