What's in a Link - Revision history

Marco: /* Transcript */

2025-01-06T16:03:11Z

Transcript

← Older revision		Revision as of 16:03, 6 January 2025
Line 23:		Line 23:

	==Transcript==		==Transcript==
	Ok so, [[user:Marco Marco]] asked me to tell you something about this paper and why I wrote it, what was going on at the time, who I was trying to address and I will cover also some of the things that have followed since. So, before I did the paper, I worked on question answering. This all got started at a seminar at Harvard where {Susumu Kuno} became my thesis supervisor, suggested: "How would you do a natural language question answering system for a database?" And I said to myself: "Well, if you're gonna do that, you ought to know something about meaning." So I went off and read a lot of philosophy and took a lot of linguistics courses and it was really subtle, people had, at the time, syntax a little bit but meaning was really mysterious. The best I could come up with from the philosophy literature is this quote from [http://en.wikipedia.org/wiki/Rudolf_Carnap Carnap]: "To know the truth condition of a sentence is to know what is asserted by it, in usual terms its 'meaning' ." So the philosophers essentially say a term has a meaning based on some ability to decide when it's true and when it's not. That's what meaning is for them and when they do it, the truth conditions are an abstract mapping from possible worlds onto truth values. Instantiations of all possible predicates on all possible objects into truth values. And I said to myself how can this infinite thing be represented finitely in a reasoning head? And the only thing I know that can do that sort of thing is an abstract procedure, something like a computer program, a Turing machine, a Post production system. Something of that sort. So I proposed a theory of semantics, which I called procedural semantics, where the meaning is defined by abstract procedures for determining reference, verifying facts, computing values, including truth values and carrying out actions. And it's build on top of, not set theory or category theory or any sort of things that have subtle complexities in their foundations, but things that we understand pretty concretely like conditional expressions and computation of a value and "if then" and "while loops" and so forth, which I maintain is a much more solid foundation than set theory or category theory or even the foundations of real variables in mathematics. And it provides a principled connection between mental symbols, that this reasoning engine is carrying in its head, and the things that are out there in the world that they actually denote or mean. In fact this is the first semantic theory in history that provides a principled, causal relationship between meanings in the head and concrete objects in the physical world. And it was practical. I applied it to a task that I was set by a guy who worked for the NASA Manned Spacecraft Center, who had collected all of the first years work the Lunar rocks into a database. And he could get his [http://en.wikipedia.org/wiki/Fortran Fortran] programmer to write a question to answer a question if some scientist wrote him one and he wanted to know if he could get his programmer out of that loop. And at the time I had developed a theory of semantics and a system for doing this, so I got a contract and I build a system that actually answered questions on the first years worth of the Apollo 11 moon rocks. It was demonstrated live at the Second Annual Lunar Science Conference {1971} and it answered questions that people came up to it and typed in. And it did reasonably well at it, it was a very interesting system. And the interesting thing about it is that its meaning representation language is an extension of the predicate calculus with generalized quantifiers, quantifiers that include imperative operations and actual calculations and non standard things, not only the I/O operator, but also things like a prime number of objects, or more than a certain amount of objects etc. The basic structure was for some quantifier, governing a variable in some class such that some condition is true, do an action. It just looks like [http://en.wikipedia.org/wiki/ALGOL ALGOL], right?		Ok so, [[user:Marco\|Marco]] asked me to tell you something about this paper and why I wrote it, what was going on at the time, who I was trying to address and I will cover also some of the things that have followed since. So, before I did the paper, I worked on question answering. This all got started at a seminar at Harvard where {Susumu Kuno} became my thesis supervisor, suggested: "How would you do a natural language question answering system for a database?" And I said to myself: "Well, if you're gonna do that, you ought to know something about meaning." So I went off and read a lot of philosophy and took a lot of linguistics courses and it was really subtle, people had, at the time, syntax a little bit but meaning was really mysterious. The best I could come up with from the philosophy literature is this quote from [http://en.wikipedia.org/wiki/Rudolf_Carnap Carnap]: "To know the truth condition of a sentence is to know what is asserted by it, in usual terms its 'meaning' ." So the philosophers essentially say a term has a meaning based on some ability to decide when it's true and when it's not. That's what meaning is for them and when they do it, the truth conditions are an abstract mapping from possible worlds onto truth values. Instantiations of all possible predicates on all possible objects into truth values. And I said to myself how can this infinite thing be represented finitely in a reasoning head? And the only thing I know that can do that sort of thing is an abstract procedure, something like a computer program, a Turing machine, a Post production system. Something of that sort. So I proposed a theory of semantics, which I called procedural semantics, where the meaning is defined by abstract procedures for determining reference, verifying facts, computing values, including truth values and carrying out actions. And it's build on top of, not set theory or category theory or any sort of things that have subtle complexities in their foundations, but things that we understand pretty concretely like conditional expressions and computation of a value and "if then" and "while loops" and so forth, which I maintain is a much more solid foundation than set theory or category theory or even the foundations of real variables in mathematics. And it provides a principled connection between mental symbols, that this reasoning engine is carrying in its head, and the things that are out there in the world that they actually denote or mean. In fact this is the first semantic theory in history that provides a principled, causal relationship between meanings in the head and concrete objects in the physical world. And it was practical. I applied it to a task that I was set by a guy who worked for the NASA Manned Spacecraft Center, who had collected all of the first years work the Lunar rocks into a database. And he could get his [http://en.wikipedia.org/wiki/Fortran Fortran] programmer to write a question to answer a question if some scientist wrote him one and he wanted to know if he could get his programmer out of that loop. And at the time I had developed a theory of semantics and a system for doing this, so I got a contract and I build a system that actually answered questions on the first years worth of the Apollo 11 moon rocks. It was demonstrated live at the Second Annual Lunar Science Conference {1971} and it answered questions that people came up to it and typed in. And it did reasonably well at it, it was a very interesting system. And the interesting thing about it is that its meaning representation language is an extension of the predicate calculus with generalized quantifiers, quantifiers that include imperative operations and actual calculations and non standard things, not only the I/O operator, but also things like a prime number of objects, or more than a certain amount of objects etc. The basic structure was for some quantifier, governing a variable in some class such that some condition is true, do an action. It just looks like [http://en.wikipedia.org/wiki/ALGOL ALGOL], right?

