MODEL THEORY FOR DUMMIES Basic idea is to give a mathematical characterization of a ‘world’ in just enough detail to assign a meaning to every expression.

Slides:



Advertisements
Similar presentations
Computer Science CPSC 322 Lecture 25 Top Down Proof Procedure (Ch 5.2.2)
Advertisements

Basic Structures: Sets, Functions, Sequences, Sums, and Matrices
Instructor: Hayk Melikya
Basic Structures: Sets, Functions, Sequences, Sums, and Matrices
Review Test 5 You need to know: How to symbolize sentences that include quantifiers of overlapping scope Definitions: Quantificational truth, falsity and.
Search in the semantic domain. Some definitions atomic formula: smallest formula possible (no sub- formulas) literal: atomic formula or negation of an.
Last time Proof-system search ( ` ) Interpretation search ( ² ) Quantifiers Equality Decision procedures Induction Cross-cutting aspectsMain search strategy.
Predicate Calculus.
1 Relational Algebra and Calculus Yanlei Diao UMass Amherst Feb 1, 2007 Slides Courtesy of R. Ramakrishnan and J. Gehrke.
Logical and Rule-Based Reasoning Part I. Logical Models and Reasoning Big Question: Do people think logically?
Ofer Strichman, Technion Deciding Combined Theories.
RDF Semantics by Patrick Hayes W3C Recommendation Presented by Jie Bao RPI Sept 4, 2008 Part 1 of RDF/OWL Semantics Tutorial.
22 March 2009Instructor: Tasneem Darwish1 University of Palestine Faculty of Applied Engineering and Urban Planning Software Engineering Department Formal.
Knowledge Interchange Format Michael Gruninger National Institute of Standards and Technology
LDK R Logics for Data and Knowledge Representation Context Logic Originally by Alessandro Agostini and Fausto Giunchiglia Modified by Fausto Giunchiglia,
A Brief Summary for Exam 1 Subject Topics Propositional Logic (sections 1.1, 1.2) –Propositions Statement, Truth value, Proposition, Propositional symbol,
Atomic Sentences Chapter 1 Language, Proof and Logic.
1 Knowledge Based Systems (CM0377) Lecture 4 (Last modified 5th February 2001)
LDK R Logics for Data and Knowledge Representation ClassL (part 3): Reasoning with an ABox 1.
CS 103 Discrete Structures Lecture 10 Basic Structures: Sets (1)
1 Logical Agents CS 171/271 (Chapter 7) Some text and images in these slides were drawn from Russel & Norvig’s published material.
CS344: Introduction to Artificial Intelligence Lecture: Herbrand’s Theorem Proving satisfiability of logic formulae using semantic trees (from Symbolic.
Chapter 3 RDF and RDFS Semantics. Introduction RDF has a very simple data model But it is quite liberal in what you can say Semantics can be given using.
1 Relational Algebra and Calculas Chapter 4, Part A.
LDK R Logics for Data and Knowledge Representation PL of Classes.
Albert Gatt LIN3021 Formal Semantics Lecture 4. In this lecture Compositionality in Natural Langauge revisited: The role of types The typed lambda calculus.
Language: Set of Strings
Key Concepts Representation Inference Semantics Discourse Pragmatics Computation.
CompSci 102 Discrete Math for Computer Science
1 Logical Agents CS 171/271 (Chapter 7) Some text and images in these slides were drawn from Russel & Norvig’s published material.
Copyright © Cengage Learning. All rights reserved.
Section 2.1. Section Summary Definition of sets Describing Sets Roster Method Set-Builder Notation Some Important Sets in Mathematics Empty Set and Universal.
