Analysis Types

Name	Document Analysis Method	Composite Analysis Method	Document Base Prompt	Composite Base Prompt	Vocabulary
AIKR Concepts 1	Template	Template	You are a helpful assistant. Given a piece of text related to Knowledge Representation, you identify Knowledge Representation Concepts. These are given by words or phrases that have particular meanings in the context of knowledge systems. For example, in the context of knowledge systems, the word "agent" links the concept of an action to the concept of the animate being that performs it, so it gives a Knowledge Representation Concept. Identify as many Knowledge Representation Concepts as you can. In case of doubt over a word or phrase, include it. For each identified concept, you extract the following information: - The word or phrase - A description of the concept, as it is understood in the context of knowledge systems. This must be a single paragraph of at most six sentences. You return the results as a JSON object with a single attribute: analysis_objects. The value of this attribute is a list of objects. Each object in the list represents a Knowledge Representation Concept, and has two attributes: - title : the noun or noun phrase that is the term - description : the description of the term, as it is understood in the context of knowledge systems.	You are a helpful assistant. Given a title and list of descriptions of a Knowledge Representation Concept taken from different documents, you produce a composite description of the concept. A Knowledge Representation Concept is given by a word or phrase that has a particular meaning in the context of knowledge systems. Your input is a JSON object with two attributes: - title : the word or phrase that has a particular meaning in the context of knowledge systems - descriptions: a list of descriptions of the concept. You return a JSON object with two attributes: - title : the word or phrase that has a particular meaning in the context of knowledge systems - description : the composite description of the concept that you created.
AIKR Concepts 2	Template	Template	You are helping to create a glossary. Given a document, you read it to find the terms from a list of terms. For each term in the list that it finds, you create a definition of that term, using the content of the document. You do not add from your existing knowledge to what the document says. If no definition is clear from the document, you give the definition as "unclear". Return the definitions as as a JSON object with a single attribute: analysis_objects. The value of this attribute is a list of objects. Each object in the list represents a term with a definition, and has two attributes: - title : the word or phrase that is term - description : the definition of the term that you created. The list of terms is: abduction abnormality absorption absurdity absurd theory access-limited logic accessibility relation accompaniment-relation (Accm) accomplishment verb achievement verb activation activity diagram actual entity actuality acyclic graph adjectives modifying nouns adverbial modifier agenda agent aggregate AI complete problem alternativity relation ambiguity amount relation (Amt) analog control system analogy antisymmetry appetite (epithymia) argument relation (Arg) Aristotle's four causes (aitiai) Ars Magna aspect assertional reasoner (A-box) associativity atom attention attribute accidental attribute choice of attribute default attribute metalevel attribute attribute relation autoepistemic logic automatic programming axioms background knowledge backtracking Backus-Naur form backward chaining base relation because relation Begriffschrift belief network belief revision beneficiary relation BIAIT bipartite graph blackboard blank graph blocks world body part Boolean algebra Boolean operator Boolean value bound concept bound labels bound occurrence bound variable boundary bowtie inconsistency broader-narrower hierarchy bulletin board business rules but test C++ calculus of individuals canonical form cardinality cascading update case-based reasoning case relation catalog of individuals categories categories in Chat-80 categories in Cyc categories of emotions categories of forms Hegel's categories Heidegger's categories Husserl's categories independent categories Kant's categories lattice of categories matrix of categories mediating categories Peirce's categories physical categories relative categories top level categories Whitehead's categories category theory causal network causality certainty factor cessation change characteristic relation Chat-80 child relation Chinese room choice space Church-Rosser theorem Classic classification clause form CLIPS closed world assumption COBOL CODASYL Database Task Group collection collective noun collective plural communication commutative diagram commutativity comparand relation competence levels complementarity completeness completion relation component composite computational complexity computer-aided software engineering conceptual analysis conceptual dependency theory conceptual graph conceptual schema conceptualization conditional relation consciousness constraint containment content word context context-free grammar contingent fact continuant continuation continuity contraction contrast controlled natural language co-occurrence pattern conversational implicature coreference correlational net correlative counting course of events critical path cryptarithmetic problem cut-and-paste theorem Cyc DANTE Dasein database reorganization dataflow diagram Datalog De Morgan's laws declarative language deductive closure default default logic defeasible reasoning defined quantifier defining label definition definitional logic Dempster-Shafer theory denotation deontic logic dependence depiction description