Knowledge Modelling and Decision Support in the Water Domain

1. ECO-GEOWATER Euro Lab Course “GIS and Water Domain” Knowledge Modelling and Decision Support in the Water Domain A short introduction into theory and practice of creating knowledge bases and build an inference machine Reiner Borchert

2. Content • Introduction • Ontologies and Knowledge Bases • The Inference Machine • A simple example, taken from the FLUMAGIS project (on the Protégé platform) Knowledge Modelling and Decision Support in the Water Domain

3. 1. Introduction I: Background The FLUMAGIS project has been proposed to create a software tool for implementing new issues of the European Union WaterFrameGuideline. Knowledge Modelling and Decision Support in the Water Domain

4. 1. Introduction II: Some issues of the Water Frame Guideline • The catchment of a river has to be considered as a whole, regardless of political and administrative frontiers. • The ecological situation of the whole watershed has to be assessed and compared to regional reference states. • Socio-economic aspects must be taken into account. • A broad public participation in decision and planning has to be aimed at (minimizing conflicts etc.). • These examples show how the guideline leads to a more complex proceeding in decision finding and planning. Knowledge Modelling and Decision Support in the Water Domain

5. 1. Introduction III: Questions to a River System • Which ecological deficits can be detected in the watershed, and what are they caused by? • What kind of measures (actions) must be executed in order to remove or mitigate the detected deficits? • Which of the proposed actions will cause the desired effects in the best way? • Which participant interests are touched by a certain decision? Knowledge Modelling and Decision Support in the Water Domain

6. 1. Introduction IV: Questions to a Decision Support System • What types of ecological deficits can be expected in the watershed, and what issue types are they caused by? • What types of actions can be executed in order to remove or mitigate a certain deficit type? • What are the effects of a proposed action type and how can they be compared to the effects of other action types? • How can participant interests be detected? Knowledge Modelling and Decision Support in the Water Domain

7. 1. Introduction V: Building a Knowledge Base – Conceptualization and Generalization • Steps towards a domain specific ontology: • Find all relevant topics of your domain (e.g. river bank, deficit, width, cause/effect). • Find out which topics are objects, which are attributes of objects, and which are relations between objects. • Create classes of objects (= concepts). • Define their attributes and relations (= properties). • Try to find more general topics that classify concepts with common properties (e.g. tree is more general than oak or pine). Knowledge Modelling and Decision Support in the Water Domain

8. 2. Ontologies and Knowledge Bases I: What is a Knowledge Base (KB)? • A description of all relevant topics and concepts of a domain • A collection of facts, rules, and constraints concerning the concepts, stored in a machine-readable way • A tool for inferring new facts (forward and backward chaining) • Contrast to databases: • DB: structured by tables, columns (fields), data types, but: no information about the meaning of structure items without metadata • KB: provides information about the meaning of data and logical rules to deal with them. Knowledge Modelling and Decision Support in the Water Domain

9. 2. Ontologies and Knowledge Bases II: Components of a Knowledge Base (KB) • Object Ontology: all relevant real world object types, attributes, and relations. • Domain Ontologies: topics, methods, and proceedings. • Inference Machine: a causal net (containing all relevant cause/effect relations), modules to execute inferences, evaluations, and analyses. • Geodata Access: To run inferences, analyzes, evaluations, prognoses, the KB needs to access the available facts (real world data). In the water domain you normally have to deal with huge geodatabases. Knowledge Modelling and Decision Support in the Water Domain

10. 2. Ontologies and Knowledge Bases III: Ontologies I What is an Ontology ? „An ontology is a specification of a conceptualization: A body of formally represented knowledge is based on a conceptualization: the objects, concepts, and other entities that are assumed to exist in some area of interest and the relationships that hold among them.“ (Genesereth & Nilsson, 1987) In other words: an ontology is not an image of the real world, but a specification of imaginations and concepts of them we are carrying in our heads. Knowledge Modelling and Decision Support in the Water Domain

11. 2. Ontologies and Knowledge Bases IV: Ontologies II Thus classifications don’t focus on objective reality, but on the requirements of our specific view of the world. They deal much more with language, logic, and semantics rather than physics. According to this there are (probably unlimited) numerous ways to create ontologies of the real world in a proper way. Anyway, ontologies can be faulty: classifications and relations can be logically contradictory, they can violate formal rules, or they can be inadequate and misinterpreting the imaginations in our heads. Knowledge Modelling and Decision Support in the Water Domain

12. 2. Ontologies and Knowledge Bases V: Ontologies III Concepts and Individuals - Classes and Instances • Class = classification of objects sharing common properties • Instance = individual, classified or not, has properties with concrete values Each human language constitutes a basic ontology of the things we have to deal with. Substantives serve as classes, adjectives as attributes, and verbs as interactions and relations between any objects. Knowledge Modelling and Decision Support in the Water Domain

13. 2. Ontologies and Knowledge Bases VI: Ontologies IV The Practical Use of Ontologies: a well known example… Knowledge Modelling and Decision Support in the Water Domain

14. 2. Ontologies and Knowledge Bases VII: Ontologies V Talking about any facts we use this fundamental ontology when we create phrases. Grammatical subjects and objects refer to instances of the classes termed by the substantives. Example: “This tree is an oak” …means: This instance of the class “tree”, which may have a property called “has botanical species”, has the property value “oak”. Knowledge Modelling and Decision Support in the Water Domain