CS6133 Software Specification and Verification
Mathematical Preliminaries
2.1 Sets 2.2 Set Operations –Set Operations –Venn Diagrams –Set Identities –Union and Intersection of Indexed Collections 2.3 Functions 2.4 Sequences and.
For Wednesday Read chapter 9, sections 1-3 Homework: –Chapter 7, exercises 8 and 9.
Chapter 2 With Question/Answer Animations. Section 2.1.
LDK R Logics for Data and Knowledge Representation ClassL (Propositional Description Logic with Individuals) 1.
1 CSC384: Intro to Artificial Intelligence Lecture 5.  Knowledge Representation.
1 First order theories (Chapter 1, Sections 1.4 – 1.5) From the slides for the book “Decision procedures” by D.Kroening and O.Strichman.
ece 627 intelligent web: ontology and beyond
Inference in First Order Logic. Outline Reducing first order inference to propositional inference Unification Generalized Modus Ponens Forward and backward.
Daniel Kroening and Ofer Strichman Decision Procedures An Algorithmic Point of View Deciding Combined Theories.
LDK R Logics for Data and Knowledge Representation Propositional Logic Originally by Alessandro Agostini and Fausto Giunchiglia Modified by Fausto Giunchiglia,
Albert Gatt LIN3021 Formal Semantics Lecture 3. Aims This lecture is divided into two parts: 1. We make our first attempts at formalising the notion of.
First-Order Logic Semantics Reading: Chapter 8, , FOL Syntax and Semantics read: FOL Knowledge Engineering read: FOL.
Logics for Data and Knowledge Representation ClassL (part 1): syntax and semantics.
Formal Semantics Purpose: formalize correct reasoning.
LDK R Logics for Data and Knowledge Representation Description Logics: family of languages.
Metalogic Soundness and Completeness. Two Notions of Logical Consequence Validity: If the premises are true, then the conclusion must be true. Provability:
1 Section 7.1 First-Order Predicate Calculus Predicate calculus studies the internal structure of sentences where subjects are applied to predicates existentially.
Logics for Data and Knowledge Representation ClassL (part 1): syntax and semantics.
Chapter 2 1. Chapter Summary Sets (This Slide) The Language of Sets - Sec 2.1 – Lecture 8 Set Operations and Set Identities - Sec 2.2 – Lecture 9 Functions.
Linked Data & Semantic Web Technology The Semantic Web Part 7. RDF Semantics Dr. Myungjin Lee.
Announcements  Upcoming due dates  Thursday 10/1 in class Midterm  Coverage: everything in lecture and readings except first-order logic; NOT probability.
Artificial Intelligence Logical Agents Chapter 7.
Introduction to Set Theory (§1.6) A set is a new type of structure, representing an unordered collection (group, plurality) of zero or more distinct (different)
Semantics In propositional logic, we associate atoms with propositions about the world. We specify the semantics of our logic, giving it a “meaning”. Such.
Logics for Data and Knowledge Representation
A Brief Summary for Exam 1
MA/CSSE 474 More Math Review Theory of Computation
Chapter 3 RDF and RDFS Semantics
Relational Logic Semantics
Knowledge Representation I (Propositional Logic)
Herbrand Logic Semantics
Logics for Data and Knowledge Representation
Relational Logic Semantics
Logical and Rule-Based Reasoning Part I
Herbrand Semantics Computational Logic Lecture 15
Representations & Reasoning Systems (RRS) (2.2)
Presentation transcript:

MODEL THEORY FOR DUMMIES Basic idea is to give a mathematical characterization of a ‘world’ in just enough detail to assign a meaning to every expression.

MODEL THEORY FOR DUMMIES Basic idea is to give a mathematical characterization of a ‘world’ in just enough detail to assign a meaning to every expression. Exactly what counts as ‘just enough’ depends on the language.

MODEL THEORY FOR DUMMIES Basic idea is to give a mathematical characterization of a ‘world’ in just enough detail to assign a meaning to every expression. Exactly what counts as ‘just enough’ depends on the language. Eg. for propositional logic, all you need to know is the truthvalues of the proposition letters, so an interpretation (“possible world”) is a truth-assignment to the proposition letters.

MODEL THEORY FOR DUMMIES Basic idea is to give a mathematical characterization of a ‘world’ in just enough detail to assign a meaning to every expression. Exactly what counts as ‘just enough’ depends on the language. Eg. for propositional logic, all you need to know is the truthvalues of the proposition letters, so an interpretation (“possible world”) is a truth-assignment to the proposition letters. For first-order logic, you need to know 1.what the quantifiers range over (the universe); 2.for each name, what thing it names; 3.for each relation symbol, what combinations of things make it true (a set of n-tuples).

MODEL THEORY FOR DUMMIES Basic idea is to give a mathematical characterization of a ‘world’ in just enough detail to assign a meaning to every expression. Exactly what counts as ‘just enough’ depends on the language. Eg. for propositional logic, all you need to know is the truthvalues of the proposition letters, so an interpretation (“possible world”) is a truth-assignment to the proposition letters. For first-order logic, you need to know 1.what the quantifiers range over (the universe); 2.for each name, what thing it names; 3.for each relation symbol, what combinations of things make it true (a set of n-tuples). For maps, you need to know the topological structure of the terrain, the projection function, and for each map symbol, what property of the terrain region it indicates.

MODEL THEORY FOR DUMMIES Basic idea is to give a mathematical characterization of a ‘world’ in just enough detail to assign a meaning to every expression. Exactly what counts as ‘just enough’ depends on the language. Key point is that MT makes only the assumptions about The World that are needed to determine truthvalues, and they must be expressible mathematically, ie ‘structurally’. Model theory is metaphysically neutral. - eg first-order MT doesn’t claim that relations *are* sets of n-tuples; it just says: whatever relations *really are*, all I need to know about them is which n-tuples they are true of.

MODEL THEORY FOR DUMMIES Basic idea is to give a mathematical characterization of a ‘world’ in just enough detail to assign a meaning to every expression. Exactly what counts as ‘just enough’ depends on the language. Key point is that MT makes only the assumptions about The World that are needed to determine truthvalues, and they must be expressible mathematically, ie ‘structurally’. Model theory is metaphysically neutral radically agnostic. Eg first-order MT makes no assumptions about what is in the universe.

An Interpretation is: 0. A set LV and a mapping XL from to LV 1. A nonempty set IR of resources, called the domain or universe of I. 2. A non-empty subset IP of IR called Properties 3. A mapping IEXT from IP into the powerset of IRx(IR union LV) 4. A mapping IS: vocab(I) -> IR Basic RDF model theory

An Interpretation is: 0. A set LV and a mapping XL from to LV 1. A nonempty set IR of resources, called the domain or universe of I. 2. A non-empty subset IP of IR called Properties 3. A mapping IEXT from IP into the powerset of IRx(IR union LV) 4. A mapping IS: vocab(I) -> IR Basic RDF model theory IR could overlap LV i.e. a subset of pairs with x in IR and y in either IR or LV. This is basically a table of the values of this property for each object. If some object isn’t mentioned then it doesn’t have a value for this property IS assigns semantic values to a subset of the total RDF vocabulary.

An Interpretation is: 0. A set LV and a mapping XL from to LV 1. A nonempty set IR of resources, called the domain or universe of I. 2. A non-empty subset IP of IR called Properties 3. A mapping IEXT from IP into the powerset of IRx(IR union LV) 4. A mapping IS: vocab(I) -> IR Which satisfies: if E is a or then I(E) = IS(E) if E is a then I(E) = LV(E) if E is an asserted triple with the form s p o then I(E) = true iff is in IEXT(I(p)), otherwise I(E)= false. if E is a set of triples then I(E) = false just in case I(E') = false for some asserted triple E' in E, otherwise I(E) = true. if E is an then I(E) = true if I[A](set(E))=true for some A defined on anon(E), otherwise I(E)= false Basic RDF model theory

