© 2006 Tom Beckman and Ronald P. Reck1 Issues in Knowledge Representation and Semantic Interoperability Tom Beckman Principal, Beckman Associates Ronald Reck Principal, RRecktek

© 2006 Tom Beckman and Ronald P. Reck2 Why is Knowledge Representation Important to Semantic Systems Provide systematic method to capture, define, and describe relevant semantics Provide explicit definition and description of Concepts, Attributes, Values, and Relationships Explicitly represent knowledge, experience, and expertise Determine the best Knowledge Representations to use depending on the task, environment, and user Employ appropriate knowledge structures and inference engines Select the best elicitation techniques to capture knowledge Compare and integrate similar concepts and data elements Determine the best level of granularity to describe data values Ensure proper consolidation of data elements across legacy systems Develop improved definition and description of concepts Provide improved data sharing, semantic interoperability, and search capability

© 2006 Tom Beckman and Ronald P. Reck3 Knowledge Representation Basics Knowledge Representation Characteristics:  Models knowledge and reasoning about knowledge  Describes characteristics and dimensions of knowledge  Formally defines structures and processes for electronic and human reasoning  Exposes Knowledge Structures and hides Inference Engines Symbolic Knowledge Representation Components:  Knowledge Structures:Objects DeclarativeStatic  Reasoning Mechanisms:Process ProceduralDynamic  Content and Context:Symbols SemanticsMeaning Explicit Representation of:  Knowledge  Experience  Expertise  Semantics of Content: Symbols, numbers, and language  Context: Concept relationships and features  Importance  Uncertainty

© 2006 Tom Beckman and Ronald P. Reck4 Knowledge Representation Dimensions Concept:  Symbolic Format:  Concept Types: Object, Entity, and Abstraction  Domain content knowledge Structure:  Declarative representation  Composed of Nodes and Links  Expert System types parallel human cognitive schema Process:  Procedural representation  Reasoning and Inference  Modeling and Simulation

© 2006 Tom Beckman and Ronald P. Reck5 Concept Definition Symbolic Format: Typing: Object, Entity, Abstraction, and Event Features:  Essential attributes that define the concept  Attributes that discriminate boundaries between concepts  Attribute Values also help define concept details Relations:  Typing: causal, taxonomy/hierarchy, associative  Between concepts  Between attributes

© 2006 Tom Beckman and Ronald P. Reck6 Concept Dimensions Meaning: is nothing more than the sum of these concept dimensions (after Bruner) Definition Attributes:  Stereotypical description of characteristics  Format: Relations:  Between concepts  Between attributes Linguistics:  Part of speech  Grammar rules Context:  Based on user experience and purpose  Common understanding Mental Models and Cognitive Schema

© 2006 Tom Beckman and Ronald P. Reck7 Attribute Value Typology Numeric:  Ordinal: Likert Scale  Interval: Range, Continuous Variable  Continuous: Ratio, normalized continuous variable Semantic:  Text Value Types:  Unstructured: Instant Messaging  Semi-Structured: , Memo  Structured: Document, Hypertext  Symbolic Value Types: Binary: Boolean Categorical: Unrelated Nominal Ordinal: Related Nominal Sensory:  Image: Digital spatial array, picture, video  Signal: Time series, audio, sensor

© 2006 Tom Beckman and Ronald P. Reck8 Attribute Value Typing Numeric:  Most precise reasoning  Needs explanation of computing and results  Often hidden – working in the background behind symbols and text Symbolic:  Less precise but most concise  Ease of reasoning and explanation Linguistic:  Best for explanation and understanding  Not as precise, and not concise  Hard to directly reason with – language parsers Image: Described and classified using taxonomies and metadata Signal: Time series are described, interpreted, and classified using taxonomies, metadata, and analysis engines

© 2006 Tom Beckman and Ronald P. Reck9 Conceptual Primitives Knowledge Templates are key conceptual primitives:  Represent assertions – the basic building blocks of structures  Define, describe, and detail symbol features, values, & relations  Can also represent Uncertainty & Importance  Come in several standard templates: Basic: Faceted: Measured: Declarative Conceptual Primitives:  Feature Descriptor: Ex:  Relation: Ex: Procedural Conceptual Primitives:  Action: Ex:  Inference: Ex: These knowledge elements have certain properties:  Naming (Object)  Describing (Attribute and Value)  Organizing (Hierarchy)  Relating (Functional, Causal, & Empirical Links)  Constraining and Negating (Networks and Rules)

