(12) Semantic Web Technologies - Ontological Engineering
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(12) Semantic Web Technologies - Ontological Engineering (12) Semantic Web Technologies - Ontological Engineering Presentation Transcript

  • Semantic Web Technologies Lecture Dr. Harald Sack Hasso-Plattner-Institut für IT Systems Engineering University of Potsdam Winter Semester 2012/13 Lecture Blog: http://semweb2013.blogspot.com/ This file is licensed under the Creative Commons Attribution-NonCommercial 3.0 (CC BY-NC 3.0)Dienstag, 15. Januar 13
  • last lect ure2 & c s ti l e u R ema n e S b t h W e Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13
  • Semantic Web Technologies Content3 1. Introduction 2. Semantic Web - Basic Architecture Languages of the Semantic Web - Part 1 3. Knowledge Representation and Logics Languages of the Semantic Web - Part 2 4. Applications in the ,Web of Data‘ Semantic Web Technologies , Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13
  • 4 a l i c o g n g t o l r i O n e e i n n g E Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13
  • Semantic Web Technologies Content 4. Applications in the Web of Data 4.1. Ontological Engineering 4.2. Linked Data Engineering 4.3. Semantic Search Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13
  • 6 4.Ontology Engineering 4.1.Ontologies revisited 4.2.Methodologies of Ontology Design 4.2.1. In General 4.2.2. Method of Uschold and King 4.2.3. Ontology 101 4.2.4. Unified Process for ONtology (UPON) 4.2.5. Ontology Design Patterns 4.3.Ontology Learning 4.4.Ontology Mapping and Ontology Merging Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13
  • What is an Ontology?7 „A theory of being, which tries to explain the being itself, by developing a system of universal categories and their intrinsic relationships...“ Philosophy Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13
  • What is an Ontology?8 "An ontology is an explicit, formal specification of a shared conceptualization. The term is borrowed from philosophy, where an Ontology is a systematic account of Existence. For AI systems, what ‘exists’ is that which can be represented.“ Computer Science Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13
  • What is an Ontology?9 Top-Level Ontology (Upper Ontology, Foundation Ontology) Domain Ontology Task Ontology Application Ontology Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität Potsdam (acc. to Guarino,1998)Dienstag, 15. Januar 13
  • Ontologies and the Semantic Web10 •Semantic Web is based on the Interoperability of Metadaten •Among heterogeneous Metadata there is a Semantic Gap that can be bridged with the help of ontologies •Problem of the Semantic Gap: • different ontologies can be applied to represent identical knowledge. Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13 Turmbau zu Babel, Pieter Brueghel, 1563
  • The Semantic Gap - A Simple Example •Let‘s model a world:11 World C C B A B A Initial State Final State Modelling 1: Modelling 2: Objects Relations Objects Relations block A on(X,Y) block A on(X,Y) block B clear(X) block B clear(X) block C holding(X) block C onTable(X) table T handEmpty holding(X) hand H handEmpty Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13 Turmbau zu Babel, Pieter Brueghel, 1563
  • Modelling and Ontologies •behind the model there is an ontology C12 Modelling 1: C B Objectx Relations B A A block A on(X,Y) Final State Initial State block B clear(X) block C holding(X) table T handEmpty hand H ⊤ entity relation table block hand binary unary block A on clear handEmpty table T block B hand A holding block C Axiom: on(X,Y) ⋀ on(Y,Z) → above(X,Z) Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13 Turmbau zu Babel, Pieter Brueghel, 1563
  • Modelling and Ontologies •behind the model there is an ontology C13 B Modelling 2: C A Objects Relations A B Final State block A on(X,Y) Initial State block B clear(X) block C onTable(X) holding(X) handEmpty ⊤ entity relation block binary unary block A on clear handEmpty block B holding block C onTable Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13
  • Ontological Engineering •Ontologies enable interoperability among metadata14 •Therefore, we need •Methods for efficient development of ontologies (Ontology Design) •Methods for efficient comparison of ontologies (Ontology Mapping) •Methods for efficient combination of ontologies (Ontology Merging) •There are automated methods to support Ontological Engineering: •Learning new ontologies from a given set of information resources (Ontology Learning) •Populating existing ontologies with individuals from information resources Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13
  • „Zur only method nur driven, the „The Methode wirdto beder getrieben, dem die Empirie lästig wird.“ empiricism is annoying.“15 -- Johann Wolfgang von Goethe, aus „Maxims and Reflections” „Maximen und Reflexionen” Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13
  • 16 4.Ontology Engineering 4.1.Ontologies revisited 4.2.Methodologies of Ontology Design 4.2.1. In General 4.2.2. Method of Uschold and King 4.2.3. Ontology 101 4.2.4. Unified Process for ONtology (UPON) 4.2.5. Ontology Design Patterns 4.3.Ontology Learning 4.4.Ontology Mapping and Ontology Merging Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13
