Artificial intelligence project CYC

CONTENTS OBJECTIVE +WHAT IS CYC ? +WHAT DOES DO ? CYC Architecture Knowledge Base +Constants +Individuals +Collection +Truth Function +Functions Inference Engine +How it works Natural Language System CYCL-Representation Language Mapping Ontology into CYCL

OBJECTIVE CYC attempts to assemble an comprehensive ontology and Knowledge Base of common sense knowledge. This Helps AI projects to do human like reasoning The objective was to codify, in machine-usable form, millions of pieces of knowledge that comprise human common sense. Example: “Every Tree is a Plant” && “Plants eventually die” from which we can infer “All trees Die”. (note: the first two phrases are the things present in the normal database)

CYC ARCHITECTURE

CYC Said!!! CYC Foundation: “Imagine a world in which every single person is given free access to programs that reason with the sum of all human knowledge. That's what we're doing.”

KNOWLEDGE BASE

KNOWLEDGE BASE cont… The Knowledge Base (KB) is a collection of over one million human-defined assertions, rules or common sense ideas. The concept names in CYC are known as constants. Individuals: Individual items known as individuals, such as #$BillClinton or #$France. Collections, such as #$Tree-ThePlant (containing all trees) or #$EquivalenceRelation (containing all equivalence relations). A member of a collection is called an instance of that collection.  Truth Functions which can be applied to one or more other concepts and return either true or false.

KNOWLEDGE BASE cont.. EXAMPLE: #$siblings is the sibling relationship, true if the two arguments are siblings. By convention, truth function constants start with a lower-case letter. Functions, This produce new terms from given ones. #$FruitFn, when provided with an argument describing a type (or collection) of plants, will return the collection of its fruits. By convention, function constants start with an upper-case letter and end with the string "Fn". The Cyc KB is divided into many (currently thousands of) "microtheories", each of which is essentially a bundle of assertions that share a common set of assumptions

Expert System

INFERENCE ENGINE An Inference engine is a computer program that tries to derive answers from a knowledge base.  The CYC inference engine performs general logical deduction (including modus ponens, modus tollens, and universal and existential quantification)

NATURAL LANGUAGE SYSTEM The CYC-NL system can be described in terms of its three primary components, although in truth there are many other utilities that contribute to its success. The three main components are the Lexicon: The lexicon is the backbone of the NL system. It contains syntactic and semantic information about English words. Each word is represented as a Cyc constant. Example: the constant#$Light-TheWord is used to represent the English word "light".

NLP cont.. The syntactic parser --The syntactic parser utilizes a phrase-structure grammar loosely based on Government and Binding principles. --Using a number of context-free rules, the parser builds tree-structures, bottom-up, over the input string. --The parser outputs all trees allowed by the rule system, so multiple parses are possible in cases of syntactic ambiguity.

NLP cont.. The semantic interpreter. Cyc-NL's semantic component transforms syntactic parser into CycL formulas. The output of the semantic component is "pure" CycL: a parsed sentence can immediately be asserted into the KB, for example, or a parsed question can be presented to the SQL generator in order to pose a database query.

Populating CYC from web Knowledge Users User Interface (with Natural Language Dialog) Knowledge Authors Knowledge Entry Tools Cyc API Cyc Reasoning Modules Cyc Ontology & Knowledge Base Interface to External Data Sources External Data Sources Data Bases Web Pages Text Sources Other KBs

Populating CYC from web Choosing a query: Because the number of concepts in the KB is so large, the number of possible CycL queries is enormous; choosing interesting, productive queries auto-matically is a necessary step in automating the knowledge acquisition process. An example of such a query might be:   Searching: Once a query is selected, it is translated into one or more English search strings. The query above might be rendered into strings such as: 3. Parsing results: The relevant components of sentences are identified by their location relative to the search string. The terms are then parsed into CycL via the natural lan-guage parsing process described in section 3.3, resulting in one or more GAFs such as: KB consistency checking: Some of the results retrieved during the search process are disprovable, because they are inconsistent with knowledge already present in the knowl-edge base; others are already known or trivially provable, and therefore redundant. Any GAF found via inference to be inconsistent or redundant is discarded.

Open CYC and Research CYC Open CYC is a open source part of the original CYC Knowledge base The original Knowledge base is Proprietary. But later an upgraded version of the Open CYC was released with the entire Knowledge base. More recently, CYC has been made available to AI researchers under a research-purposes license as Research CYC

Recent Application Terrorism Knowledge Base The comprehensive Terrorism Knowledge Base is an application of cyc in development that will try to ultimately contain all relevant knowledge about terrorist groups, their members, leaders, ideology, founders, sponsors, affiliations, facilities, locations, finances, capabilities, intentions, behaviors, tactics, and full descriptions of specific terrorist events. The knowledge is stored as statements in mathematical logic, suitable for computer understanding and reasoning. Cyclopedia Cyclopedia is being developed that superimposes cyc keywords on pages taken from Wikipedia pages.

Searching for Common Sense: Populating Cyc™ from the Web Reference http://www.cyc.com http://en.wikipedia.org Searching for Common Sense: Populating Cyc™ from the Web

Thank You - Regards HariHaran