	For some quantified variable in a class that so the condition is true, test some other condition. And there is a TEST function and a PRINTOUT function, and with this process you can hook up to any kind of database no matter how it's structured. You have an interface from your natural language processing to this universal, procedural, operational-based meaning representation language. And then you define the primitives in this language with functions. Functions that can access the database or in extreme cases can go out in a warehouse and count bolts in a bin. So it's an extremely powerful semantic framework. Ok so, this theory permits a computer to understand in a single, uniformal way the meanings of conditions to be tested, questions to be answered and actions to be carried out. And it permits a very general purpose system for language understanding that can be used with lots of different databases and more importantly can actually cross databases and produce queries that integrate information from multiple databases in a uniform semantic framework. Without having to get those databases into some common paradigm. And as I said it can actually perceive an act on objects in the external world. And it can perceive and reason about these meaningful, meaning objects themselves, which are procedures as some kind of program inside the machines head. Ok so, there is a semantics, a theory of what things mean, but it doesn't directly address some of the things which you like to do with your meaning in the meaning representation. It's quite powerful, it's very expressive, it can compute things, but it doesn't have a bunch of associative things that you and I have in our heads. That let you go from one node to another node and pick up new things. So what I wanted was a system that was comparably well-founded, comparably expressive but also had the associativity that we humans have for following facts from one thing to another and has the capability of supporting the reasoning operations you need to do in a way that's efficient and scales.		For some quantified variable in a class that so the condition is true, test some other condition. And there is a TEST function and a PRINTOUT function, and with this process you can hook up to any kind of database no matter how it's structured. You have an interface from your natural language processing to this universal, procedural, operational-based meaning representation language. And then you define the primitives in this language with functions. Functions that can access the database or in extreme cases can go out in a warehouse and count bolts in a bin. So it's an extremely powerful semantic framework. Ok so, this theory permits a computer to understand in a single, uniformal way the meanings of conditions to be tested, questions to be answered and actions to be carried out. And it permits a very general purpose system for language understanding that can be used with lots of different databases and more importantly can actually cross databases and produce queries that integrate information from multiple databases in a uniform semantic framework. Without having to get those databases into some common paradigm. And as I said it can actually perceive an act on objects in the external world. And it can perceive and reason about these meaningful, meaning objects themselves, which are procedures as some kind of program inside the machines head. Ok so, there is a semantics, a theory of what things mean, but it doesn't directly address some of the things which you like to do with your meaning in the meaning representation. It's quite powerful, it's very expressive, it can compute things, but it doesn't have a bunch of associative things that you and I have in our heads. That let you go from one node to another node and pick up new things. So what I wanted was a system that was comparably well-founded, comparably expressive but also had the associativity that we humans have for following facts from one thing to another and has the capability of supporting the reasoning operations you need to do in a way that's efficient and scales.

