Ontology Engineering Quality Modular architectures Reengineering and Design

Ontology Engineering Quality, Modular architectures, Reengineering, and Design patterns Aldo Gangemi Laboratory for Applied Ontology The Institute for Cognitive Science and Technologies National Research Council, Rome, Italy a. gangemi@istc. cnr. it

Summary § Use cases and competency questions (only some of them are detailed) § Quality of ontologies § Ontology engineering techniques (recap) § Modular architectures § Reengineering § Ontology design patterns SSSW, Cercedilla, 19 -24 July 2004 www. loa-cnr. it 2

Factual cases for ontologies § § § § § The red block is on the blue one My car has four (five) wheels Prof. Enrico Motta has trespassed a speed limit Last transaction by Mr. Blackspot is suspicious The monitored network had a serious breakdown Atlantic Enterprise II carries drifting longlines onboard Monna Lisa painting at the Louvre shows a mysteriously smiling woman The Aegyptian sphinx is not the same as the Greek one, but they result to be sometimes blended in Renaissance art ACME e-publishing workflow requires that the project manager is responsible for …, the author has the duty to … the assistant is obliged to … SSSW, Cercedilla, 19 -24 July 2004 www. loa-cnr. it 3

Competency questions wrt those facts § § § § § Is the yellow block on the blue one? And the green one? Is there any spare wheel that I can borrow? When, where and why has Enrico trespassed the limit? Why do you think Mr. Blackspot transaction is suspicious? Is it convenient to repair the network for the monitoring company? Are there any vessels from a nearby country in the marine area 50 N 060 W, which can fish a Thunnus alalunga stock through allowed techniques? Is it convenient for the monitoring company to repair the broken network? How to index the Monna Lisa painting at the Louvre? What’s the difference between (and examples of) respective shinges? Did the last ACME e-publishing procedure conform to the workflow? SSSW, Cercedilla, 19 -24 July 2004 www. loa-cnr. it 4

Quality in the ontology lifecycle

What is an ontology? (yet another!) § § § § § A Formal, Partial Specification of a Conceptualization Conceived by some Rational agent for some (good or bad) Reason, and made in order to Negotiate that conceptualization with Someone else, or to Reuse it. § Extended from Gruber 1993, Guarino 1998. SSSW, Cercedilla, 19 -24 July 2004 Logic Representation Meaning Cognition Embodiment Context Agreement Society Culture www. loa-cnr. it 6

Ontologies and KBs An axiomatic theory: the Blocks World Ontology (BWO) § § § Signature: {On, Block} Axioms: On(x, y) Block(x) Block(y) On(x, y) On(y, x) (On(x, y) On(y, z)) On(x, z) A model M in BWO: § On(red_block#1, blue_block#1) § On(green_block#1, red_block#1) § On(yellow_block#1, red_block#1) SSSW, Cercedilla, 19 -24 July 2004 www. loa-cnr. it 7

Toy blocks in M M SSSW, Cercedilla, 19 -24 July 2004 www. loa-cnr. it 8

Toy blocks in M M SSSW, Cercedilla, 19 -24 July 2004 www. loa-cnr. it 9

Quality dimensions of ontologies? Coverage (all the intended models) Precision (only the intended models) policy, economics, regulations, geography, sociology, … SSSW, Cercedilla, 19 -24 July 2004 Flexibility (relatedness of alternative intended models, scoping both inside and outside of a domain) contexts of many kinds … www. loa-cnr. it 10

Quality checking of BWO § Does BWO catch all the intended models § Is BWO so precise in order to exclude non-intended models (if any): • “Non-Vertical” On? • “Unconnected” On? § Is BWO flexible enough? • Can we specialize theory in order to make it more precise? • Can we represent supplementary entities (“surface”, “cable”)? • Can we encode alternative axioms for the same signature, in the same or a different theory? SSSW, Cercedilla, 19 -24 July 2004 www. loa-cnr. it 11

