seminarioOntologie

An Overview of Ontologies and their Practical Applications: 

An Overview of Ontologies and their Practical Applications Gianluca Correndo correndo@di.unito.it http://www.di.unito.it/~correndo

What is an Ontology?: 

What is an Ontology?

Ontology: 

Ontology Semantics – the meaning of meaning. Philosophical discipline, branch of philosophy that deals with the nature and the organisation of reality.

In Computer Science …: 

In Computer Science … An ontology is an explicit specification of a conceptualization [Gruber] Defines A common vocabulary of terms Some specification of the meaning of the terms A shared understanding for people and machines

Why develop an ontology?: 

Why develop an ontology? To make domain assumptions explicit Easier to change domain assumptions Easier to understand and update legacy data To separate domain knowledge from operational knowledge Re-use domain and operational knowledge separately A community reference for applications (standards) To share a consistent understanding of what information means

Communication: 

Communication

A Specification of a Conceptualization: 

A Specification of a Conceptualization Concepts (class, set, type, predicate) Event, gene,molecule, cat Properties of concepts and relationships between them (slot) Taxonomy: generalisation ordering among concepts isA, partOf, subProcess Relationship, role or attribute: functionOf, hasActivity location, eats, size

What is a concept?: 

What is a concept? Different communities have different notions on what a concept means: Formal concept analysis talk about formal concepts Description Logics talk about concept labels ISO-704:2000 – Terminology Work Often the classical notion of a frame in AI or a class in OO modeling is seen as equivalent to a concept.

An explicit description of a domain: 

An explicit description of a domain Constraints or axioms on properties and concepts: value: integer domain: cat cardinality: at most 1 range: 0 <= X <= 100 oligonucleotides < 20 base pairs cows are larger than dogs cats cannot eat only vegetation cats and dogs are disjoint Values or concrete domains integer, strings 20, tryptophan

An explicit description of a domain: 

An explicit description of a domain Individuals or Instances sulphur, trpA Gene, felix Nominals Concepts that cannot have instances Instances that are used in conceptual definitions ItalianDog = Dog bornIn Italy Instances An ontology = concepts + properties + axioms + values + nominals A knowledge base = ontology+instances

Light and Heavy expressivity: 

Light and Heavy expressivity Lightweight Concepts, atomic types Is-a hierarchy Relationships between concepts Heavyweight Metaclasses Type constraints on relations Cardinality constraints Taxonomy of relations Reified statements Axioms Semantic entailments Expressiveness Inference systems A matter of rigour and representational expressivity

Slide12: 

Regno Animalia Tipo Chordata   Classe Mammalia   Ordine Primates   Famiglia Hominidae   Genere Homo   Specie sapiens Carl von Linné (1707-1778) Aristotele (384 b.C. – 322 b.C. ) Science of Being (Metaphysics, IV,1) What is being? What are the features common to all beings?

So what is an ontology?: 

So what is an ontology?

…Things in Common: 

…Things in Common They are approaches to help structure, classify, model, and/or represent the concepts and relationships pertaining to some subject matter of interest to some community. They are intended to enable a community to come to agreement and to commit to use the same terms in the same way. The meaning of the terms is specified in some way and to some degree.

Thesauri: 

Example: Fruit Orange Apfelsine (german) Vegetable similarTo synonymWith NarrowerTerm Graph with labels edges (similar, nt, bt, synonym) Fixed set of edge labels (aka relations) Use of lexical stem no instances Well known in library science cf. terminologies / classifications (Dewey) Thesauri

WordNet: 

WordNet

UMLS (Unified Medical Language System) http://umlsks.nlm.nih.gov/: 

UMLS (Unified Medical Language System) http://umlsks.nlm.nih.gov/ National Library of Medicine (NLM) database of medical terminology. Terms from several medical databases (MEDLINE, SNOMED International, MeSH, etc.) are unified so that different terms are identified as the same medical concept. Metathesaurus provides the concordance of medical concepts: 730.000 concepts, 1.5 million concept names in different source vocabularies Specialist Lexicon provides word synonyms, derivations, lexical variants, and grammatical forms of words used in MetaThesaurus terms: 130.000 entries. Semantic Network codifies the relationships (e.g. causality, "is a", etc.) among medical terms: 134 semantic types, 54 relationships. Used for: patient data creation, curriculum analysis, natural language processing, and information retrieval

Slide22: 

DB UMLS Metathesaurus Information System

Slide23: 

UMLS Metathesaurus Information System 2 Information System 1

Formal Ontologies: 

Formal Ontologies

Frames, SDM, OO models: 

