Chapter 7 Ontology Engineering Grigoris Antoniou Frank van Harmelen 1 Chapter 7 A Semantic Web Primer Lecture Outline 1. 2. 3. 4. 5. Introduction Constructing Ontologies Manually Reusing Existing Ontologies Using Semiautomatic Methods On-To-Knowledge SW Architecture 2 Chapter 7 A Semantic Web Primer Methodological Questions How can tools and techniques best be applied? Which languages and tools should be used in which circumstances, and in which order? What about issues of quality control and resource management? Many of these questions for the Semantic Web have been studied in other contexts E.g. software engineering, object-oriented design, and knowledge engineering Chapter 7 A Semantic Web Primer 3 Lecture Outline 1. 2. 3. 4. 5. Introduction Constructing Ontologies Manually Reusing Existing Ontologies Using Semiautomatic Methods On-To-Knowledge SW Architecture 4 Chapter 7 A Semantic Web Primer Main Stages in Ontology Development Determine scope 2. Consider reuse 3. Enumerate terms 4. Define taxonomy 5. Define properties 6. Define facets 7. Define instances 8. Check for anomalies Not a linear process! 1. 5 Chapter 7 A Semantic Web Primer Determine Scope There is no correct ontology of a specific domain An ontology is an abstraction of a particular domain, and there are always viable alternatives What is included in this abstraction should be determined by the use to which the ontology will be put by future extensions that are already anticipated Chapter 7 A Semantic Web Primer 6 Determine Scope (2) Basic questions to be answered at this stage are: What is the domain that the ontology will cover? For what we are going to use the ontology? For what types of questions should the ontology provide answers? Who will use and maintain the ontology? 7 Chapter 7 A Semantic Web Primer Consider Reuse With the spreading deployment of the Semantic Web, ontologies will become more widely available 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 starting point for our own ontology Chapter 7 A Semantic Web Primer 8 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 Traditional knowledge engineering tools (e.g. laddering and grid analysis) can be used to obtain the set of terms an initial structure for these terms 9 Chapter 7 A Semantic Web Primer Define Taxonomy Relevant terms must be organized in a taxonomic hierarchy Opinions differ on whether it is more efficient/reliable to do this in a top-down or a bottom-up fashion If A is a subclass of B, then every instance of A must also be an instance of B (compatible with semantics of rdfs:subClassOf Chapter 7 A Semantic Web Primer Ensure that hierarchy is indeed a taxonomy: 10 Define Properties Often interleaved with the previous step The semantics of subClassOf demands that whenever A is a subclass of B, every property statement that holds for instances of B must also apply to instances of A It makes sense to attach properties to the highest class in the hierarchy to which they apply 11 Chapter 7 A Semantic Web Primer Define Properties (2) While attaching properties to classes, it makes sense to immediately provide statements about the domain and range of these properties There is a methodological tension here between generality and specificity: Flexibility (inheritance to subclasses) Detection of inconsistencies and misconceptions Chapter 7 A Semantic Web Primer 12 Define Facets: From RDFS to OWL Cardinality restrictions Required values owl:hasValue owl:allValuesFrom owl:someValuesFrom Relational characteristics symmetry, transitivity, inverse properties, functional values Chapter 7 A Semantic Web Primer 13 Define Instances Filling the ontologies with such instances is a separate step Number of instances &gt;&gt; number of classes Thus populating an ontology with instances is not done manually Retrieved from legacy data sources (DBs) Extracted automatically from a text corpus 14 Chapter 7 A Semantic Web Primer Check for Anomalies An important advantage of the use of OWL over RDF Schema is the possibility to detect inconsistencies In ontology or ontology+instances incompatible domain and range definitions for transitive, symmetric, or inverse properties cardinality properties requirements on property values can conflict with domain and range restrictions Examples of common inconsistencies 15 Chapter 7 A Semantic Web Primer Lecture Outline 1. 2. 3. 4. 5. Introduction Constructing Ontologies Manually Reusing Existing Ontologies Using Semiautomatic Methods On-To-Knowledge SW Architecture 16 Chapter 7 A Semantic Web Primer Existing Domain-Specific Ontologies Medical domain: Cancer ontology from the National Cancer Institute in the United States Cultural domain: Art and Architecture Thesaurus (AAT) with 125,000 terms in the cultural domain Union List of Artist Names (ULAN), with 220,000 entries on artists Iconclass vocabulary of 28,000 terms for describing cultural images Geographical domain: Getty Thesaurus of Geographic Names (TGN), containing over 1 million entries Chapter 7 A Semantic Web Primer 17 Integrated Vocabularies Merge independently developed vocabularies into a single large resource E.g. Unified Medical Language System integrating100 biomedical vocabularies The UMLS metathesaurus contains 750,000 concepts, with over 10 million links between them The semantics of a resource that integrates many independently developed vocabularies is rather low But very useful in many applications as starting point 18 Chapter 7 A Semantic Web Primer Upper-Level Ontologies Some attempts have been made to define very generally applicable ontologies Mot domain-specific Cyc, with 60,000 assertions on 6,000 concepts Standard Upperlevel Ontology (SUO) 19 Chapter 