  • Methodologies of Ontology Design •A methodology of Ontology Design describes all activities17 necessary for the construction of an ontology. •Why do we need a formal methodology? •development of consistent ontologies •efficient development of complex ontologies •distributed development of ontologies •We distinguish (acc. to Fernandez-Lopez et. al., 1997) •Ontology management activities •Ontology development oriented activities •Ontology support activities Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13
  • Ontology Management Activities •Scheduling18 •Identification tasks/problems to solve •Arrangement/Planning of tasks/problems to solve •Identification of required resources (time, memory, etc…) • •Control •Guarantees correct execution of tasks/problems to solve • •Quality Assurance •Quality assurance of all produced resources during development (Ontologies, Software, Documentation) Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13
  • Ontology Development Oriented Activities 1.Pre-Development19 2.Development 3.Post-Development 1.Pre-Development •Environment Study •What is the designated software platform for the ontology? •Which applications should use the ontology? •Feasibility Study •Can the ontology really be developed? •Does it make sense to develop the ontology? Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13
  • Ontology Development Oriented Activities 2.Development20 •Specification •Why is the ontology developed, what is the benefit and who are the end-users? •Conceptualization •Structuring domain knowledge in a conceptual model •Formalization •Formalize conceptual model in (semi-)computable model •Implementation •Construction of a computable model in an ontology representation language Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13
  • Ontology Development Oriented Activities21 3.Post-Development •Maintenance •Update and adjustment of the ontology (if necessary) •Use / Reuse •Usage of the ontology within the designated applications as well as in unplanned applications/ontologies Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13
  • Ontology Support Activities •Knowledge Acquisition22 •Gather knowledge from experts (Ontology Learning) •Evaluation •Technical evaluation of the ontology in each step of the development process •Integration •Reuse of existing ontologies (Ontology Reuse) •Merging •Construction of a new ontology from already existing ontologies within a specific domain Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13
  • Ontology Support Activities •Alignment23 •Mapping rules for involved ontologies •Documentation •Each step of the ontology development must be acurately documented •Configuration Management •Manages all versions of documentation and of the developed ontology Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13
  • Ontological Engineering acc. to Fernandez-Lopez et. al., 1997 Management Development Oriented Support24 environment study feasibility study knowledge acquisition scheduling evaluation integration specification conceptualization control formalization implementation documentation merging quality assurance maintenance use / reuse configuration alignment management Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13
  • Ontological Engineering acc. to http://geekandpoke.typepad.com/geekandpoke/2012/01/simply-explained-dp.html25 SOmething GREAT ONTOLOGY Development Process Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13
  • 26 4.Ontology Engineering 4.1.Ontologies revisited 4.2.Methodologies of Ontology Design 4.2.1. In General 4.2.2. Method of Uschold and King 4.2.3. Ontology 101 4.2.4. Unified Process for ONtology (UPON) 4.2.5. Ontology Design Patterns 4.3.Ontology Learning 4.4.Ontology Mapping and Ontology Merging Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13
  • Methode of Uschold and King •Process based development27 Building identify documen- capture coding integrating evaluation purpose tation 1 2 3 4 •1995/96, first proposal of a methodology for ontology development •IBM, University of Edinburgh, Unilever,... •Development of an ,Enterprise Ontology‘ Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13
  • Building identify purpose capture coding integrating evaluation documen- tation Methode pf Uschold und King28 1 Identify Purpose and Domain of Application •Why is the ontology needed? •Designated application? •(use / reuse / share / used as part of KB / …) •Identify all terms relevant for the application Example: Travel Ontology •Development of a common ontology for the domain ,Travel‘ that should be used in travel offices •Ontology could also be used for other application areas, e.g. to develop a catalogue for hotels and transportation means •relevant terms e.g: locations, types of locations, accommodations, types of accommodations (hotel / motel / camping / …), railway, busses, subway,... Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13