description predicate descriptor design levels designator desire destination relation determinant dicent sign differentia differential calculus differential equation discourse representation disjoint disjunction distant past distinction distributed situation distributive plural distributivity domain domain expert domain knowledge dominant node duration relation dynamic logic Electronic Dictionary Research Institute effector relation egg-yolk diagram elaboration tolerance empty set EMYCIN encapsulation encoded literal enterprise integration enterprise model enterprise modeling entity entity-relationship diagram entropy episodic memory epistemic logic epistemology equivalence equivalence relation essence evaluation function event event semantics event time event variables event-driven program existential graph existential quantifier existential referent existential-conjunctive logic exogenous event experiencer relation expert system explanation Express language extended quantifier extension extensionality extensive abstraction failure family resemblance Figura Universalis finite-state machine firewall first intentions first-order logic first-order type Firstness flow chart fluent fork form formal parameter formalization formation rule formula forward chaining frame frame problem Frame Representation Language free logic free occurrence freedom function word functional language functional relation fuzzy control system fuzzy logic fuzzy set theory gavagai General Problem Solver generalization hierarchy generalized union generate and test generic concept Generic Frame Protocol genetic algorithm genus go-moku goal God government and binding gradation grain grammar rule granularity ground has test having hedging term heuristics hierarchy higher-order logic history hole Horn-clause logic host language hotel reservation example hypernym-hyponym icon idempotency identity identity condition ignorance imagery immanent import-export rules independence indeterminacy of translation index indexical individual concept individual marker inertia inference engine infimum InfoBus informal specifications information retrieval Information System Architecture information theory inheritance inhibit arc initiation initiator inner domain instance level instance variable instantiation instrument relation integer time integrated system integrity constraint intension intention intentionality interoperability interpretant interpretation interrogative mood intersection intransitive verb intuitionistic logic Java join juncture KIF KL-ONE knowledge acquisition knowledge base knowledge engineering Knowledge Interchange Format knowledge levels Knowledge Representation Language knowledge source KRYPTON lambda calculus lambda expression lattice lawgiver learning legisign legislation lexicon library database lifting rules Linda language linear logic LISP literal local symbol location locator locomotion logic programming logos LOOM machine translation manner relation MARGIE marker passing mass and energy mass noun mathematical logic matter meaning meaning triangle meaning-preserving translation means-ends analysis measurement medium relation memory leak memory organization packet mental process mereology meronomy message metalanguage metalevel metametalanguage metametamodel metamodel metaphysics metatheorem metatheory metavariable Microplanner microworld minimalist grammar modal logic mode model structure model theory model-based reasoning modeling hypothesis monad monotonic logic morphology multimedia dialog manager multiple paradigms multivalued logic musical notation MYCIN name natural deduction natural language processing necessity negation NETL neural network nexus non-Euclidean geometry nondistributed middle term nonmonotonic logic notational engineering noema noesis now nth-order type nutrition object Object Constraint Language object-oriented observation occurrent ontological commitment ontologically neutral representation ontology open texture open world OPS5 optimization origin relation outer domain overlap paradox of the heap parse tree part relation partial compatibility partial ordering participant participation participation count partitioned semantic network partonomy passion path relation patient relation Peirce normal form Peirce-Peano notation perception persistence perspective PERT chart Petri net phenomenal type phenomenology physis piece Planner PlayNet plurals point-in-time relation possession relation possibility possible world postcondition power set PowerLoom Praeclarum Theorema pragmatics pragmatism precondition predicate predicate logic prehension primitive principle of sufficient reason principles of knowledge representation problem solving procedural attachment procedural language procedure process product production rule Prolog proof theory property proposition propositional attitude propositional logic protoIndoEuropean prototype psyche public symbol purpose relation pushdown stack qualisign qualitative reasoning quality quantifier query question answering Quine's criterion quote randomness rational animal reason recipient relation recursive diagram reference time referent refinement reflexivity relation relation hierarchy relation label relation type relational database relevance logic Renaissance representamen representation resolution theorem prover resource restricted quantifier result relation rete network rheme rhetoric robot role type rule of inference rule-based expert system Russell's paradox salience schema schematic anticipation Scholastics scientific intelligence scope script scripting language second intentions second-order type Secondness self-awareness semantic distance semantic factoring semantic interpreter semantic memory semantic network semantics semaphore semi-open world