Marco: /* Transcript */

2025-01-06T16:03:03Z

Transcript

← Older revision		Revision as of 16:03, 6 January 2025
Line 23:		Line 23:

	==Transcript==		==Transcript==
	Ok so, [[user:Marco]] asked me to tell you something about this paper and why I wrote it, what was going on at the time, who I was trying to address and I will cover also some of the things that have followed since. So, before I did the paper, I worked on question answering. This all got started at a seminar at Harvard where {Susumu Kuno} became my thesis supervisor, suggested: "How would you do a natural language question answering system for a database?" And I said to myself: "Well, if you're gonna do that, you ought to know something about meaning." So I went off and read a lot of philosophy and took a lot of linguistics courses and it was really subtle, people had, at the time, syntax a little bit but meaning was really mysterious. The best I could come up with from the philosophy literature is this quote from [http://en.wikipedia.org/wiki/Rudolf_Carnap Carnap]: "To know the truth condition of a sentence is to know what is asserted by it, in usual terms its 'meaning' ." So the philosophers essentially say a term has a meaning based on some ability to decide when it's true and when it's not. That's what meaning is for them and when they do it, the truth conditions are an abstract mapping from possible worlds onto truth values. Instantiations of all possible predicates on all possible objects into truth values. And I said to myself how can this infinite thing be represented finitely in a reasoning head? And the only thing I know that can do that sort of thing is an abstract procedure, something like a computer program, a Turing machine, a Post production system. Something of that sort. So I proposed a theory of semantics, which I called procedural semantics, where the meaning is defined by abstract procedures for determining reference, verifying facts, computing values, including truth values and carrying out actions. And it's build on top of, not set theory or category theory or any sort of things that have subtle complexities in their foundations, but things that we understand pretty concretely like conditional expressions and computation of a value and "if then" and "while loops" and so forth, which I maintain is a much more solid foundation than set theory or category theory or even the foundations of real variables in mathematics. And it provides a principled connection between mental symbols, that this reasoning engine is carrying in its head, and the things that are out there in the world that they actually denote or mean. In fact this is the first semantic theory in history that provides a principled, causal relationship between meanings in the head and concrete objects in the physical world. And it was practical. I applied it to a task that I was set by a guy who worked for the NASA Manned Spacecraft Center, who had collected all of the first years work the Lunar rocks into a database. And he could get his [http://en.wikipedia.org/wiki/Fortran Fortran] programmer to write a question to answer a question if some scientist wrote him one and he wanted to know if he could get his programmer out of that loop. And at the time I had developed a theory of semantics and a system for doing this, so I got a contract and I build a system that actually answered questions on the first years worth of the Apollo 11 moon rocks. It was demonstrated live at the Second Annual Lunar Science Conference {1971} and it answered questions that people came up to it and typed in. And it did reasonably well at it, it was a very interesting system. And the interesting thing about it is that its meaning representation language is an extension of the predicate calculus with generalized quantifiers, quantifiers that include imperative operations and actual calculations and non standard things, not only the I/O operator, but also things like a prime number of objects, or more than a certain amount of objects etc. The basic structure was for some quantifier, governing a variable in some class such that some condition is true, do an action. It just looks like [http://en.wikipedia.org/wiki/ALGOL ALGOL], right?		Ok so, [[user:Marco Marco]] asked me to tell you something about this paper and why I wrote it, what was going on at the time, who I was trying to address and I will cover also some of the things that have followed since. So, before I did the paper, I worked on question answering. This all got started at a seminar at Harvard where {Susumu Kuno} became my thesis supervisor, suggested: "How would you do a natural language question answering system for a database?" And I said to myself: "Well, if you're gonna do that, you ought to know something about meaning." So I went off and read a lot of philosophy and took a lot of linguistics courses and it was really subtle, people had, at the time, syntax a little bit but meaning was really mysterious. The best I could come up with from the philosophy literature is this quote from [http://en.wikipedia.org/wiki/Rudolf_Carnap Carnap]: "To know the truth condition of a sentence is to know what is asserted by it, in usual terms its 'meaning' ." So the philosophers essentially say a term has a meaning based on some ability to decide when it's true and when it's not. That's what meaning is for them and when they do it, the truth conditions are an abstract mapping from possible worlds onto truth values. Instantiations of all possible predicates on all possible objects into truth values. And I said to myself how can this infinite thing be represented finitely in a reasoning head? And the only thing I know that can do that sort of thing is an abstract procedure, something like a computer program, a Turing machine, a Post production system. Something of that sort. So I proposed a theory of semantics, which I called procedural semantics, where the meaning is defined by abstract procedures for determining reference, verifying facts, computing values, including truth values and carrying out actions. And it's build on top of, not set theory or category theory or any sort of things that have subtle complexities in their foundations, but things that we understand pretty concretely like conditional expressions and computation of a value and "if then" and "while loops" and so forth, which I maintain is a much more solid foundation than set theory or category theory or even the foundations of real variables in mathematics. And it provides a principled connection between mental symbols, that this reasoning engine is carrying in its head, and the things that are out there in the world that they actually denote or mean. In fact this is the first semantic theory in history that provides a principled, causal relationship between meanings in the head and concrete objects in the physical world. And it was practical. I applied it to a task that I was set by a guy who worked for the NASA Manned Spacecraft Center, who had collected all of the first years work the Lunar rocks into a database. And he could get his [http://en.wikipedia.org/wiki/Fortran Fortran] programmer to write a question to answer a question if some scientist wrote him one and he wanted to know if he could get his programmer out of that loop. And at the time I had developed a theory of semantics and a system for doing this, so I got a contract and I build a system that actually answered questions on the first years worth of the Apollo 11 moon rocks. It was demonstrated live at the Second Annual Lunar Science Conference {1971} and it answered questions that people came up to it and typed in. And it did reasonably well at it, it was a very interesting system. And the interesting thing about it is that its meaning representation language is an extension of the predicate calculus with generalized quantifiers, quantifiers that include imperative operations and actual calculations and non standard things, not only the I/O operator, but also things like a prime number of objects, or more than a certain amount of objects etc. The basic structure was for some quantifier, governing a variable in some class such that some condition is true, do an action. It just looks like [http://en.wikipedia.org/wiki/ALGOL ALGOL], right?