  • Building identify purpose capture coding integrating evaluation documen- tation Methode of Uschold and King29 2 Ontologie Building • Ontology Capture Identify key concepts (classes) and relationships (relations) of the domain under consideration • Humans as domain experts, who possess the mandatory knowledge... • ...are not neccessarely fully-trained logicians that are able to design an ontology • Therefore, knowledge engineers often are appointed to support the domain experts Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13
  • Building identify purpose capture coding integrating evaluation documen- tation Methode of Uschold and King30 2 Ontologie Building •Ontology Capture •Identify key concepts (classes) and relationships (relations) of the domain under consideration and provide them as plain text Example: Travel Ontology • Transportation is a class. Each Transportation has a Starting Point • Bus is a class. Bus is a Transportation. • City Bus is a class. A City Bus is a Bus, whose Starting Point as well as its Destination and all Stopovers are located in the same City. •Identification of ontology concepts •Bottom-up / Top-down / Middle-Out Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13
  • Building identify purpose capture coding integrating evaluation documen- tation Methode of Uschold and King31 2 Ontologie Building •Ontology Capture • Bottom-up identification of ontology concepts • Construction from ,special‘ to ,general‘ • Identification of concepts with the most clear specification, then generalisation towards abstract concepts Example: Travel Ontology • Transportation is conceptualized with a Bottom-up strategy Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13
  • Building identify purpose capture coding integrating evaluation documen- tation Methode of Uschold and King32 2 Ontologie Building •Ontology Capture • Bottom-up identification of ontology concepts Transportation is-subClass-of London Transportation Subway City Bus Taxi Paris Transportation London London London Paris Paris Paris Underground Local Bus Taxi Metro Local Bus Taxi Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13
  • Building identify purpose capture coding integrating evaluation documen- tation Methode of Uschold and King33 2 Ontologie Building •Ontology Capture • Bottom-up identification of ontology concepts • increased total cost • difficult to find common ground among related concepts • increased risk of inconsistencies → then revision is necessary (even more expensive) Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13
  • Building identify purpose capture coding integrating evaluation documen- tation Methode of Uschold and King34 2 Ontologie Building •Ontology Capture •Top-down identification of ontology concepts •first identify abstract concepts, then continue with specialization Example: Travel Ontology • Transportation is conceptualized with a Top-down strategy Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13
  • Building identify purpose capture coding integrating evaluation documen- tation Methode of Uschold and King35 2 Ontologie Building •Ontology Capture •Top-down identification of ontology concepts object is-subClass-of concrete object abstract object Transport Transport Transport Subway City Bus Taxi with Taxi with Bus with Subway uses uses uses Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13
  • Building identify purpose capture coding integrating evaluation documen- tation Methode of Uschold and King36 2 Ontologie Building •Ontology Capture •Top-down identification of ontology concepts •Level of detail can be better controled •perhaps abstract concepts are not needed at all for ontology / application •Less stability of the model → then revision is necessary (even more expensive) Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13
  • Building identify purpose capture coding integrating evaluation documen- tation Methode of Uschold and King37 2 Ontologie Building •Ontology Capture •Middle-Out identification of ontology concepts •Start with core concepts, then specialication and/or generalization Example: Travel Ontology • Transportation is conceptualized with a Middle-Out strategy Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13
  • Building identify purpose capture coding integrating evaluation documen- tation Methode of Uschold and King38 2 Ontologie Building •Ontology Capture •Middle-Out identification of ontology concepts Transportation is-subClass-of Subway Bus Taxi City Bus Shuttle Bus Travel Bus Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13
  • Building identify purpose capture coding integrating evaluation documen- tation Methode of Uschold and King39 2 Ontologie Building •Ontology Capture •Middle-Out identification of ontology concepts •well balanced (wrt. level of detail / abstraction) •more stable than the other two methods Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13
  • Building identify purpose capture coding integrating evaluation documen- tation Methode of Uschold and King40 2 Ontologie Building •Coding •All who participate in the development of the ontology must agree on a common structure of the knowledge base •Integration of Existing Ontologies •Decision, whether and how existing ontologies should be reused •can be performed in parallel with the other activities Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13