Beware overcommitment! BWO*: § Signature: {On, Constitution, Moves, Block, Substance, Atom, Baby, …} § Axioms: {!} SSSW, Cercedilla, 19 -24 July 2004 www. loa-cnr. it 12

Blowing off the SW bottleneck SSSW, Cercedilla, 19 -24 July 2004 www. loa-cnr. it 13

Recap on ontology engineering techniques § Extraction of patterns by machine learning or NLP (pull) § Generation of SW documents through Web Services-oriented pre-designed patterns (push) § Reuse of existing/legacy ontologies (pull/push) • Simple reuse (pull/push) • Standards for definite domains (push) • Building blocks (pull/push) § Re-engineering of metadata (pull) § Emergence from communities of interest (pull/push) SSSW, Cercedilla, 19 -24 July 2004 www. loa-cnr. it 14

Anyway … § From KE viewpoint, there is a need for - a priori or a posteriori: • Selection: how can ontologies survive and reproduce? • Quality: what expertise is addressed, and to what extent? • Modularity: is there a viable design methodology for ontology architectures? • Reusability: are there viable ontological components to reuse (e. g. as building blocks)? § Inherent needs for Semantic Web deployment? § All of them? SSSW, Cercedilla, 19 -24 July 2004 www. loa-cnr. it 15

Modular architectures Semantics of modules Stratification Foundational and core ontologies

Semantics of modules § Island namespaces • E. g. XML-OWL: local consistency must be ensured § Dependent modules • E. g. “uses” relation in Ontolingua or Loom: augmented local consistency must be ensured § Partially ordered modules • Precise semantics: global consistency must be ensured § Contexts • E. g. C-OWL: bridging axioms (local semantics) SSSW, Cercedilla, 19 -24 July 2004 www. loa-cnr. it 17

OWL-RDF (XML) namespaces ns 1: vehicles ns 2: traffic norms Merging of namespaces No global consistency required SSSW, Cercedilla, 19 -24 July 2004 www. loa-cnr. it 18

An ontology dependency graph uses ns 1: vehicles ns 2: traffic norms Merging of namespaces Consistency in ns 2 of used elements from ns 1 must be ensured SSSW, Cercedilla, 19 -24 July 2004 www. loa-cnr. it 19

An ontology library inherits from ns 1: vehicles ns 2: traffic norms Merging of namespaces Consistency of all elements from ns 1 must be ensured in ns 2 SSSW, Cercedilla, 19 -24 July 2004 www. loa-cnr. it 20

Where are the modules? CYC theory of flogistum conversational maxims mereology Word. Net “how to make a coffee” for dummies DOLCE Allen’s event calculus Italian Constitution the modal jazz harmony railway timetable RCC-8 UMLS Metathesaurus Roget’s thesaurus AAT thesaurus the glossary of nurses SSSW, Cercedilla, 19 -24 July 2004 www. loa-cnr. it 21

Stratification (qualified building blocks) § Stacking of modules in types (strata), with additional constraints over: • Coverage • Reusability • Axiomatization depth § An example SSSW, Cercedilla, 19 -24 July 2004 www. loa-cnr. it 22

Wonder. Web Foundational Ontologies Library (WFOL) § Reflects different commitments and purposes, rather than a single monolithic view. § A starting point for building new foundational or domain ontologies. § A reference point for easy and rigorous comparison among different ontological approaches. § A common framework for analyzing, harmonizing and integrating existing ontologies and metadata standards. SSSW, Cercedilla, 19 -24 July 2004 www. loa-cnr. it 23

The structure of the WFOL § Modules are organized along two dimensions: • visions, corresponding to basic ontological choices made • specificity, corresponding to the levels of generality/specific domains Choose Vision Mappings between Visions/Modules and Lexicons 4 D 3 D Top Choose Specificity Bank Law Formal Links Between Visions and Modules Single Module SSSW, Cercedilla, 19 -24 July 2004 www. loa-cnr. it Single Vision 24

