Tutorial by Mike Bennett
Date: Tuesday 18 September 2018
JOWO (at FOIS), Cape Town
Please join us for a one-day tutorial with Mike Bennett on conceptual ontology engineering. In this tutorial you will learn how to model the knowledge of an organization in technology-neutral business language and frame this as a formal ontology. This tutorial teaches a range of techniques that focus on concepts to articulate the formal semantics of business information. Learn how to use high level abstractions to differentiate between concepts and how to classify these. You will also learn how these kinds of models can be put to work in addressing common data modeling problems, reducing integration costs and revealing new insights from data. If you work with data architecture, legal or regulatory technology or new technologies such as micro-finance, big data, Blockchain, Internet of Things or data visualization, this tutorial will have something of interest to you.
This tutorial sets out the basic principles of concept modeling, situating these kinds of models within a broader modeling framework. You will learn how to frame this kind of ontology artifact, how to think in terms of concepts and how to define these in formal logic. The bulk of this tutorial focuses on conceptual issues: understanding concepts, classification theory and powertypes, the use of formal logics in ontology development and issues relating to terminology and vocabulary. You will learn ontology development techniques such as the use of upper ontologies to provide disambiguation of similar concepts and how these abstractions address common data problems. Specific examples of upper and cross-domain ontologies are covered in depth, including contextually defined concepts (roles etc.), event and process modeling, contracts and transactions. The course concludes by identifying the range of ways in which conceptual ontologies may be used in various practical deployment architectures and how to use or extend popular ontologies such as the Financial Industry business Ontology (FIBO), with examples. No prior knowledge of ontology modeling is required.
By the end of this tutorial you should be able to create your own conceptual ontologies and understand how the use of ontologies as conceptual models can enhance software development and cut integration costs. You will also understand how to derive technical artifacts from these for data integration and model driven development, as well as pragmatic, operational ontologies for semantically enabled reporting and inference processing applications.
This course is aimed at anyone interested in data modeling, knowledge representation and systems development, including students, researchers, data architects and business analysts. It is relevant to anyone exploring the use of formal ontologies for a range of different application areas, particularly in emerging technology areas such as micro-finance, distributed ledger technology (AKA Blockchain), big data, machine learning and the Internet of Things (IoT). No prior knowledge of ontology modeling or standards is required. Some basic knowledge of information technology is assumed, including familiarity with the technology development lifecycle, but no prior knowledge of any language for programming, databases or modeling is assumed.
Introduction: Concepts and words; the data development lifecycle; use of computationally independent models. Introducing ontology: a conceptual model for data and beyond.
Modeling Semantics: principles of semantic modeling, illustrated with a rolling example. Defining concepts. Classification and taxonomy, properties, the differentiating characteristics of concepts; understanding formal logic and set theory. Formal semantics basics representation of classes, properties and logical restrictions.
Conceptual Issues: Anatomy of a Concept; words, concepts and lexical space. Homonyms, heteronyms and some strange habits of words. Concepts without words. Different approaches to formal semantics.
Classification principles: kinds of taxonomy; subsumption based taxonomies; faceted classification; powertypes and kinds of individuals.
Introducing Data: Distinguishing things from data about things; semantic truth-makers versus data; real things that are data; establishing data applicability (semantic distance) for a given type of ontology; datatype properties in ontologies; information kinds and the use of a values ontology.
Top Level (Upper) and Cross Domain Ontologies: Why upper ontology? Understanding existing top level ontologies and standards. Semantic abstraction and re-use; dimensions of a top level ontology. Some popular top level ontologies. Things defined by their context; things that happen; other partitioning considerations. Realism versus concept-centric ontology.
TLOs In Depth: Contextual Things Deep dive session on things in roles and other contextual matter. Different conceptualization options for roles and relators. Examples of these using customer and counterparty data modeling issues.
TLOs In Depth: Things that Happen Deep dive session on continuant and occurrent things (endurants and perdurants). Classifying kinds of occurrent. Different conceptualization options for things that ought to happen or might happen. The semantics of plans and risks. Modeling business processes as ontology.
Recommended Mid-level Ontologies: Authoritative Sources of Meaning: identifying meaningful published concept definitions and adapting these into the ontology framework using TLO (with examples). The REA ontology for transactions; LKIF and other legal ontologies; ontologies for business process and other common problem areas.
Conceptual Ontology Development: Framing Simplest kind of Thing concepts (archetypes); top down, bottom up and middle out ontology development; the use of the wire frame upper ontology for pragmatic conceptual ontology development.
Putting it to Work: Business concept ontologies versus application ontologies. Introducing the Financial Industry Business Ontology (FIBOTM) Standard. Putting these to use: mapping, reporting, inference processing, Blockchain, graph analytics, machine learning, legal and regulatory (RegTech) and novel finance and micro-finance opportunities (FinTech). Styles of ontology for different ontology uses (with examples). Getting to there: a roadmap for ontological maturity.