  • Building identify purpose capture coding integrating evaluation documen- tation Methode of Uschold and King41 3 Evaluation •Technical evaluation of the ontologies and the application software in each step of the development process 4 Documentation •Establishing guidelines for documentation Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13
  • 42 4.Ontology Engineering 4.1.Ontologies revisited 4.2.Methodologies of Ontology Design 4.2.1. In General 4.2.2. Method of Uschold and King 4.2.3. Ontology 101 4.2.4. Unified Process for ONtology (UPON) 4.2.5. Ontology Design Patterns 4.3.Ontology Learning 4.4.Ontology Mapping and Ontology Merging Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13
  • Ontology Development 101 (Noy, McGuinness, 2000) •Example of a wine and food ontology43 Which wine fis the right one for fish? French wine-growing regions and wines A shared ontology on wine and food Californian wine-growing regions and wines Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13
  • Ontology Development 101 (Noy, McGuinness, 2000) •Example of a wine and food ontology44 Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13
  • Ontology Development Process determine consider enumerate define define define create scope reuse terms classes properties constraints instances45 •in practice an iterative Process that repeats continuously and improves the ontology •there are always different approaches for modelling an ontology •in practice the designated application decides about the modelling approach „There is no one cor rect way to model a domain. There are always vi able alternativ es.“ Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13
  • Determine Domain and Focus determine consider enumerate define define define create scope reuse terms classes properties constraints instances46 •Which domain should be covered by the ontology? •What should the ontology be used for? •What types of Questions should be answered by the knowledge represented in the ontology? •Who will use and maintain the ontology? •Formulation of Competence Questions These Questions migh t change within the ontology life cycle Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13
  • Determine Domain and Focus determine consider enumerate define define define create scope reuse terms classes properties constraints instances47 Competence Questions (Wine Ontology) •Which properties of the wine should be considered for modelling? •Is Bordeaux a white wine or a red wine? •Does a Sauvignon Blanc match with fish? •Which wine matches best for grilled meat? •Which properties of a wine do influence whether it matches with a specific dish? •Does the bouquet of a wine change with different vintages? •... Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13
  • Consider Reuse determine consider enumerate define define define create scope reuse terms classes properties constraints instances48 •Why should we consider reuse? •in order to save cost •in order to apply tools that are applied for other existing ontologies also for our own ontology •in order to reuse ontologies that have been validated by their application If you don‘t f ind a suitabl adaption is t e ontology or oo complex th if the en create a n ew ontology! Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13
  • Develop a Terminology determine consider enumerate define define define create scope reuse terms classes properties constraints instances49 •About which concepts are we talking? •Which properties have these concepts? •What do we want to say about these concepts? Example: Wine Ontology •wine, grape, winery, location,... •a wine‘s color, body, flavor, sugar content,... •subtypes of wine: white wine, red wine, Bordeaux wine,... •types of food: seafood, fish, meat, vegetables, cheese,... •... Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13
  • Develop Classes and Class Hierarchies determine consider enumerate define define define create scope reuse terms classes properties constraints instances50 •Classes are concepts in the designated domain •class of wines •class of wineries •class of red wines •... •Classes are collections of objects with similar properties •Choose a top-down / bottom-up / middle-out approach to model class hierarchies Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13
  • Define Properties determine consider enumerate define define define create scope reuse terms classes properties constraints instances51 •Properties in a class definition describe attributes of instances •every wine has a color, residual sugar, producer, etc... Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13
  • Define Property Constraints determine consider enumerate define define define create scope reuse terms classes properties constraints instances52 •Property constraints (restrictions) describe or restrict the set of possible property values •The name of a wine is a String •The producer is an instance of Winemaker Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13