Formal criteria (concepts) § individuals vs. concepts (Italy vs. Country) § 3 D vs 4 D (Italy at once vs. Italy as temporal worm) § unity vs. amount (Italy vs. soil of Italy) § regions vs. entities (Italy extension vs. territory of Italy) § descriptions and situations (a plan and its execution, a theory and its models, a norm and its cases) SSSW, Cercedilla, 19 -24 July 2004 www. loa-cnr. it 25

Formal criteria (relations) § symmetry (e. g. “connection” vs. “part”) § transitivity (e. g. “ancestor” vs. “father”) § immediacy vs. compositionality (e. g. “part” vs. “systemic component”) § intra-categoriality vs. inter-categoriality (e. g. “part” vs. “participation”) § schematic primitives • part, function, structure, participation, inherence, localization, succession, satisfaction … SSSW, Cercedilla, 19 -24 July 2004 www. loa-cnr. it 26

A toy example of stratification Foundational ontology (domain-independent) {Object, Process, Part, Time, Location, Representation, Plan, …} inherits from Core ontology (specific domain-independent) {Work of art, Painting technique, Author, Artistic period, Plastic art, Interpretation, …} inherits from Domain ontology {Sculpture, Restoration, Mythical being, Caryatid, Doric order, Armilla, Fresco, …} SSSW, Cercedilla, 19 -24 July 2004 www. loa-cnr. it 27

A realistic example of stratification Fishery domain ontologies – “roof” and “floors” Onto. Word. Net fragments – “posts” The “toyhouse” SSSW, Cercedilla, 19 -24 July 2004 Fishery core ontology – “walls” DOLCE foundational ontology – “ground” www. loa-cnr. it 28

Sample taxonomy within library Class(Moonfish#10 partial (Agrovoc) Class(Saltwater_Fishes#10 partial (Agrovoc) Class(Aquatic-Organism#3 partial (COF) Class(LIFE_FORM$ORGANISM$BEING$LIVING_THING#4 partial (ONTOWord. Net) Class(agentive-physical-object#1 partial (DOLCE) … SSSW, Cercedilla, 19 -24 July 2004 www. loa-cnr. it 29

An example of a foundational ontology: DOLCE § DOLCE: a Descriptive Ontology for Linguistic and Cognitive Engineering § Cognitive/Linguistic bias • Categories mirror cognition, common sense, and the lexical structure of natural language. • Categories as conceptual containers: no “deep” metaphysical implications wrt “true” reality. • No deep commitment on “intellectual economy” of the primitive notions: focus on the simplicity of the representation primitives. SSSW, Cercedilla, 19 -24 July 2004 www. loa-cnr. it 30

Basic Ontological Choices in DOLCE § § § Objects/Substances and Events/Processes are distinct categories linked by the relation of participation. Qualities inhere in Objects (Physical Qualities) or in Events (Temporal Qualities) and they corresponds to “individualized properties”, i. e. they inhere only in a specific entity, e. g. “the color of this court”, “the velocity of this serve”, etc. Each kind of Quality is associated to a Quality Space representing the space of the values that qualities can assume • • § § Quality Spaces are neither in time nor in space Different quality spaces associated to the same kind of Quality are admitted. Space and Time are specific quality spaces Different kinds of space and time are admitted. Different Objects or Events can be spatio-temporally co-localized: the relation of constitution is considered Rich axiomatization of classes and relations SSSW, Cercedilla, 19 -24 July 2004 www. loa-cnr. it 31

DOLCE+ top-level SSSW, Cercedilla, 19 -24 July 2004 www. loa-cnr. it 32

Reengineering legacy metadata A complete workflow

Ontologies by precision game(x) activity(x) athletic game(x) court game(x) athletic game(x) y. played_in(x, y) court(y) tennis(x) court game(x) double fault(x) y. part_of(x, y) tennis(y) game athletic game court game tennis outdoor game field game football tennis football game field game court game athletic game outdoor game Taxonomy Glossary Catalog game NT athletic game NT court game RT court NT tennis RT double fault Thesaurus Axiomatized theory DB/OO scheme Ontological precision: the ability to catch/provide all and only the intended meaning (for a logical theory, to be satisfied by intended models) SSSW, Cercedilla, 19 -24 July 2004 www. loa-cnr. it 34

