Ontology engineering. Valen.na Tamma. Based on slides by A. Gomez Perez, N. Noy, D. McGuinness, E. Kendal, A. Rector and O. Corcho

Transcription

1 Ontology engineering Valen.na Tamma Based on slides by A. Gomez Perez, N. Noy, D. McGuinness, E. Kendal, A. Rector and O. Corcho

2 Summary Background on ontology; Ontology and ontological commitment; Logic as a form of representa.on; Ontology development phases

3 Ontology design process Requirement and domain analysis Add Instances Determine scope Define constraints Consider reuse Define proper.es Enumerate terms Define classes

4 Requirement analysis Performing Requirements, Domain & Use Case Analysis is a cri.cal stage as in any soqware engineering design. It allows ontology engineers to ground the work and priori.se. The analysis has to elicit and make explicit: The nature of the knowledge and the ques.ons (competency ques-ons) that the ontology (through a reasoner) needs to answer. This process is crucial for scoping and designing the ontology, and for driving the architecture; Architectural issues; The effec.veness of using tradi.onal approaches with knowledge intensive approaches;

5 Aim: The main goal of this phase is to support the applica.on in dealing with: Changing assump.ons Hypothesis genera.on (analogy) System evolu.on, or dynamic knowledge evolu.on where.me and situa.ons change necessita.ng re evalua.on of assump.ons Support for interopera.on with other (poten.ally legacy) systems Genera.on of explana.on for dialogue genera.on facilitate interface with users Standardiza-on of terminology: to reflect the engineers different backgrounds Separa.on of concerns is crucial when dealing with knowledge Declara.ve domain knowledge (what?) needs to be treated differently from procedural knowledge (how?) Ontologies vs Problem solving methods Background (unchanging) knowledge from changing informa.on Provenance and level of trust of knowledge

6 Applica.on requirements Applica.on requirements can be acquired by: Iden.fying any controlled vocabulary is used in the applica.on; Iden.fying hierarchical or taxonomic structures intrinsic in the domain that might be used for query expansion: Vegetarian pizza such as: margherita, funghi, grilled vegetables Analysing structured queries and the knowledge they require Expressive power required: Efficient inference (requiring limited expressive power) vs. increased expressivity (requiring expensive or resource bounded computa.on) Ad hoc reasoning to deal with par.cular domain requirements: temporal rela.ons, geospa.al, process specific, condi.onal opera.ons Computa.onal tractability Need for Explaina.ons, Traces, Provenance

7 Domain requirements Take into account heterogeneity, distribu.on, and autonomy needs soqware agents based applica.ons; Open vs. Closed World (does lack of informa.on imply nega.ve informa.on?) Sta.c vs dynamic ontology processes: Evolu.on, alignment Limited or incomplete knowledge Knowledge evolu.on over.me Analysis and consistency checking of instance data Use Case analysis should facilitate the understanding of: The informa.on that is likely to be available The ques.ons that are likely to be asked Types and roles of users

8 Ontology capture analysis: IDEF 5 The ldef5 Ontology Capture Method aims to construct ontologies in a way that closely reflects human understanding of the specific domain. The process is based on extensive itera.ons, discussions, reviews, and introspec.on. Knowledge extrac.on is a discovery process requiring considerable introspec.on. Collabora.on between ontology engineers and domain experts in a group effort. Ontological analysis is accomplished by examining the vocabulary that is used to discuss the characteris.c objects and processes that compose the domain, developing rigorous defini.ons of the basic terms in that vocabulary, and characterizing the logical connec.ons among those terms. The product of this analysis, an ontology, is a domain vocabulary complete with a set of precise defini.ons, or axioms, that constrain the meanings of the terms sufficiently to enable consistent interpreta.on of the data that use that vocabulary.

9 Ontology capture analysis: IDEF 5 The IDEF5 ontology development process consists of the following five (0verlapping) ac.vi.es: Organizing and Scoping: This ac.vity involves establishing the purpose, viewpoint, and context for the ontology development project and assigning roles to the team members. Data Collec.on: This ac.vity involves acquiring the raw data needed for ontology development. Data Analysis: This ac.vity involves analyzing the data to facilitate ontology extrac.on. Ini.al Ontology Development: This ac.vity involves developing a preliminary ontology from the acquired data. Ontology Refinement and Valida.on: This ac.vity involves refining and valida.ng the ontology to complete the development process. hdp://