An Interpretation is: 0. A set LV and a mapping XL from to LV 1. A nonempty set IR of resources, called the domain or universe of I. 2. A non-empty subset IP of IR called Properties 3. A mapping IEXT from IP into the powerset of IRx(IR union LV) 4. A mapping IS: vocab(I) -> IR Which satisfies: if E is a or then I(E) = IS(E) if E is a then I(E) = LV(E) if E is an asserted triple with the form s p o then I(E) = true iff is in IEXT(I(p)), otherwise I(E)= false. if E is a set of triples then I(E) = false just in case I(E') = false for some asserted triple E' in E, otherwise I(E) = true. if E is an then I(E) = true if I[A](set(E))=true for some A defined on anon(E), otherwise I(E)= false Basic RDF model theory This set is probably finite

An example vocabulary(I) : Red dc:creator Ron _:someone _:something P C Universe (IR): { } P { } IEXT P is a property which assigns values to two things in the universe. There are 5 things in the universe. IEXT(P) shows what the values of P are for everything it is defined on. IS IS assigns a value for each name in the vocabulary One of them doesn’t have a name

An example vocabulary(I) : Red dc:creator Ron _:someone _:something P C Universe (IR): { } P { } IEXT There are 5 things in the universe. IS One of them doesn’t have a name I( Red dc:creator Ron) = true because is and is in IEXT( P ) which is IEXT(I(dc:creator)) I(Red dc:creator _:something) = false because is not in IEXT(I(dc:creator)) I([Red dc:creator _:something]) = true because there is a mapping A: _:something and is in IEXT(I(dc:creator)) [square brackets] are being used to indicate that this is a document rather than just a set of triples.

A note on IEXT It would be simpler to just say that I(p) is a subset of IR x (IR union LV), and write if E is an asserted triple with the form s p o then I(E) = true iff is in I(p), …… rather than IEXT(I(p))….. Why bother with IEXT?

A note on IEXT It would be simpler to just say that I(p) is a subset of IR x (IR union LV), and write if E is an asserted triple with the form s p o then I(E) = true iff is in I(p), …… rather than IEXT(I(p))….. Why bother with IEXT? Because we might want to interpret a triple like [a a b]. Suppose I(a) was a set of pairs, then how could that set itself be inside one of the pairs in the set? That would violate the axiom of foundation (a basic axiom of Zermelo-Fraenkel set theory). We could use a nonstandard set theory that allows non-well-founded sets, but that would be a radical move….The use of IEXT is a less controversial alternative. ‘foo’a { } I IEXT a appears in its own extension, which is fine.

I satisfies E means I(E)=true E entails E’ means any I which satisfies E also satisfies E’ Some Lemmas … 1. Any RDF expression has a satisfying interpretation (is consistent). [Herbrand] 2. If I satisfies all the triples in a document, then it satisfies the document. 3. If E and E’ are sets of triples, then E entails E’ iff E contains E’. 4. If E is a document and E’ is an instance of E, then E’ entails E. 5. If E and E’ are documents, then E entails E’ iff there is a set of triples F such that set(E) contains F and F is an instance of set(E’). The upshot of 3. and 5. is that all entailments in RDF can be checked by a very simple two-step process: E ----take a subset----> F -----existentially generalize---->E’

Replace all anonNodes in a document E by urirefs from a set V (disjoint from vocab(E)) Call this sk(E). Then 1. sk(E) entails E (obviously) Skolemization

Replace all anonNodes in a document E by urirefs from a set V (disjoin t from vocab(E)) Call this sk(E). Then 1. sk(E) entails E (obviously) 2. E probably doesn’t entail sk(E) Skolemization