Lifting and datamodel creation/integration SSSW, Cercedilla, 19 -24 July 2004 www. loa-cnr. it 35

Legacy aquaculture hierarchies from fishery terminology systems AQUACULTURE (AGROVOC) NT 1 fish culture NT 2 fish feeding NT 1 frog culture … rt agripisciculture rt aquaculture equipment … Fr aquaculture Es acuicultura AQUACULTURE (ASFA) NT Brackishwater aquaculture NT Freshwater aquaculture NT Marine aquaculture rt Aquaculture development rt Aquaculture economics rt Aquaculture engineering rt Aquaculture facilities SSSW, Cercedilla, 19 -24 July 2004 Biological entity (FIGIS) Taxonomic entity Major group Order Family Genus Species Capture species (filter) Aquaculture species (filter) Production species (filter) Tuna atlas spec SUBJECT (One. Fish) Aquaculture development Aquaculture economics @ Aquaculture planning www. loa-cnr. it 36

Heterogeneous datatypes in legacy FIGIS DTDs SSSW, Cercedilla, 19 -24 July 2004 www. loa-cnr. it 37

Sample data model analysis/conversion in FOS Term ≠ Concept Term String Concept = Class BT ≈ subsumption between classes RT ≈ top-level conceptual relation {Descriptors} = {Classes}, {Individuals} Individual Class Concept ≠ Subject/Topic/Domain SSSW, Cercedilla, 19 -24 July 2004 www. loa-cnr. it 38

Formalization and Core Ontology Creation SSSW, Cercedilla, 19 -24 July 2004 www. loa-cnr. it 39

Datamodel mapping and its effects on translation § agrovoc_schema: Descriptor • agrovoc: River • agrovoc: Amazon § owl: Class(agrovoc: River) § owl: Individual(agrovoc: Amazon(rdf: type agrovoc: River)) SSSW, Cercedilla, 19 -24 July 2004 www. loa-cnr. it 40

Datamodel mapping and required transformations § agrovoc: RT § agrovoc_schema: Descriptor • agrovoc: Fishing_vessel • agrovoc: Fishing_gear • agrovoc: Fishing_vessel, RT, Fishing_gear § Class(agrovoc: Fishing_vessel partial (restriction(agrovoc: RT some. Values. From(agrovoc: Fishing_gear)))) SSSW, Cercedilla, 19 -24 July 2004 www. loa-cnr. it 41

One fishery core ontology for techniques SSSW, Cercedilla, 19 -24 July 2004 www. loa-cnr. it 42

Modularization and alignment SSSW, Cercedilla, 19 -24 July 2004 www. loa-cnr. it 43

FOS library Fishery domain ontologies – “roof” and “floors” Onto. Word. Net fragments – “posts” The “toyhouse” SSSW, Cercedilla, 19 -24 July 2004 Fishery core ontology – “walls” DOLCE foundational ontology – “ground” www. loa-cnr. it 44

Alignment method § Does the class to be aligned (or a proximal one) exist in COF? if so, align it, e. g. : • Class(figis: Marine_fishes partial cof: Aquatic_organism) • § Does the class exist in Word. Net? if so, include the Onto. Word. Net fragment related to that class into the library, e. g. : • Class(figis: Aquatic_resource partial own: Asset) • § Otherwise: Do the experts think the class is very relevant for fishery, or the class has a rich taxonomy under it? if so, create a small taxonomy that sketches a new core ontology for that domain, e. g. by adapting them from Onto. Word. Net (e. g. vehicles, equipments) or from existing ones (biomedicine) • § Otherwise: Align the class directly under a generic class in the foundational layer, e. g. : • Class(figis: Country partial dolce+: Political_Geographic_Object). SSSW, Cercedilla, 19 -24 July 2004 www. loa-cnr. it 45

Minimal case of alignment foundational dolce+: physical_object core cof: Fishing_vessel domain asfa: Trawlers = rdfs: sub. Class. Of SSSW, Cercedilla, 19 -24 July 2004 www. loa-cnr. it 46