Frames, SDM, OO models Frames Rich set of language constructs: frames, slots, facets, defaults Impose restrictive constraints on how they are combined or used to define a class All frames asserted into taxonomy by hand All concepts are primitive Octet/GKB, Protégé, OCML, Ontolingua OKBC – Open Knowledge Base Connectivity OKBC – Lite OO / Semantic Data Models (EER, UML) Taxonomy/inheritance – semantics Intuitive, lots of tools, widely used

Frame Data Model: 

Frame Data Model Frames Classes: genes, reactions Instances: lr10 Relationships Slots: chromosome, map-position, citations, reactants, products, Keq Facets: chromosome is single-valued, instance of class chromosomes; Citations is multiple valued, set of strings

Description Logics: 

Description Logics A family of logic based knowledge representation formalisms Descendants of semantic networks and KL-ONE Describe domain in terms of concepts (set of individuals), roles (relationships) and individuals Distinguished by: Formal semantics (typically model theoretic) Decidable fragments of FOL Closely related to propositional modal & dynamic logics Provision of inference services Sound and complete decision procedures for key problems Implemented systems (highly optimised)

Description Logic Family: 

Description Logic Family DLs are a family of logic based KR formalisms Particular languages mainly characterised by: Set of constructors for building complex concepts and roles from simpler ones Set of axioms for asserting facts about concepts, roles and individuals ALC is the smallest DL that is propositionally closed Constructors include booleans (and, or, not), and Restrictions on role successors E.G., Concept describing “happy fathers” could be written: Man  hasChild.Female  hasChild.Male  hasChild.(Rich  happy)

DL Concept and Role Constructors: 

DL Concept and Role Constructors Range of other constructors found in DLs, including: Number restrictions (cardinality constraints) on roles, e.g., 3 hasChild, 1 hasMother Qualified number restrictions, e.g., 2 hasChild.Female, 1 hasParent.Male Nominals (singleton concepts), e.g., {Italy} Concrete domains (datatypes), e.g., hasAge.(21), earns spends.< Inverse roles, e.g., hasChild– (hasParent) Transitive roles, e.g., hasChild* (descendant) Role composition, e.g., hasParent o hasBrother (uncle)

What’s in a “Logic based ontology”?: 

What’s in a “Logic based ontology”? Primitive concepts - in a hierarchy Described but not defined Properties - relations between concepts, also in a hierarchy Constructors – on concepts and properties “Some”, “only”, “at least”, “at most”, and, or, not Defined concepts Made from primitive concepts, constructors and descriptors Enzyme  protein and catalyses reaction Reason that enzyme is a kind of protein “Is-kind-of” = “implies” “Dog is a kind of wolf” mean “all dogs are wolves” Axioms disjointness, further description of defined concepts A Reasoner To organise it for you. Consistency & taxonomy for defined concepts established though logical reasoning

Reasoning support in DL : 

Reasoning support in DL Consistency — check if knowledge is meaningful Subsumption — structure knowledge, compute taxonomy Equivalence — check if two classes denote same set of instances Instantiation — check if individual i instance of class C Retrieval — retrieve set of individuals that instantiate C Problems all reducible to consistency (satisfiability): FACT, racer, cerebra

Pratical Session: 

Pratical Session

Pratical Session: 

Pratical Session

Formal Ontology Applications: 

Formal Ontology Applications

Formal Ontology Applications: 

Formal Ontology Applications Ontology engineering support Semantic web Intelligent information retrieval E-Commerce Intelligent web-services Agent technologies

Problems with Information Retrieval: 

Problems with Information Retrieval Working with the Web is currently done at a very low level: Clicking on links and using keyword search for links is the main (if not only) navigation technique Keyword-based search engines (Alta Vista, Infoseek, Yahoo, MetaCrawler, Google)

Problems with Information Retrieval: 

Problems with Information Retrieval Main burden of information retrieval is that it is only information retrieval. It helps to retrieve information sources but the human user has to manually extract and interpret the information. Information presentation and maintenance is not supported.

Semantic Web Vision: 

Semantic Web Vision Express explicitly a high level description of resources accessible via Web More processable data availabe Information more directly available Enabling intelligent Web features

DAML-S: Ontology language: 

DAML-S: Ontology language Build upon the well-defined semantics of DAML+OIL Is expected to provide a common understanding of the semantic in a web-service By specifing an ”Upper Ontology for Services”

An Upper Ontology for Services: 

An Upper Ontology for Services Three essential types of knowledge about a service, each characterized by the question it answers: What does the service require of the user(s),and provide for them? How does it work? How is it used?

Backup Slides: 

Backup Slides

Ontology for data interoperability: 

Ontology for data interoperability Ontology-based Information Integration (TAMBIS) Spread a query over different and heterogeneous data sources Quite used in gene ontology applications but not only…

Thesauri & Classification: 

Thesauri & Classification UNSPSC: United Nations Standard Products and Services Code Provides structrue and a unique identification of terms Thesauri act as a good starting point for developing an ontology