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Unveiling Knowledge: From Data to Understanding

Explore the transition from raw data to refined knowledge leveraging ontology, epistemology, and logic, enhancing human cognition and fostering innovation. Enhance community access to knowledge through logical framework.

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Unveiling Knowledge: From Data to Understanding

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  1. From Data to Knowledge An Aspect of the NSF CDI Initiative CDI: Cyber-Enabled Discovery and Innovation

  2. 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

  3. 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

  4. Examples: From Data to Knowledge • Car Ads • Symbols: $, 12k, ford, 4-Door • Data: price(12k), mileage(12k), make(ford) • Conceptualized data: • Car(C123) has Price($12,000) • Car(C123) has Mileage(12,000) • Car(C123) has Make(Ford) • BodyTypeisa Feature • Car(C123) has Feature(Sedan) • Knowledge • Community agreement that the ontology is “correct” • Community agreement that the facts in the ontology are “correct” • Appointments • Biology

  5. Examples: From Data to Knowledge • Appointments • Biology

  6. Examples: From Data to Knowledge • Biology

  7. 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.

  8. 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

  9. A Web of Knowledge asSemantic-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

  10. 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++)

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