An Aspect of the NSF CDI InitiativeNSF CDI: Cyber-Enabled Discovery and Innovation

From Data to Knowledge: Leveraging Ontology, Epistemology, and Logic Definitions & examples of a way to “[enhance] human cognition and generating new knowledge from [the] wealth of heterogeneous digital data” on the web) A web of knowledge Community access to knowledge

Definitions: “From Data to Knowledge” Progression of terms: symbols, data, conceptualized data, knowledge Symbols: characters and character-string instances Data: symbols as values in attribute-value pairs Conceptualized data: data in the framework of a conceptual model Knowledge: conceptualized data with a degree of certainty or community agreement From Data to Knowledge Recognize symbols Classify symbols with respect to meta-data attributes Embed attribute-value pairs into a conceptual framework of concepts, relationships, and constraints Present for community approval or check with respect to community-approved knowledge or link to original source

Examples: From Data to Knowledge Car Ads Symbols: $, 12k, ford, 4-Door Data: price(12k), mileage(12k), make(ford) Conceptualized data: Car(C 123 ) has Price($12,000) Car(C 123 ) has Mileage(12,000) Car(C 123 ) has Make(Ford) BodyType isa Feature Car(C 123 ) has Feature(Sedan) Knowledge Community agreement that the ontology is “correct” Community agreement that the facts in the ontology are “correct” Appointments Biology

Examples: From Data to Knowledge Appointments Biology

Examples: From Data to Knowledge Biology

Definitions: “Ontology,” “Epistemology,” and “Logic” Ontology Existence  answers “What exists?” Computationally, it answers: what concepts, relationships, and constraints exist and how they are interrelated. Epistemology The nature of knowledge  answers: “What is knowledge?”, “How is knowledge acquired?”, “What do people know?” Computationally, it answers: what is knowledge (conceptualized data with community agreement). Logic Principles of valid inference—answers: “What can be inferred?” Computationally, it answers: what can be inferred (in a formal sense) from conceptualized data.

Examples: “Computational Answers” Ontology: What exists? In Car Ads: Car, Make, Model, Car has Make, Engine isa Feature In Appointments: Service Provider, Date, Appoint with Doctor In Biology: Protein Activity, Molecular Weight, Chromosome Location is aggregate of ChromosomeNumber and Start and End and Orientation Epistemology: What is knowledge? A fact-filled Biology ontology Chromosome Number (21) starts at Start (29,350,518) and ends at End (29,367,889) with Orientation(minus) How is it acquired? Creation of a fact-filled Biology ontology obtained from a reliable source Provenance: Was the source from which the Biology ontology was created reliable? What do people know? Does my knowledge that I have an appointment with Dr. Jones on Thursday align with the appointment ontology as established by the doctor’s office? I view the world with my car ads ontology  how does it align with the community standard ontology? Logic: Principles of valid inference Find red Nissans later than a 2002 with less than 100k miles In Appointments: can reason that a dermatologist is a medical service provider

A Web of Knowledge as Semantic-Web Pages Human-readable page (ordinary HTML, XML, …) One or more annotation attachments a reference to the ontology used for annotation queriable RDF triples of extracted information pointers into the original source for every item highlighting possibilities for extracted data hover possibilities to connect to the ontology

Community Access to Knowledge Access to knowledge  both ontological knowledge as well as facts. Ease of Use Free-form queries Form-based queries Scalability Semantic indexes Caching (on the scale of Google ++ )