  • Definition of Instances determine consider enumerate define define define create scope reuse terms classes properties constraints instances53 •Create Instances for the classes •Every class directly becomes the type of its instances •Every superclass of a direct type is also type of its instances •Create instances for properties, i.e. assignment of property values for the instances according to the given constraints •„the glass of red wine that I drank last supper...“ Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13
  • 54 4.Ontology Engineering 4.1.Ontologies revisited 4.2.Methodologies of Ontology Design 4.2.1. In General 4.2.2. Method of Uschold and King 4.2.3. Ontology 101 4.2.4. Unified Process for ONtology (UPON) 4.2.5. Ontology Design Patterns 4.3.Ontology Learning 4.4.Ontology Mapping and Ontology Merging Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13
  • Unified Process for Ontology Building De Nicola, Missikoff, Navigli (2005)55 •Based on Unified Process (UP) methodology in software development and Unified Modelling Language (UML) •Use-Case driven, i.e. more suitable for application ontologies than for domain ontologies •Goals: •Reduction of time and cost in the development of large scale ontologies •Quality improvement of the developed ontology via progressive validation of intermediate results •Methodology for efficient collaboration of Knowledge Engineers and Domain Experts with clear separation of roles •Intermediate results can already be evaluated by the user Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13
  • Unified Process for Ontology Building De Nicola, Missikoff, Navigli (2005) •Development is divided into Cycles, which are subdivided into 4 Phases of Iterations (Inception, Elaboration, Construction,56 Transition). Each iteration results in a new prototype •Each iteration consists of 5 workflowes (Requirements, Analysis, Design, Implementation, Test) Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13
  • Unified Process for Ontology Building De Nicola, Missikoff, Navigli (2005) •Workflows and Phases are almost orthogonal, i.e. involvement of single workflows in different phases of ontology57 development does vary Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13
  • Unified Process for Ontology Building De Nicola, Missikoff, Navigli (2005) (1)Requirements Workflow58 Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13
  • Unified Process for Ontology Building De Nicola, Missikoff, Navigli (2005) (2)Analysis Workflow59 Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13
  • Unified Process for Ontology Building De Nicola, Missikoff, Navigli (2005) (3)Design Workflow60 Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13
  • Unified Process for Ontology Building De Nicola, Missikoff, Navigli (2005) (4)Implementation Workflow61 Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13
  • Unified Process for Ontology Building De Nicola, Missikoff, Navigli (2005) (5)Test Workflow62 Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13
  • 63 4.Ontology Engineering 4.1.Ontologies revisited 4.2.Methodologies of Ontology Design 4.2.1. In General 4.2.2. Method of Uschold and King 4.2.3. Ontology 101 4.2.4. Unified Process for ONtology (UPON) 4.2.5. Ontology Design Patterns 4.3.Ontology Learning 4.4.Ontology Mapping and Ontology Merging Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13
  • Ontology Design Patterns (Gangemi, 2005) •Adapting an Idea originally from Architecture64 •recurring modeling problems •providing a set of adaptable standard solutions •Ontology Design Patterns provide •small reusable (abstract) ontology templates with explicit documentation •searchable repository ordered by competence questions •We distinguish: •Content Patterns •Domain dependent, language independent •Logical Patterns •Domain independent, related to representation language •Presentation Patterns •Ontology from user perspective, as e.g. naming conventions •Transformation Patterns •how to transform an ontology in another representation language Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13
  • Content Pattern vs. Logical Pattern •Logical ODPs solve design problems independently of a65 particular conceptualization •Content ODPs are patterns for solving design problems for the domain classes and properties that populate an ontology; they address content problems •Content ODPs are instantiations of Logical ODPs (or of compositions of Logical ODPs) •Modeling problems solved by Content ODPs have two components: domain and requirements. •the same domain can have many requirements •the same requirement can be found in different domains •A typical way of capturing requirements is by means of competency questions •Content ODPs are collected and described in catalogues and comply to a common presentation template Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13
  • Content Pattern - A Simple Example •Example: taking over a temporary role66 •e.g.: Basil Rathbone played Sherlock Holmes in the 1939 movie „The Hound of the Baskervilles“ •Analyze the sentence, detect the modeling issues, and match to the Content ODPs •A person •represent objects and plays a character the roles they play Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13