Replace all anonNodes in a document E by urirefs from a set V (disjoin t from vocab(E)) Call this sk(E). Then 1. sk(E) entails E (obviously) 2. E probably doesn’t entail sk(E) …BUT… 3. If sk(E) entails F and F doesn’t contain any vocabulary from V, then E entails F Proof: Suppose I satisfies E. Then there is mapping A in anon(E) such that I+A satisfies set(E). If sk(x) is the uriref that replaces the anonNode x, then define I’ to be like I except I’(sk(x))=A(x), then clearly I’ satisfies sk(E). sk(E) entails F, so I’ satisfies F, so I’/vocab(F) satisfies F. But vocab(F) does not intersect V, so I’/vocab(F)=I; whence, I satisfies F. QED. Skolemization

Replace all anonNodes in a document E by urirefs from a set V (disjoin t from vocab(E)) Call this sk(E). Then 1. sk(E) entails E (obviously) 2. E probably doesn’t entail sk(E)….BUT 3. If sk(E) entails F and F doesn’t contain any vocabulary from V, then E entails F Proof: Suppose I satisfies E. Then there is mapping A in anon(E) such that I+A satisfies set(E). If sk(x) is the uriref that replaces the anonNode x, then define I’ to be like I except I’(sk(x))=A(x), then clearly I’ satisifes sk(E). sk(E) entails F, so I’ satisfies F, so I’/vocab(F) satisfies F. But vocab(F) does not intersect V, so I’/vocab(F)=I; whence, I satisfies F. QED. So, as far as V-free expressions are concerned, E and sk(E) entail the same things. So (with the no-V-provision), asserting sk(E) and asserting E amount to making the same assertion. Skolemization

Replace all anonNodes in a document E by urirefs from a set V (disjoin t from vocab(E)) Call this sk(E). Then 1. sk(E) entails E (obviously) 2. E probably doesn’t entail sk(E)….BUT 3. If sk(E) entails F and F doesn’t contain any vocabulary from V, then E entails F Proof: Suppose I satisfies E. Then there is mapping A in anon(E) such that I+A satisfies set(E). If sk(x) is the uriref that replaces the anonNode x, then define I’ to be like I except I’(sk(x))=A(x), then clearly I’ satisifes sk(E). sk(E) entails F, so I’ satisfies F, so I’/vocab(F) satisfies F. But vocab(F) does not intersect V, so I’/vocab(F)=I; whence, I satisfies F. QED. So, as far as V-free expressions are concerned, E and sk(E) entail the same things. So (with the no-V-provision), asserting sk(E) and asserting E amount to making the same assertion. Mind you, it’s different if you aren’t making an assertion… Skolemization

What does it mean to publish some RDF? You could be saying: I am asserting this. Infer what you like from it. E entails ????

What does it mean to publish some RDF? You could be saying: I am asserting this. Infer what you like from it. E entails ???? OR You could be saying: I am asking about this. Can you infer it from anything? ???? entails E

What does it mean to publish some RDF? You could be saying: I am asserting this. Infer what you like from it. E entails ???? OR You could be saying: I am asking about this. Can you infer it from anything? ???? entails E The model theory works equally well in either case, but the proof techniques differ. In making an assertion, anonNodes behave very much like urirefs: they both act like logical constants, and cannot be validly bound to new values at inference time. In making a query, anonNodes are treated as genuine variables, and can be bound to new values in order to make inferences possible.

Shared content and relative entailment ?? How do we capture the idea of ‘shared content’ which isn’t explicitly represented in RDF expressions but on which meaning depends??

Shared content and relative entailment ?? How do we capture the idea of ‘shared content’ which isn’t explicitly represented in RDF expressions but on which meaning depends?? Idea: express such shared knowledge as mutual acceptance of a set of interpretations which capture the accepted constraints. If COM is a set of interpretations, then say that E entails E’ relative to COM if every interpretation which satisfies E and is compatible with some member of COM also satisfies E’.