Sample taxonomy within library Class(Moonfish#10 partial (Agrovoc) Class(Saltwater_Fishes#10 partial (Agrovoc) Class(Aquatic-Organism#3 partial (COF) Class(LIFE_FORM$ORGANISM$BEING$LIVING_THING#4 partial (ONTOWord. Net) Class(agentive-physical-object#1 partial (DOLCE) … SSSW, Cercedilla, 19 -24 July 2004 www. loa-cnr. it 47

Consistency issues § From ASFA formalization, we get that: • Class(asfa: Trap_fishing partial asfa: Catching_methods) • Class(asfa: Trap_fishing partial asfa: Fishing) § From the alignment, we know that (transitively): • Class(asfa: Catching_methods partial dolce+: Object), while Class(asfa: Fishing partial dolce+: Activity) § And from DOLCE+ we know that: • (disjoint. Classes dolce+: Object dolce+: Activity) § Hence the intersection: • Class(asfa: Trap_fishing partial (intersection asfa: Catching_methods asfa: Fishing)) § is inconsistent SSSW, Cercedilla, 19 -24 July 2004 www. loa-cnr. it 48

BT polysemy issues § From AGROVOC we know: • § From a biomedical ontology(ON 9. 3) we know: • • • § Class(agrovoc: Blood_Cells partial on 9: Cellular_structure) Class(agrovoc: Blood partial on 9: Tissue) (Disjoint. Classes on 9: Tissue on 9: Cellular_structure) Class(on 9: Tissue partial (restriction(dolce+: constituent some. Values. From(on 9: Cellular_structure)))) Therefore, on the basis of ON 9, we can suggest that • § Class(agrovoc: Blood_Cells partial agrovoc: Blood) is a polysemous use of the BT relations (a cellular structure cannot be a tissue, because the two classes are disjoint), therefore it is possibly the case that: • • Class(agrovoc: Blood_Cells partial (restriction(dolce+: constituent some. Values. From(agrovoc: Blood)))) SSSW, Cercedilla, 19 -24 July 2004 www. loa-cnr. it 49

Refinement, annotation, and merging SSSW, Cercedilla, 19 -24 July 2004 www. loa-cnr. it 50

Re. Learning procedure § Given RT relations and axioms from reference ontologies: • Class(asfa: Trawlers partial (restriction(asfa: RT some. Values. From(asfa: Pelagic_fisheries)))) • Class(own: Instrumentality partial (restriction(dolce+instrument_for some. Values. From(dolce+: Activity)))) § and we know transitively that: • Class(asfa: Trawlers partial own: Instrumentality) • Class(asfa: Pelagic_fisheries partial cof: Fishing_activity) § then we can infer that: • Class(asfa: Trawlers partial (restriction(dolce+instrument_for some. Values. From(asfa: Pelagic_fisheries)))) SSSW, Cercedilla, 19 -24 July 2004 www. loa-cnr. it 51

Annotations § § Domain lexicon (synonyms and multilingual labels) Subject trees (topics) Identification codes (unique identitifers) Comments (glosses, scope notes, examples, etc. ) SSSW, Cercedilla, 19 -24 July 2004 www. loa-cnr. it 52

Merging § Homonymic merging: • • agrovoc: Trawlers figis: Trawlers asfa: Trawlers fos_vessel: Trawler § Heterogeneous taxonomic position (emergent polysemy): • Class(agrovoc: Dredgers partial agrovoc: Ships) • Class(asfa: Dredgers partial asfa: Work_platforms) § Expert’s merging (emergent synonymy): • asfa: Ships • figis: Non-fishing_vessels SSSW, Cercedilla, 19 -24 July 2004 www. loa-cnr. it 53

Post-processing lifecycle SSSW, Cercedilla, 19 -24 July 2004 www. loa-cnr. it 54

Query expansion and brokering to distributed document management systems SSSW, Cercedilla, 19 -24 July 2004 www. loa-cnr. it 55