  • 67 Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13
  • 4.Ontology Engineering68 4.1.Ontologies revisited 4.2.Methodologies of Ontology Design 4.2.1. In General 4.2.2. Method of Uschold and King 4.2.3. Ontology 101 4.2.4. Unified Process for ONtology (UPON) 4.2.5. Ontology Design Patterns 4.3.Ontology Learning 4.4.Ontology Mapping and Ontology Merging Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13
  • Ontology Learning •Ontology Design is very expensive wrt. time and resources •can we automate the process or at least some parts?69 •Ontologies can be „learned“ automatically •Ontology Learning defines a set of methods and techniques •for fundamental development of new ontologies •for extension or adaption of already existing ontologies •in a (partly) automated way from various resources. •also referred to as Ontology Generation, Ontology Mining, or Ontology Extraction •Automatisation requires help from •Natural Language Processing (NLP) •Data Mining •Machine Learning techniques (ML) Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13
  • Ontology Learning •Fundamental data for ontology learning:70 •Structured Data Machine Learning •Semi-structured Data XML HTML Natural XML Language HTML Processing + Machine •Unstructured Data Learning Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13
  • Ontology Learning - Basic Approach (I)71 document corpus terms ontology pet (1) Term (2) Conceptualization extraction … <dog> <dogs> <cat> dog cat <siamese cat> siamese cat (3) Evaluation and Adaption semi-automated process Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13
  • Ontology Learning - Basic Approach (II)72 • Natural Language Processing: (1)Tokenizer / Sentence Splitter (2)Morphological Analysis • Stemming • Lemmatizer (3)POS-Tagger • Syntactic categories (verb, nomen, preposition, etc...) (4)Regular Expression Matching (5)Chunks • Detection of larger coherent structures in sentences (6)Syntactic Parser Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13
  • Ontology Learning - Layer Cake73 ∀x((country(x)→∃y capitalOf(y,x)) ∧ (∀z (capitalOf(z,x)→y=z))) General Axions river ⊓ mountain = ∅ Axiomatic Schemata capitalOf ⊑R locatedIn Hierarchy of Relations flowThrough(dom:river, range:GeoEntity) Relations capital ⊑c city , city ⊑c InhabitedGeoEntity Hierarchy of Concepts c:=country:=<description(c), uri(c)> Concept Description {country, nation, Land} Multilingual Synonyms river, country, nation, city, capital, ... Terms Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13
  • Ontology Learning Tasks •which tasks from ontology development can74 be automated? Ontology Learning Tasks • Ontology creation • Ontology schema extraction • Extraction of ontology instances • Ontology integration and navigation • Ontology update • Ontology enrichment Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13
  • Ontology Learning Tasks •Ontology creation75 •Design from the scratch by an expert •Maschine Learning (ML) supports the expert during design by •Suggestions of well suited relations among concepts •Integrity / consistency checking of the designed ontology •Ontology schema extraction •Extraction of schemata from web documents / text documents / etc. •ML uses input data and meta ontology to create fully-fledged domain ontologies (with the help of human experts) Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13
  • Ontology Learning Tasks76 •Extraction of ontology instances •Extraction of ontology instances from semi-structured or unstructured data to fill already existing ontology schemata with individuals •applies technologies from Information Retrieval and Data Mining •Ontology integration and navigation •Reconstruction of existing knowledge bases and navigation in existing knowledge bases, •e.g. translation of a knowledge base from FOL to OWL Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13
  • Ontology Learning Tasks77 •Ontology update •Extension, reconstruction and adaption of already existing ontologies, e.g. adation to a changed domain •relates to parts of ontologies that have been created in the way that they can be changed •Ontology enrichment •(also Ontology tuning) relates to automated update of smaller parts of existing ontologies •doesn‘t changes concepts and relations, but refines them (more precise) •in difference to ontology update only parts of the ontology are considered that usually shouldn‘t be changed Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13
  • 78 4.Ontology Engineering 4.1.Ontologies revisited 4.2.Methodologies of Ontology Design 4.2.1. In General 4.2.2. Method of Uschold and King 4.2.3. Ontology 101 4.2.4. Unified Process for ONtology (UPON) 4.2.5. Ontology Design Patterns 4.3.Ontology Learning 4.4.Ontology Mapping and Ontology Merging Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13
  • Ontology Mapping79 •to communicate, partners must use the same formal specification of a formal conceptualization •but, to agree on the same ontology is not always a simple task... (different applications, different views and opinions, different contexts,...) •Partners, who use different ontologies (for the same domain) will not be able to communicate or to understand each other •Ontologies must be mapped upon each other (= Ontology Mapping / Ontology Matching / Ontology Alignment ) Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13