© 2006 Tom Beckman and Ronald P. Reck10 Determining Concept Similarity (1) Compare potentially identical concepts across disparate legacy databases, documents, and Web sites Real understanding for potential data sharing comes from detailed examination and matching of concept characteristics Compare Symbol Names:  Identical or synonym  Degree of similarity as relative specificity Same Concept Type: Object, Entity, Abstraction, or Event Compare Concept Descriptions:  Natural language descriptions found in data dictionaries  Compare keywords and context in concept descriptions Compare Concept Relations:  Relation Typing: causal, taxonomy/hierarchy, associative  Relations between concepts and between attributes  Relative position of concept in taxonomy  Metadata Compare Concept Task Usage and Context

© 2006 Tom Beckman and Ronald P. Reck11 Determining Concept Similarity (2) At the data schema or logical level, concepts are described and defined as attributes/features and their allowable values Compare Concept Attributes (Data Elements):  Compare attribute names and determine similarity  Compare attribute descriptions in data schema  Determine relative importance of attributes  Number of essential common attributes between concept schema  Number of dissimilar attributes between schema  Look for compound data elements Compare Data Element Values:  Value descriptions found in data schema and data dictionaries  Compare keywords and context in attribute value descriptions  Compare Value Typing: numeric, symbolic, natural language, image/signal  Compare Value Characteristics: field length, valid numeric range, boundary values, metrics  Compare importance and uncertainty measures Compare Business Rules: Validity and consistency checks

© 2006 Tom Beckman and Ronald P. Reck12 Examples of Data Element Matching Ex 1: Comparing Address Zip Codes  Concept = Address  Attributes =  Attribute Value Options: = o Field Length: 5 digits, 9 digits (full Zip Code), 6 digits (Canadian) o Typing: US: numeric; Canadian/English: alpha and numeric o Valid Zip Code Values o Business Rule: If IRS Philadelphia SC, then Zip Code = Ex 2: Determining Suspect Height  Qualitative Approach: Height = o o Taller or shorter than witness or interviewer  Quantitative Approach: Height = o In inches; feet and inches; range estimate; o Valid range (for adult – context) = 30 to 90 inches

© 2006 Tom Beckman and Ronald P. Reck13 Capturing Accurate Data Values MIT Expert System story  Investment Expert System – Randy Davis suggestion  Qualitative values for economic attributes  rule explosion DOJ Teenage Drug Usage Survey  Monthly survey on frequency of teenage drug usage  During the past month, how often did you smoke weed?  Values =  Should have just captured an estimate of the number of times; later the data could be grouped as needed  As captured, unclear if data trends mean anything unless huge shift Data Accuracy Principle:  Capture numeric values when possible and relatively easy  Even qualitative data can be put on Likert value scale (1-7) Harmonizing values from varied data schema  When possible, estimate and convert qualitative values to numeric  Perhaps place new converted values in enterprise data warehouse

© 2006 Tom Beckman and Ronald P. Reck14 Issues in Semantic Systems No consolidation/harmonization of existing legacy databases:  Terms are semantically under-defined and under-described: must find and validate possible synonyms across disparate legacy DBs, metadata, and text/content.  Terms as related and organized into concept hierarchy, but more general or specific.  Multiple attributes and their values define and describe the concept and context.  Consolidate terms/concepts from disparate DBs: determine synonym, similarity, and context.  Consolidate attributes from disparate DBs: is the same feature being described?  Consolidate value typing and problematic conditions from disparate DBs Need improved description of concepts Need improved attribute typing and values Determine needed level of concept precision/granularity Creation of standard vocabularies Explicit representation of relationships, importance, and uncertainty Weak accuracy of matching and access function How to assert new facts Handling of Synonyms, Antonyms, Typing, and Metadata

© 2006 Tom Beckman and Ronald P. Reck15 Semantic Web Components Domain Content: Knowledge, experience, and expertise Domain Taxonomy and Ontology:  RDF/OWL  Object Methods: Inheritance and Classification Organization and Structure: Web sites and document collections Classification Methods:  Similarity-based  Rule-based  Network-based  Object-based Indexing: Item typing and meta-tagging Linguistics: Natural Language Processing & Text Generation Search Query: Keyword Bayesian Search & Semantic Search Entity Extraction: People, places, and events Analysis Methods: Data Mining, Text Mining, Link Analysis, Machine Learning, & Knowledge Discovery Intelligent Agents: Simulation and Modeling

© 2006 Tom Beckman and Ronald P. Reck16 Issues in Knowledge Representation and Semantic Interoperability Tom Beckman Principal, Beckman Associates Ronald P. Reck Principal, RRecktek Questions??