Fishery KBase through the Ontosaurus ontology server SSSW, Cercedilla, 19 -24 July 2004 www. loa-cnr. it 56

Mock-up for querying distributed dynamic data "tell me what vessels from a nearby country are currently in the marine area 50 N 060 W within Atlantic Ocean, provided that also some Thunnus alalunga stock can be fished by those vessels, through allowed techniques" (retrieve ? ves (exists (? type ? tech ? spec ? cou ? stock) (and (instance-of ? ves ? type) (superrelations ? type Fishing-Vessel) (superrelations ? tech Fishing-Technique) (has-depend-ons ? type ? tech) (has-depend-ons ? tech ? spec) (subrelations ? spec |Thunnus alalunga|) (instance-of ? cou Country) (instance-of ? stock Aquatic-Stock) (FLAG-STATE ? ves ? cou) (WEAK-CONNECTION ? cou |Atlantic Ocean@fig|) (OCEANIC-POSITION ? stock |50 N 060 W@fig|) (OCEANIC-POSITION ? ves |50 N 060 W@fig|) (fail (has-depend-ons ? tech |Drifting longlines@fig|)) (RESOURCE-SPECIES ? stock |Thunnus alalunga|)))) Blue: core concept types Pale blue: domain concept types Red: individuals Dark red: individual(s) found -> (|i||Atlantic Enterprise II|) SSSW, Cercedilla, 19 -24 July 2004 www. loa-cnr. it 57

Ontology Design Patterns: the route to practical, well-founded ontologies What is an ODP Part Space-time Roles and tasks Information objects

Related projects § FP 5 Project Wonder. Web: Ontology Infrastructure for the Semantic Web: http: //wonderweb. semanticweb. org • Languages, tools, foundational ontologies, reengineering methods § W 3 C Semantic Web Best Practices and Deployment Working Group: http: //www. w 3. org/2001/sw/Best. Practices/ • Ontology engineering design patterns, metadata reengineering § FP 6 Project Metokis: Methodology and Tool Infrastructure for the Creation of Knowledge Units: http: //metokis. salzburgresearch. at • Ontological engineering for task description and content description (workflows, content analysis and filtering, etc. ). Leveraging also on FP 5 projects Cultos and Inkass SSSW, Cercedilla, 19 -24 July 2004 www. loa-cnr. it 59

Sample Generic Use Cases § § § who does what, when and where? what are the parts of sth? what is it made of? what’s the place of sth? what’s the time frame of sth? how can you do what you do? does my behaviour conform to that rule? what’s the function of that artifact? how is it built? how did that phenomenon happen? what’s your role in that affair? is my scheduling compatible with yours? SSSW, Cercedilla, 19 -24 July 2004 www. loa-cnr. it 60

Related work on design patterns § Theoretical architecture: C. Alexander, The Timeless way of building, 1979 § Software engineering: E. Gamma et al. , Elements of reusable OO software, 1994 • D. Maplesden et al. , Design Pattern Modelling Language • N. Baker et al. , Meta-modelling patterns (-> “descriptive models”) § Ontology engineering (very recent interest): • G. Guizzardi et al. , LINGO: foundation for meta-environments, domain engineering in requirement analysis (bridging OE and OO) • J. R. Reich, patterns of ontology constructs, applied to Mbiology • M. Klein: ontology change/versioning patterns • G. Schreiber: started committee on DPs in OE • ODPs here: focus on the architecture of the content, rather than on the architecture of the logical form that represents that content SSSW, Cercedilla, 19 -24 July 2004 www. loa-cnr. it 61