7 A Semantic Web Primer Topic Hierarchies Some ontologies do not deserve this name: simply sets of terms, loosely organized in a hierarchy This hierarchy is typically not a strict taxonomy but rather mixes different specialization relations (e.g. is-a, part-of, contained-in) Such resources often very useful as starting point Example: Open Directory hierarchy, containing more then 400,000 hierarchically organized categories and available in RDF format Chapter 7 A Semantic Web Primer 20 Linguistic Resources Some resources were originally built not as abstractions of a particular domain, but rather as linguistic resources These have been shown to be useful as starting places for ontology development E.g. WordNet, with over 90,000 word senses 21 Chapter 7 A Semantic Web Primer Ontology Libraries Attempts are currently underway to construct online libraries of online ontologies Rarely existing ontologies can be reused without changes Existing concepts and properties must be refined using rdfs:subClassOf and rdfs:subPropertyOf Alternative names must be introduced which are better suited to the particular domain using owl:equivalentClass and owl:equivalentProperty We can exploit the fact that RDF and OWL allow private refinements of classes defined in other ontologies 22 Chapter 7 A Semantic Web Primer Lecture Outline 1. 2. 3. 4. 5. Introduction Constructing Ontologies Manually Reusing Existing Ontologies Using Semiautomatic Methods On-To-Knowledge SW Architecture 23 Chapter 7 A Semantic Web Primer The Knowledge Acquisition Bottleneck Manual ontology acquisition remains a timeconsuming, expensive, highly skilled, and sometimes cumbersome task <a href="/keyword/machine-learning-techniques/" >machine learning techniques</a> may be used to alleviate knowledge acquisition or extraction knowledge revision or maintenance 24 Chapter 7 A Semantic Web Primer Tasks Supported by Machine Learning Extraction of ontologies from existing data on the Web Extraction of relational data and metadata from existing data on the Web Merging and mapping ontologies by analyzing extensions of concepts Maintaining ontologies by analyzing instance data Improving SW applications by observing users 25 Chapter 7 A Semantic Web Primer Useful <a href="/keyword/machine-learning-techniques/" >machine learning techniques</a> for Ontology Engineering Clustering Incremental ontology updates Support for the knowledge engineer Improving large natural language ontologies Pure (domain) ontology learning 26 Chapter 7 A Semantic Web Primer <a href="/keyword/machine-learning-techniques/" >machine learning techniques</a> for Natural Language Ontologies Natural language ontologies (NLOs) contain lexical relations between language concepts They are large in size and do not require frequent updates The state of the art in NLO learning looks quite optimistic: A stable general-purpose NLO exist Techniques for automatically or semi-automatically constructing and enriching domain-specific NLOs exist 27 Chapter 7 A Semantic Web Primer <a href="/keyword/machine-learning-techniques/" >machine learning techniques</a> for Domain Ontologies They provide detailed descriptions Usually they are constructed manually The acquisition of the domain ontologies is still guided by a human knowledge engineer Automated learning techniques play a minor role in knowledge acquisition They have to find statistically valid dependencies in the domain texts and suggest them to the knowledge engineer Chapter 7 A Semantic Web Primer 28 <a href="/keyword/machine-learning-techniques/" >machine learning techniques</a> for Ontology Instances Ontology instances can be generated automatically and frequently updated while the ontology remains unchanged Fits nicely into a machine learning framework Successful ML applications Are strictly dependent on the domain ontology, or Populate the markup without relating to any domain theory General-purpose techniques not yet available Chapter 7 A Semantic Web Primer 29 Different Uses of Ontology Learning Ontology acquisition tasks in knowledge engineering Ontology creation from scratch by the knowledge engineer Ontology schema extraction from Web documents Extraction of ontology instances from Web documents Ontology integration and navigation Updating some parts of an ontology Ontology enrichment or tuning Ontology maintenance tasks 30 Chapter 7 A Semantic Web Primer Ontology Acquisition Tasks Ontology creation from scratch by the knowledge engineer ML assists the knowledge engineer by suggesting the most important relations in the field or checking and verifying the constructed knowledge bases ML takes the data and meta-knowledge (like a metaontology) as input and generate the ready-to-use ontology as output with the possible help of the knowledge engineer Ontology schema extraction from Web documents 31 Chapter 7 A Semantic Web Primer Ontology Acquisition Tasks(2) Extraction of ontology instances from Web documents This task extracts the instances of the ontology presented in the Web documents and populates given ontology schemas This task is similar to information extraction and page annotation, and can apply the techniques developed in these areas 32 Chapter 7 A Semantic Web Primer Ontology Maintenance Tasks Ontology integration and navigation Deals with reconstructing and navigating in large and possibly machine-learned knowledge bases Updating some parts of an ontology that are designed to be updated Ontology enrichment or tuning This does not change major concepts and structures but makes an ontology more precise Chapter 7 A Semantic Web Primer 33 Potentially Applicable Machine Learning Algorithms Propositional rule learning algorit...