10 Conceptual modelling A data model describes data, or database schemas an ontology describes the world Adam Farquhar, Ontology 101, Stanford University, 1997 Resources and their rela.onships are described from an objec.ve standpoint, and they do not reflect the defini.ons in databases, or the views of programmers. Experts from different backgrounds with significant domain knowledge will classify knowledge differently from someone interested in op.miza.on of algorithms, or forcing informa.on into an exis.ng framework, or legacy applica.ons Shortcuts at the top levels do not help; automa.on and mappingamong ontologies and terminology at lower levels provides significant benefit

11 Conceptual Modelling Defini.ons range from high level mind maps to detailed collabora.on, dialog, and informa.on modelling to support knowledge sharing; Tools can be very diverse, from inexpensive brainstorming tools and shareware to sophis.cated ontology and soqware model development environments; Common features include: drawing a picture that includes concepts and rela.onships between them producing sharable ar.facts, that vary depending on the tool oqen including web sharable drawings

12 Ontology design process Requirement and domain analysis Add Instances Determine scope Define constraints Consider reuse Define proper.es Enumerate terms Define classes

13 Determine ontology scope Addresses straight forward ques.ons such as: What is the ontology going to be used for How is the ontology ul.mately going to be used by the soqware implementa.on? What do we want the ontology to be aware of, and what is the scope of the knowledge we want to have in the ontology?

14 Competency Ques.ons Which inves.ga.ons were done with a high fatdiet study? Which study employs microarray in combina.on with metabolomics technologies? List those studies in which the fas.ng phase had as dura.on one day. What is a vegetarian pizza? What type of wine can accompany seafood?

15 Ontology design process Requirement and domain analysis Add Instances Determine scope Define constraints Consider reuse Define proper.es Enumerate terms Define classes

16 Consider Reuse We rarely have to start from scratch when defining an ontology: There is almost always an ontology available from a third party that provides at least a useful star.ng point for our own ontology Reuse allows to: to save the effort to interact with the tools that use other ontologies to use ontologies that have been validated through use in applica.ons

17 Consider Reuse Standard vocabularies are available for most domains, many of which are overlapping Iden.fy the set that is most relevant to the problem and applica.on issue A component based approach based on modules facilitates dealing with overlapping domains: Reuse an ontology module as one would reuse a soqware module Standards; complex rela.onships are defined such that term usage and overlap is unambiguous and machine interpretable Ini.al brainstorming with domain experts can be highly produc.ve; then subsequent refinement and itera.on lead to the level required by the applica.on

18 Modular ontologies

19 What to Reuse? Ontology libraries DAML ontology library ( Protégé ontology library (protege.stanford.edu/plugins.html) Upper ontologies IEEE Standard Upper Ontology (suo.ieee.org) Cyc ( General ontologies DMOZ ( WordNet ( Domain specific ontologies UMLS Seman.c Net GO (Gene Ontology) (

20 Ontology design process Requirement and domain analysis Add Instances Determine scope Define constraints Consider reuse Define proper.es Enumerate terms Define classes

21 Enumerate terms Write down in an unstructured list all the relevant terms that are expected to appear in the ontology Nouns form the basis for class names Verbs (or verb phrases) form the basis for property names Card sor.ng is oqen the best way: Write down each concept/idea on a card Organise them into piles Link the piles together Do it again, and again Works best in a small group

22 Example: animals & plants ontology Dog Cat Cow Person Tree Grass Herbivore Male Female Carnivore Plant Animal Fur Child Parent Mother Father Dangerous Pet Domes.c Animal Farm animal DraQ animal Food animal Fish Carp Goldfish

23 Ontology design process Requirement and domain analysis Add Instances Determine scope Define constraints Consider reuse Define proper.es Enumerate terms Define classes

24 Define classes and their taxonomy A class is a concept in the domain: Animal (cow, cat, fish) A class of proper.es (father, mother) A class is a collec.on of elements with similar proper.es A class contains necessary condi.ons for membership (type of food, dwelling) Instances of classes A par.cular farm animal, a par.cular person Tweety the penguin

25 Organise the concepts Example: Animals & Plants Dog Cat Cow Person Tree Grass Herbivore Male Female Carnivore Plant Animal Fur Child Parent Mother Father Healthy Pet Domes.c Animal Farm animal DraQ animal Food animal Fish Carp Goldfish 49

26 Summary Steps of ontology design Analysis and requirements; Determine scope; Consider reuse; Enumerate terms; Define classes;