Shared content and relative entailment ?? How do we capture the idea of ‘shared content’ which isn’t explicitly represented in RDF expressions but on which meaning depends?? Idea: express such shared knowledge as mutual acceptance of a set of interpretations which capture the accepted constraints. If COM is a set of interpretations, then say that E entails E’ relative to COM if every interpretation which satisfies E and is compatible with some member of COM also satisfies E’. COM is an interpretation core. It rules out interpretations which are inconsistent with anything in COM. Ordinary entailment is entailment relative to { }. Example: Define an interpretation I with universe the set of possible uri’s starting “ IP = { }, and IS(x)=the webpage located by Google when given x as input. Then {I} is an interpretation core which represents an acceptance of Google as a definitive website locator.

Shared content and relative entailment ?? How do we capture the idea of ‘shared content’ which isn’t explicitly represented in RDF expressions but on which meaning depends?? Idea: express such shared knowledge as mutual acceptance of a set of interpretations which capture the accepted constraints. If COM is a set of interpretations, then say that E entails E’ relative to COM if every interpretation which satisfies E and is compatible with some member of COM also satisfies E’. COM is an interpretation core. It rules out interpretations which are inconsistent with anything in COM. Ordinary entailment is entailment relative to { }. OPEN QUESTIONS 1. How do we specify COM? 2. What properties does relative entailment have? (V. hard to answer in general, but particular cases might be OK.)

0. A set LV and a mapping XL from to LV 1. A nonempty set IR of resources, called the domain or universe of I. 2. A non-empty subset IP of IR called Properties 3. A mapping IEXT from IP into the powerset of IRx(IR union LV) 4. A mapping IS: vocab(I) -> IR 5. A nonempty set IC of IR called Classes 6. A mapping ICEXT from IC to the power set of IR union LV RDFS interpretations

0. A set LV and a mapping XL from to LV 1. A nonempty set IR of resources, called the domain or universe of I. 2. A non-empty subset IP of IR called Properties 3. A mapping IEXT from IP into the powerset of IRx(IR union LV) 4. A mapping IS: vocab(I) -> IR 5. A nonempty set IC of IR called Classes 6. A mapping ICEXT from IC to the power set of IR union LV RDFS interpretations The set of things in the Class

0. A set LV and a mapping XL from to LV 1. A nonempty set IR of resources, called the domain or universe of I. 2. A non-empty subset IP of IR called Properties 3. A mapping IEXT from IP into the powerset of IRx(IR union LV) 4. A mapping IS: vocab(I) -> IR 5. A nonempty set IC of IR called Classes 6. A mapping ICEXT from IC to the power set of IR union LV ICEXT(I(rdfs:Resource)) = IR ICEXT(I(rdf:Property)) = IP ICEXT(I(rdfs:Class)) = IC ICEXT(I(rdfs:Literal)) = LV is in IEXT(I(rdf:type)) iff x is in ICEXT(y) is in IEXT(I(rdfs:subClassOf)) iff ICEXT(x) is a subset of ICEXT(y) is in IEXT(rdfs:subPropertyOf)) iff IEXT(x) is a subset of IEXT(y) I(rdfs:ConstraintResource) is in IC ICEXT(I(rdfs:ConstraintProperty)) is a subset of the intersection of IP and ICEXT(I(rdfs:ConstraintResource)) I(rdf:range) and I(rdf:domain) are in ICEXT(I(rdfs:ConstraintProperty)) If is in IEXT(I(rdf:range)) and is in IEXT(x) then v is in ICEXT(y) If is in IEXT(I(rdf:domain)) and is in IEXT(x) then u is in ICEXT(y)