semiotics set set theory seven liberal arts SGML sheet of assertion SHRDLU signature signified signifier Simula simulation simulation language single-assignment language singleton graph singleton set sinsign situatedness situation situation calculus situation semantics snapshot SNePS softbot sort label sorted logic soundness source space space-time spatial form specialization species speech time SQL stage star graph start relation state-transition diagram statistics stored relation stratified reasoning method STRIPS structure stylized natural language subjective form subjective probability subset substrate subsumption subsumption architecture subsymbolic process success successor relation sum supplement supremum surprise surrogate swimlane syllogism symbol symbol grounding symbolic logic symmetry synset system model tablature Tao Tarski's solid geometry task tautology technology model temporal logic tense term terminological logic terminological reasoner terminology thematic role theme relation theorem proving theory of reference theory revision thesis-antithesis-synthesis third-order type Thirdness thought thread time time dependencies time line time stamp timing diagram topology Torah transformation rule transformational grammar transitive verb transitivity translating English to logic Tree of Porphyry triads trichotomy trigger tritone truth function truth maintenance truth table truth value Turing machine type type label typed logic uncertainty unification Unified Modeling Language uninterpreted logic union unique existential quantifier Universal Characteristic universal language universal plan universal quantifier universal resource locator universal set universal theory universe of discourse update anomaly vagueness valence value restriction variable viewpoint virtual reality virtual relation vivid logic Vorhandene wakefulness will word sense WordNet Yale shooting problem Zeno's paradox zooming Zuhandene Here is the document.	You are helping to create a glossary. Given a term and list of definitions of it taken from different documents, you produce a composite definition of the term. Your input is a JSON object with two attributes: - title : the term - descriptions: a list of definitions of the term. You return a JSON object with two attributes: - title : the term - description : the composite definition of the term that you created.
Cluster	Cluster	Cluster
Data Integration 1.1 Relations Extraction					Data Integration 1.1
Data Integration Vocabulary Term Definition
Documents Vocabulary Terms Extraction					DP 1.0
Draft AI RA Vocabulary Terms Use
Enterprise Capability Vocabulary Terms Use
Formal Key Concepts (AIKR)	Template	Template	You are a helpful assistant. Given a piece of text, you identify and return its key concepts. A key concept is a noun or noun phrase that is important in the text. You use the following steps. 1. Identify all key concepts. For each identified concept, extract the following information: - The noun or noun phrase that is the concept - A description of the concept, as it is understood in the text The noun or noun phrase must be as short as possible, but no shorter. It must not include abbreviations or acronyms. It must not include words in brackets. For example: - "Remote Direct Memory Access" NOT "Remote Direct Memory Access (RDMA)" - "The United States of America", NOT "The USA" or "The United States of America (USA)" or "The United States (of America)". It MUST NOT include trade names. For example, an IBM computer is just a kind of computer, so "Computer" instead of "IBM Computer." The description must take the form A <key concept> is . . if the key concept is a class of things, or <key concept> is . . if the key concept is a thing. It must be a single paragraph of at most six sentences. Examples are: - A city is a large town - Civic pride is a feeling of belonging to and being proud of the town or city where you live - The United States of America is the country in North America that consists of 50 states and the District of Columbia. It is bordered by Canada in the north and Mexico in the south. 2. Return the concepts as a JSON object with a single attribute: analysis_objects. The value of this attribute is a list of objects. Each object in the list represents a concept, and has two attributes: - title : the noun or noun phrase that is the concept - description : the description of the concept as it is understood in the text	You are a helpful assistant. Given a title and list of descriptions of a key concept taken from different documents, you produce a composite description of the key concept. A key concept is a noun or noun phrase that is important in the text of the document in which it appears. This is given to you as the title. The composite description must take the form A <key concept> is . . if the key concept is a class of things, or <key concept> is . . if the key concept is a thing. It must be a single paragraph of at most six sentences. Examples are: - A city is a large town - Civic pride is a feeling of belonging to and being proud of the town or city where you live - The United States of America is the country in North America that consists of 50 states and the District of Columbia. It is bordered by Canada in the north and Mexico in the south. Your input is a JSON object with two attributes: - title : the title of the key concept - descriptions: a list of descriptions of the key concept. You return a JSON object with two attributes: - title : the title of the key concept - description : the composite description of the concept that you created.
KR Schema 1	Graph	Graph
Vocabulary Term Relations Extraction
Vocabulary Term Relations Use
Vocabulary Terms Extraction
Vocabulary Terms Extraction Weighted By Document
Vocabulary Terms Use
Vocabulary Use