Features of ontology design patterns § § § Any ontology is a potential domain design pattern, since it can instantiate a set of states of affairs. An ODP focuses on specialization, rather than instantiation An ODP is similar to top-level or foundational ontologies, but it has certain additional properties: 1. 2. 3. 4. 5. 6. 7. 8. 9. An ODP is a template for solving a domain modelling problem An ODP "extracts" a fragment of a TLO or FO, which is its "background” An ODP is axiomatized according to the fragment it extracts An ODP can be represented in any ontology representation language, although its intuitive and compact visualization seems an essential requirement An ODP can be an element in a partial order, where the ordering relation requires that at least one of the classes or relations in the ODP are specialized An ODP can be intuitively exemplified and catches relevant "core" notions of domains An ODP can be often built from informal or simplified schemes used by domain experts, together with the support of a foundational ontology and a methodology for domain ontology analysis, or by specializing existing ODPs An ODP can/should be used to describe a "best practice" of modelling An ODP is similar to a DB schema, but an ODP is defined wrt a foundational ontology and should have a general character, independently from local design details SSSW, Cercedilla, 19 -24 July 2004 www. loa-cnr. it 62

This talk: using UML class diagrams § Non-standard use of UML to visualize ODPs: • • generalisation -> subsumption (“sub. Class. Of”) association -> two-way conceptual relation (“property”) attribute -> one-way conceptual relation (“property”) assuming reasoning capabilities with classification and role chaining • association with no cardinality: 0. . * SSSW, Cercedilla, 19 -24 July 2004 www. loa-cnr. it 63

Basic DOLCE pattern (participant match#1 {Enrico, Aldo}) (located {Enrico, Aldo} Milton_Keynes) (located match#1 2004_Jan 18_1: 002: 00 GMT) (part match#1 ace#12) SSSW, Cercedilla, 19 -24 July 2004 www. loa-cnr. it 64

Time intervals pattern MTA(x, y) (Mereo. Topological Association) =df weekly connected(x, y) strongly connected(x, y) part(x, y) overlaps(x, y) successor(x, y)) deducible (sufficient conditions) [(located match#1 2004_Jan 18_12: 0013: 00 GMT) (located match#2 2004_Jan 23_12: 0013: 00 CET) (successor 2004_Jan 18_12: 0013: 00 GMT 2004_Jan 23_12: 0013: 00 CET)] (precedes match#1 match#2) SSSW, Cercedilla, 19 -24 July 2004 www. loa-cnr. it 65

Places pattern not deducible (only necessary conditions) (place ball#1 racquet#2) [(located ball#1_space) (located racquet#2_space) (weakly_connected ball#1_space racquet#2_space)] SSSW, Cercedilla, 19 -24 July 2004 www. loa-cnr. it 66

The D&S design pattern for roles, courses, and parameters This design pattern can be used to represent interpretations (rules, norms, plans, designs, histories, etc. ) of a same entity or a same set of related entities SSSW, Cercedilla, 19 -24 July 2004 www. loa-cnr. it 67

D&S specialization for norms A simple application: satisfiability of (being a case for) a traffic norm SSSW, Cercedilla, 19 -24 July 2004 [(legal_description traffic_norm#232) (legal_course driving_task) (legal_role driver) (legal_role vehicle) (legal_parameter speed_limit) (sequences driving_task driving#3283376400) (played_by driver Enrico) (played_by vehicle Lotus_Elise#M 186 YER) (valued_by speed_limit ≤ 60 mph) (speed_location driving#3283376400 130 mph)] [@(case driving_sit#666) (setting_for driving_sit#666 {driving#3283376400, Enrico, Lotus_Elise#M 186 YER, 130 mph})] www. loa-cnr. it 68

Normative scenario (descriptive layer) (regulative_description traffic_norm#232) [ (legal_course driving_task) Norm (legal_role driver) components (legal_role vehicle) (legal_parameter speed_limit) (requisite-for speed_limit driving_task) (functionally-depends-on vehicle driver) ] [ (sequences driving_task #x: Running&etc. ) Expected (played_by driver #y: Person&etc. ) ground (played_by vehicle #z: Car&etc. ) constraints (valued_by speed_limit #w: Speed&≤ 60 mph) (localization #x: Running&etc. #w: Speed&≤ 60 mph) … ] SSSW, Cercedilla, 19 -24 July 2004 www. loa-cnr. it 69