  • Ontology Mapping80 •is a process, where two ontologies are set in relation with each other on a conceptual level (Schema Matching). •Thereby, instances of the start ontology OS will be transformed into instances of the target ontology OT according to their semantical relationship by using a mapping M: OS → OT . •The mapping M can be •injective (not invertible) or also •bijective sein Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13
  • Heterogeneity of Ontology •Syntactical Heterogeneity: •Ontologies are available in different ontology representation languages81 (E.g.: in OWL DL and F-Logic) •can be resolved on the conceptual level, most times preserving the semantics •Terminological Heterogeneity: •Naming differences for the identification of entities in different ontologies (E.g.: ,Artikel‘ and ,Publication‘) •Might occur because different (natural) languages are used •Conceptional (Semantic) Heterogeneity •Ontologies model the same domain, but in different ways •Differences might occur in completeness, granularity, perspective, etc. •Semiotic (Pragmatic) Heterogeneity •Differences in interpretation of the domain to be modelled by humans (difficult) Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13
  • Ontology Mapping82 owl:equivalentClass Object Thing owl:equivalentClass is_a is_a is_a Wheeled Car Locomotive has is_a is_a is_a Big Car has Train Automobile Engine has owl:equivalentClass has has has is_a is_a Bus Cylinder Horsepower Horsepower Autobus owl:equivalentClass Ontology 1 Ontology 2 Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13
  • Ontology Mapping •Ontology Mapping is not a “new” problem…83 •the same problems occur in Data Integration, e.g. for federated databases •Federated databases manage local schemata for each contributing local database •Data Integration (Schema Matching) can be applied via •bilateral mapping or •global Schemata and a mapping for each single local schema (mapping can be implemented via view) Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13
  • Ontology Mapping Process •Basic approach84 OS1 1 2 3 mapping M(OS1) import find specify ontologies similarities mapping / merging merged ontology OS2 Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13
  • Ontology Mapping Process70 Schema-Based Matching Techniques Schema-Based Matching Techniques Granularity /85 Input Interpretation Element-Level Structure-Level Syntactic External Syntactic External SemanticString- Language- Linguistic Constraint- Alignment Upper Data Graph- Taxonomy- Repository Model-Based Based Resources Based Reuse Level Analysis Based Based Of Based• Name • Tokenization • Lexicons • Type • Entire vs & • Graph • Taxonomy Structures • DL reasoner similarity similarity schema Domain Statistics homo- structure • Structure • SAT Solver • Lemmati- • Thesauri or specific morphism• Description zation • Key • Frequency metadata similarity properties • Ontology Ontolo- • Distribution • Path, • Morphology fragments gies children, Basic Techniques Linguistic Internal Relational Terminological Structural Extensional Semantic Schema-Based Matching TechniquesEuzenat, Shvaiko: Ontology Matching, SpringerHarald Sack, Hasso-Plattner-Institut, Universität Potsdam Vorlesung Semantic Web, Dr. 2007 Kind of InputDienstag, 15. Januar 13
  • Ontology Merging70 •is a process, where from two or mor start ontologies a new86 ontology is created. •The new ontology unifies and substitutes the original start ontologies. •Union Approach The new ontology is the union of all entities of the start, where conflicts arrising from different representations of identical concepts from the start ontologies must be resolved. •Intersection Approach (extensional) The new ontology only consists out of parts of the start ontologies that overlap. Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13
  • 87 4.Ontology Engineering 4.1.Ontologies revisited 4.2.Methodologies of Ontology Design 4.2.1. In General 4.2.2. Method of Uschold and King 4.2.3. Ontology 101 4.2.4. Unified Process for ONtology (UPON) 4.3.Ontology Learning 4.4.Ontology Mapping and Ontology Merging Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13
  • Next lecture t a D na s d io e t k a n c i88 L li c h p p & ear A S t i c a n S e m Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13
  • 4. Semantic Web Technologies 4.1 Ontological Engineering89 Literature » A. Gomez-Perez et al. Ontological Engineering, Springer, 2004. » J. Euzenat, P. Shvaiko: Ontology Matching, Springer, 2007. Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13
  • 4. Semantic Web Technologies 4.1 Ontological Engineering90 □Blog http://semweb2013.blogspot.com/ □Webseite http://www.hpi.uni-potsdam.de/studium/ lehrangebot/itse/veranstaltung/ semantic_web_technologien-3.html □bibsonomy - Bookmarks http://www.bibsonomy.org/user/lysander07/ swt1213_12 Vorlesung Semantic Web, Dr. Harald Sack, Hasso-Plattner-Institut, Universität PotsdamDienstag, 15. Januar 13