15. 2. Ontologies and Knowledge Bases VIII: Ontologies VI The decision what is class and what is instance basically depends on context and intention. Is “oak” a concept or an individual? • “oak” may be an individual of the concept “species”. In this case “oak” appears as an individual species of the class “species”. • But: “oak” can also be a class of individual trees. Knowledge Modelling and Decision Support in the Water Domain

16. 2. Ontologies and Knowledge Bases IX: Ontologies VII Hierarchy of Generalization and Inheritance of Properties If some concepts share common properties, they may be generalized by creating a superclass, which holds the common properties. • Each class inherits the properties from its superclass, and it can define own properties additionally. • All subclasses must hold the “is a” relation with regard to the superclass. • Superclasses normally are abstract, what means that there exist no direct instances of them. Knowledge Modelling and Decision Support in the Water Domain

17. class creature class plant class tree class broad-leafed tree class oak instances: individual oaks class conifer class pine instances: individual pines class animal class vertebrate class fish class eel instances: individual eels class taxon class order class family instances: individual families (tree, fish, mammal) class genus class species instances: individual species (oak, pine, eel) 2. Ontologies and Knowledge Bases X: Ontologies VIII:2 different “oak” ontologies (phenomenology vs. taxonomy) Knowledge Modelling and Decision Support in the Water Domain

18. 3. The Inference Machine I A Causal Network Ontologies provide the “statics” of a knowledge base, whereas the employment of facts (data) and rules has to be done by active tools. A central challenge of a KB is to analyze available facts, to gain new facts from known ones, to run prognoses and maybe even simulations. A core role is played by the Inference Machine, an active module to employ logical rules on known facts. Inference can be applied in two directions: Knowledge Modelling and Decision Support in the Water Domain

19. Known facts of a KB often imply further derived facts, which can be inferred by using logical rules:  Forward chaining checks whether the “if” part of the rules is true (1). In this case a new fact can be assumed by “firing” the rule. In sequence other rules may come true (2). “If the river has flooding for at least 4 weeks a year, then a alluvial forest will appear in the flooding zone.” “If an alluvial forest exists in the flooding zone, a rich Amphibian fauna can be expected.” 3. The Inference Machine II Rules: Forward Chaining • Forward chaining is a method to check which new facts can be inferred from given ones. Knowledge Modelling and Decision Support in the Water Domain

20. If you take a look at a given river basin, then you may ask the KB for example (1):  Backward chaining proves whether a certain goal can be reached. A goal appears as a “then” term of a rule (2). Backward chaining is useful in diagnostics and analysis. “Can an alluvial forest arise at a certain location?” “If the river has flooding for at least 4 weeks a year, then a alluvial forest will appear in the flooding zone.” 3. The Inference Machine III Rules: Backward Chaining • Backward chaining is a proving method to check whether a certain goal is reachable, and which conditions must be satisfied in orderto reach it. Knowledge Modelling and Decision Support in the Water Domain

21. 3. The Inference Machine IV Bayesian Belief Network Since knowledge and data concerning natural processes includes many gaps or imprecise and faulty data we need methods to deal with them. The Bayesian Belief Network is a well known model to handle uncertainty and probability and to enable reasoning anyway. Knowledge Modelling and Decision Support in the Water Domain

22. 3. The Inference Machine V Coloured Petri Nets Modelling activities and processes is a very ambitious job in knowledge engineering. Several approaches have been tried to find proper way to describe procedures. In FLUMAGIS we will use one of the most famous models to implement activity simulations in the knowledge base: Coloured Petri Nets. Knowledge Modelling and Decision Support in the Water Domain

23. In the FLUMAGIS Causal Network all mentioned issues come together to provide reasoning about causes and effects in the water domain. Based on an ontology we establish a network of causal nodes, which are chained by certain relationships. The basic node class owns two properties to cover the relations “is caused by” and “has effects”. Using these reverse relations a net can be build up. 3. The Inference Machine VI The FLUMAGIS Causal Network I Knowledge Modelling and Decision Support in the Water Domain

24. Nodes can perform different tasks like representing deficits, potentials, actions, and aims. Working with ontologies allows to define special node types (classes) according to each task. Each node type inherits basic relations of the CausalNode class. 3. The Inference Machine VII The FLUMAGIS Causal Network II Knowledge Modelling and Decision Support in the Water Domain

25. Examples of Node Types: StatusNodes represent certain states of objects, identified by specified attribute values. ActionNodes aim at a desired state of objects. Furthermore they have a link to a certain Petri Net to execute the described action. 3. The Inference Machine VIII The FLUMAGIS Causal Network III Knowledge Modelling and Decision Support in the Water Domain

26. Thank you for listening patiently so far! Now let’s move to the platform we have chosen to build up the FLUMAGIS knowledge base: Protégé 2.0 an Open Source project of Stanford University, California. This software is totally free and extendable. We have created a lot of plug-ins to expand the program’s functionality. 3. The Inference Machine IX Protégé - An AI Tool for the Real World Knowledge Modelling and Decision Support in the Water Domain