Case scenario (ground layer) [ @(case driving_sit#666) (setting_for driving_sit#666 {driving#3283376400, Enrico, Lotus_Elise#M 186 YER, 130 mph}) ] Case is established here by means of another descriptive component (@), e. g. a database schema, a sensing system, a temporal synchronization over possible fillers of norm components, etc. -> an aggregation component is always needed, be it even an automatic clustering (based on some predefined or emerging pattern, based on rules for pattern detection, …) -> constructive filtering bias SSSW, Cercedilla, 19 -24 July 2004 www. loa-cnr. it 70

Pairing normative and case scenarios Given that: [Running(driving#3283376400) Person(Enrico) Car(Lotus_Elise#M 186 YER) Speed(130 mph)] We can infer that: [(sequences driving_task driving#3283376400) (played_by driver Enrico) (played_by vehicle Lotus_Elise#M 186 YER) (speed_location driving#3283376400 130 mph)] ? ? ? SSSW, Cercedilla, 19 -24 July 2004 www. loa-cnr. it 71

Reasoning on D&S satisfaction Does driving_sit#666 satisfy traffic_norm#232? No, why? speed_limit cannot be valued by 130 mph because 130 mph is not within the allowed range: ≤ 60 mph SSSW, Cercedilla, 19 -24 July 2004 www. loa-cnr. it 72

A pattern for information objects SSSW, Cercedilla, 19 -24 July 2004 www. loa-cnr. it 73

Construction example: Sphinges SSSW, Cercedilla, 19 -24 July 2004 www. loa-cnr. it 74

How many (and which) sphinges? class individual Greek mythical creature word “sphinx” instance-of statue lexicalizes Sphinx metaphorical blending sphinx lexicalizes sphinx subclass-of Aegyptian mythical creature instance-of lexicalizes sphinx *refers_to instance-of English expressed_ according_to instance-of realizes refers_to Sphinx realized_by The Sphinx “Oedipus and the Sphinx” interpersonal role instance-of metaphorical blending plays_role symbolic figure pharaoh SSSW, Cercedilla, 19 -24 July 2004 * in naïve iconography plays_role www. loa-cnr. it monument stoney object to be restored 75

Which design patterns have been used? § Logical or metadata level ODPs: • • • § Individual(The_Sphinx type(Sphinx)) Class(Sphinx partial Statue) Class(Sphinx partial annotation(label “sphinx”)) Logical+Ontological ODPs: • • • Individual(The_Sphinx value(Realizes Sphinx_figure)) Class(Information_object partial restriction(some. Values. From Represents(Entity))) Class(Information_object partial restriction(all. Values. From Refers. To(Description))) Class(Physical_object partial restriction(all. Values. From Plays(Role))) Class(Entity partial restriction(all. Values. From Metaphoric. Blending(Entity))) SSSW, Cercedilla, 19 -24 July 2004 www. loa-cnr. it 76

Sphinx query Ex: query on semistructured data «tell me what works of art from ancient Egypt are related to European works of art that also involve Greek mythology in a same cultural unit» (retrieve (? x 1 ? x 2) (exists (? y ? z) (and (creation_3 ? x 1) (creation_3 ? x 2) (non-physical-object ? y) (entity ? z) (realizes ? x 1 ? y) (origin ? y EGYPTIAN_EMPIRE$EGYPT) (or (and (represents ? x 2 ? z) (origin ? z Classical_Greece)) (exists ? w 1 (and (non-physical-object ? w 1) (realizes ? x 2 ? w 1) (refers-to ? w 1 ? z) (origin ? w 1 EUROPE))))))) SSSW, Cercedilla, 19 -24 July 2004 www. loa-cnr. it 77

Considerations on domain ontology building § Developing precise domain ontologies is timeconsuming, and requires high competences § Not always is deep granularity required § Not always is full expressivity required § Our position: • “lightweight” is ok, but if we a have a level for sharing intuitions and to establish negotiation, it is much better • “local” precise ontologies are ok, but if we have a level to align and merge different local ones, it is much better • precision is “heavyweight”, but scalability is reachable by using good patterns and interfaces SSSW, Cercedilla, 19 -24 July 2004 www. loa-cnr. it 78