0. A set LV and a mapping XL from to LV 1. A nonempty set IR of resources, called the domain or universe of I. 2. A non-empty subset IP of IR called Properties 3. A mapping IEXT from IP into the powerset of IRx(IR union LV) 4. A mapping IS: vocab(I) -> IR 5. A nonempty set IC of IR called Classes 6. A mapping ICEXT from IC to the power set of IR union LV if E is a or then I(E) = IS(E) if E is a then I(E) = LV(E) if E is an asserted triple with the form s p o then I(E) = true iff is in IEXT(I(p)), else I(E)= false. if E is a set of triples then I(E) = false when I(E') = false for some asserted triple E' in E, else I(E) = true. if E is an then I(E) = true if I[A](set(E))=true for some A defined on anon(E), else I(E)= false ICEXT(I(rdfs:Resource)) = IR ICEXT(I(rdf:Property)) = IP ICEXT(I(rdfs:Class)) = IC ICEXT(I(rdfs:Literal)) = LV is in IEXT(I(rdf:type)) iff x is in ICEXT(y) is in IEXT(I(rdfs:subClassOf)) iff ICEXT(x) is a subset of ICEXT(y) is in IEXT(rdfs:subPropertyOf)) iff IEXT(x) is a subset of IEXT(y) I(rdfs:ConstraintResource) is in IC ICEXT(I(rdfs:ConstraintProperty)) is a subset of the intersection of IP and ICEXT(I(rdfs:ConstraintResource)) I(rdf:range) and I(rdf:domain) are in ICEXT(I(rdfs:ConstraintProperty)) If is in IEXT(I(rdf:range)) and is in IEXT(x) then v is in ICEXT(y) If is in IEXT(I(rdf:domain)) and is in IEXT(x) then u is in ICEXT(y)

Reification of V Assume a mapping REIF from V into IR such that: is in IEXT(I(rdf:subject)) iff for some a, b, and c in V, x= REIF( ) and y= REIF(a) is in IEXT(I(rdf:predicate)) iff for some a, b and c in V, x=REIF( ) and y= REIF(b) is in IEXT(I(rdf:object)) iff for some a, b and c in V, x=REIF( ) and y= REIF(c) x is in ICEXT(I(rdf:Statement)) iff for some a, b and c in V, x=REIF( )

Reification of V Assume a mapping REIF from V into IR such that: is in IEXT(I(rdf:subject)) iff for some a, b, and c in V, x= REIF( ) and y= REIF(a) is in IEXT(I(rdf:predicate)) iff for some a, b and c in V, x=REIF( ) and y= REIF(b) is in IEXT(I(rdf:object)) iff for some a, b and c in V, x=REIF( ) and y= REIF(c) x is in ICEXT(I(rdf:Statement)) iff for some a, b and c in V, x=REIF( ) REIF Syntax Domain

Reification of V assuming syntax is in the domain (so REIF is just the identity): is in IEXT(I(rdf:subject)) iff x is a V-triple of the form is in IEXT(I(rdf:predicate)) iff x is a V-triple of the form is in IEXT(I(rdf:subject)) iff x is a V-triple of the form x is in IEXT(I(rdf:Statement)) iff x is a V-triple.

Reification of V assuming syntax is in the domain (so REIF is just the identity): is in IEXT(I(rdf:subject)) iff x is a V-triple of the form is in IEXT(I(rdf:predicate)) iff x is a V-triple of the form is in IEXT(I(rdf:subject)) iff x is a V-triple of the form x is in IEXT(I(rdf:Statement)) iff x is a V-triple. The syntax is in the domain, so IEXT isn’t needed.

Reification of V assuming syntax is in the domain (so REIF is just the identity): is in IEXT(I(rdf:subject)) iff x is a V-triple of the form is in IEXT(I(rdf:predicate)) iff x is a V-triple of the form is in IEXT(I(rdf:subject)) iff x is a V-triple of the form x is in IEXT(I(rdf:Statement)) iff x is a V-triple. BUT NOTE there in no way to assert a reified triple, ie to get it interpreted. (Nothing generates I( ) )

STILL TO COME CONTAINERS (alt, aboutEach, aboutEachPrefix) (M&S wording is unclear about and/or) ABSOLUTE/RELATIVE URIs SOME METATHEORY LEMMAS FOR RDFS ENTAILMENT