. Daniel Schober, Martin Boeker, University Medical Center Freiburg ‘Ontology Simplification’ Buzzword or real Need ? OBML 2010

. State of the Art Transition from taxonomies to description logics –Increasing formal semantics & expressivity OWL DL applied widely –W3C standard for ‚semantic web‘ –Multiple efforts with massive funding –Ontology libraries & best practice providers

. Problem DL ontologies rarely used in application settings –Used in.ac &.edu, but not in.com projects –Only small data sets exploited Competency questions and use cases usually retro-fitted DL aspects rarely exploited –Complex LEGO-style definitions –RL profile expressivity Cardinalities, disjoints, nested universal restrictions, …  Usability decreases as formality and expressive power increases  Usability reverse proportional to complexity

. Potential Reasons Inherent complexity of biodomain –Dealing with non-linear behaviors, non-classical physics –Far from sensory-perceptable world (‘meso-level’) Complexity of DL –Set theoretic approach counter-intuitive to object-orientation –Long class expressions, nested with multiple brackets –Hard to read Syntax Reasoners can’t cope with expressivity on larger scale –Computationally not feasible DL (semantics), OWL (syntax) ? … not really very robust Tools & editors ? …very far from being robust Ontologists can’t keep up with frequent changes Steep learning curve on engineering & usage side

. Hypothesis ‘Is advertising ‘simplifications’ a solution ?’ Increase end-user compliance Render DL ontologies human understandable … while not sacrificing reasoning capabilities Approach Collect and review existing simplifications –Check if whole approach feasible ? Introduce new simplification methods Create typology of simplification methods Raise general awareness Make methods accessible Test if they increase compliance

. ‚understandeable‘ ? Ontology is understandable if its RUs are understandable RU is understandable if … –it is traceable and readily applicable by the user –its intended meaning can be grasped in a short time This is the case if … –it is labeled in line with user-expectations –it is instantiated often easy map to everyday language constructs high every-day usage frequency –it resides in the ‘intuitive’ meso-level i.e. neither too abstract nor too special directly perceivable by human senses –it belongs to traceable and intelligible top-level category i.e. MaterialEntity vs. DependentContinuant –it has short logical definitions built from simple RUs themselves Understanding can be facilitated by tools –Using principles of software ergonomics –Implementing simplification and normalisation strategies

. Typology (naive start)

. 1.Syntax simplifications 2.Structural simplifications 3.Shortcuts and local approximate models 4.Views showing subsets of entities 5.Modularizations, partitions, slims 6.GUI simplifications / software ergonomics Some examples …

. Normalize syntax (1.a) Normalizing equivalent constructs into simpler forms –Syntactical complexity reduction E.g. via specialized language constructs E.g. for disjointness Simplify into

. Conflate redundancy in restrictions (1.b) Avoid redundancy in restrictions Simplify NeuralInflammation ⊑ Inflammation ∃ has-participant. CNS_Tissue ⊓ ∃ has-participant. PNS_Tissue ⊓ ∃ has-participant. Brain_Tissue into NeuralInflammation ⊑ ∃ has-participant. (CNS_Tissue ⊓ PNS_Tissue ⊓ Brain_Tissue)

. Increase human readability (1.c) Human readable syntax Omit logics-specific symbolism Simplify HeparinBiosynthesis ⊑ (HeparinMetabolism ⊓ (Biosynthesis ⊓ ∃ acts_on. Heparin)) into Manchester OWL Syntax HeparinBiosynthesis SubClassOf HeparinMetabolism SubClassOf (Biosynthesis AND acts_on SOME Heparin) or Attempto Controlled English (CNL) “Every HeparinBiosynthesis is a HeparinMetabolism. Every HeparinBiosynthesis is a Biosynthesis that acts_on a Heparin.”

