Chapter 7 K NOWLEDGE R EPRESENTATION, O NTOLOGICAL E NGINEERING, AND T OPIC M APS L EO O BRST AND H OWARD L IU

Preface Require human user ’ s semantic interpreter Need computer processing some of the semantic interpretation automatically Enabling the representation of semantic in ontology

Data, Knowledge, and Information Knowledge = Data + Interpretation New Knowledge = Old Knowledge + Information data: Relatively unstructured knowledge: very structured Interpretation continuum

Acquisition, Representation, and Manipulation Data source in database range from unstructured to structured The primary purpose of relational database is for storage and ease of access to data, not complex use High degree of interpretation needs software applications and human beings

Knowledge Representation Technology address the structure and meaning of knowledge Key issue of Knowledge Representation technology –Defining semantically rich and sufficiently correct model of knowledge domains –Representing and encoding knowledge in knowledge base efficiently and correctly –Defining access and inference/reasoning methods to best use the knowledge –Defining knowledge extraction, acquisition, and discovery methodologies –Defining mechanisms by which knowledge from disparate sources can be combined

Ontological Engineering Adding real content to the multimedia document involved in Web technology and information Content-based and human-meaningful data, and robust problem solving Human-meaningful categorization and annotation of Web pages ensures retrieval of relevant items

Ontology Formalization of a conceptualization Differs from other data model in that the relationship among entities uniformly, consistently, intuitively A human viewing an ontology can understand it directly Includes –Entities –the relationship between those entities –The properties(and property values) of those entities –The functions and processes involving those entities –Constraints on and rules about those entities

How Ontologies Relate to Topic Maps Topic map standard(ISO 13250) was defined to merge of different indexing schemes Syntactic interoperability Vs. Semantic interoperability If built from a sound ontology, topic map can provide semantic interoperability Ontological engineering applied to building conceptual design, and topic maps to reflect correctly the semantics of the underlying knowledge Ontology provides principle and guidelines to ensure reusability, robustness, and even a wide range of applications

How to Build an Ontology(1/2) 1. Define a universe of discourse (meaningful objects schematic portion – Author, Work data portion – Shakespeare, Hamlet) 2. List how the various things relate to each other (Author Write some Work) 3. Now build the ontology based on your analysis in step 1-2 by using knowledge representation engine(i.e Prot é g é )

How to Build an Ontology(2/2) sets/topoicset.iptc- topictype.xml#TopicTypes.Person = “ #written- by #author #shkespeare #work #hamlet

Ontology-Driven Topic Maps Topic map could be generated from ontologies Ontological engineering is a discipline to guide the design and construction of topic map We recommend ontology-driven topic map An explicit ontology be created, which in turns induces the necessary topic map

The Advantages of the Ontology-Driven Topic Maps Approach Offers the advantages of a typical loose coupling approach Ontology share & cost reduce Existing ontology guarantee well-form and out of bug

The Future of the Ontology-Driven Topic Maps Approach We hope that standard ontologies will become available for various domains of knowledge

Summary Knowledge Representation Ontological Engineering Ontology and Topic Maps