. Simplify labels (2.c) Naming Conventions Shorten long relation names –“ Anatomic_Structure_Is_Physical_Part_Of ” Remove redundancy Simplify Ovary ⊑∃ Anatomic_Structure_Is_Physical_Part_Of. Reproductive_System into Ovary ⊑ ∃ Is_Physical_Part_Of. Reproductive_System ‘Anatomic_Structure’-prefix is already specified via domain of relation

. Conflate property chains (3.a) Use Shortcuts Property chains (OWL 2) allow shortening expressions –Compress two triples into one –Conflate / fold expression over 2 or more properties Simplify GeneA transcribed_to GeneA_mRNA GeneA_mRNA translated_to GeneA_Protein into GeneA_Protein product_of GeneA

. Simplified umbrella classes (3.b) Allows for graceful evolution through temporary proximity models –which can later be untangled seamlessly Goal model for diseases PathologicalDisposition ⊑ ∃ inheresIn. PathologicalStructure PathologicalDisposition ⊑ ∀ hasRealization. PathologicalProcess PathologicalProcess ⊑ ∃ hasParticipant. PathologicalStructure PathologicalProcess ⊑ ∃ realizationOf. PathologicalDisposition Pre-coordination is labor-intensive due to combinatorial explosion

. Simplified umbrella classes (3.b) A pragmatic proximity model can be introduced –Insert temporary umbrella class –ignoring disposition / structure / process distinction PathologicalEntity ≡ PathologicalStructure ⊔ PathologicalDisposition ⊔ PathologicalProcess Later gracefully evolve towards complex model All needed relations for … –Pathological Structures: part-of / located-in –Pathological Dispositions: inheres-in –Pathological Processes: has-participant / located-in … can be captured via one super-relation has-locus Allows connecting from any PathologicalEnity to relevant location –but without commitment to granularity –But still, the simplified model supports some inferences It can later be expanded –without rendering the simplification false

. Discussion Typology in early stage –Re-structure into polyhierarchy of disjoint orthogonal branches Potential sortals ordering simplifications –By entity tackled –By persistence –By life cycle kick-off, development, deployment/usage –By ergonomics (Wahrnehmungspsychologie) –By user role –By user background mathematician, computer scientist, logician, philosopher, linguist, biologist,

. Discussion Ease access to simplification methods Publish –OBO Foundry initiative –Ontology Engineering and Patterns Task Force (SWBPD-WG) –Ontology Design Pattern portals None currently addresses ‘simplifications’ Rather seen as properties of general design patterns Introduce special ‘simplification pattern type’ or add additional descriptor to existing pattern types ?

. Conclusions Reason for limited impact of OWL DL –Performance problems –Inherent complexity Complexity can be coped with by simplifications Collection of >30 reviewed simplification methods –Put in Typology –Collection and Typology to be expanded Cross-talk with ODP community Compare user compliance pre- and post-simplified –Test how fast two codes/ontologies lead to desired result for same test task Feedback appreciated

. Resources & Acknowledgements Resources Find Simplifications & reviews on Acknowledgements Martin Boeker Stefan Schulz Josef Ingenerf The DebugIT community

. Normalize syntax (1.a) E.g. for instance-assertions Simplify into

. Towards Simplification Methods Two types of simplifications 1.Removing complexity Prevents full exploitation of semantics Format transformation into OWL lite or SKOS 2.Hiding complexity Allows full exploitation of semantics Views and excerpts of ontologies Define characteristics for ‘simplicity’ and ‘understanding’ for following aspects –Individual cognitive abilities –Semantics & syntax –Software ergonomics

. Simplification Collection and Review

. Conflate redundancy in restrictions (1.b) Avoid redundancy in restrictions Frequent source of errors for inadequate modeling –E.g. below: each individual AdrenalineReceptor is simultaneously expressed in three different body parts Simplify AdrenalineReceptor ⊑ ∃ Gene_Product_Expressed_In_Tissue. Lung ⊓ ∃ Gene_Product_Expressed_In_Tissue. Brain ⊓ ∃ Gene_Product_Expressed_In_Tissue. Muscle into AdrenalineReceptor ⊑ ∀ Expressed_In. (Lung ⊔ Brain ⊔ Muscle)

. Conflate property chains (3.a) Use Shortcuts Property chains (OWL 2) allow shortening expressions –Compress two triples into one –Conflate and fold expression over 2 or more properties Simplify Pneumonia outcome_of LungInflammation LungInflammation treated_by Antibiotics  Pneumonia improved_by Antibiotics Tryptophan substrate_of IndolePhosphatase IndolePhosphatase has_product TrypthophanPhosphate A is_son_of B and B is_brother_of C into Tryptophan processed_to TrypthophanPhosphate A has_uncle C Two properties can be chained by a new property In particular views shortcuts increase understanding

. To investigate –What complexities can be automatically detected and be removed ? Parsers can unify / normalize and simplify syntax –‘Guided simplification finder’ chooses appropriate simplifications based on user requirements ?