	For some quantified variable in a class that so the condition is true, test some other condition. And there is a TEST function and a PRINTOUT function, and with this process you can hook up to any kind of database no matter how it's structured. You have an interface from your natural language processing to this universal, procedural, operational-based meaning representation language. And then you define the primitives in this language with functions. Functions that can access the database or in extreme cases can go out in a warehouse and count bolts in a bin. So it's an extremely powerful semantic framework. Ok so, this theory permits a computer to understand in a single, uniformal way the meanings of conditions to be tested, questions to be answered and actions to be carried out. And it permits a very general purpose system for language understanding that can be used with lots of different databases and more importantly can actually cross databases and produce queries that integrate information from multiple databases in a uniform semantic framework. Without having to get those databases into some common paradigm. And as I said it can actually perceive an act on objects in the external world. And it can perceive and reason about these meaningful, meaning objects themselves, which are procedures as some kind of program inside the machines head. Ok so, there is a semantics, a theory of what things mean, but it doesn't directly address some of the things which you like to do with your meaning in the meaning representation. It's quite powerful, it's very expressive, it can compute things, but it doesn't have a bunch of associative things that you and I have in our heads. That let you go from one node to another node and pick up new things. So what I wanted was a system that was comparably well-founded, comparably expressive but also had the associativity that we humans have for following facts from one thing to another and has the capability of supporting the reasoning operations you need to do in a way that's efficient and scales.		For some quantified variable in a class that so the condition is true, test some other condition. And there is a TEST function and a PRINTOUT function, and with this process you can hook up to any kind of database no matter how it's structured. You have an interface from your natural language processing to this universal, procedural, operational-based meaning representation language. And then you define the primitives in this language with functions. Functions that can access the database or in extreme cases can go out in a warehouse and count bolts in a bin. So it's an extremely powerful semantic framework. Ok so, this theory permits a computer to understand in a single, uniformal way the meanings of conditions to be tested, questions to be answered and actions to be carried out. And it permits a very general purpose system for language understanding that can be used with lots of different databases and more importantly can actually cross databases and produce queries that integrate information from multiple databases in a uniform semantic framework. Without having to get those databases into some common paradigm. And as I said it can actually perceive an act on objects in the external world. And it can perceive and reason about these meaningful, meaning objects themselves, which are procedures as some kind of program inside the machines head. Ok so, there is a semantics, a theory of what things mean, but it doesn't directly address some of the things which you like to do with your meaning in the meaning representation. It's quite powerful, it's very expressive, it can compute things, but it doesn't have a bunch of associative things that you and I have in our heads. That let you go from one node to another node and pick up new things. So what I wanted was a system that was comparably well-founded, comparably expressive but also had the associativity that we humans have for following facts from one thing to another and has the capability of supporting the reasoning operations you need to do in a way that's efficient and scales.

Marco: /* Transcript */

2025-01-06T16:02:53Z

Transcript

← Older revision		Revision as of 16:02, 6 January 2025
Line 23:		Line 23:

	==Transcript==		==Transcript==
	Ok so, [[Marco]] asked me to tell you something about this paper and why I wrote it, what was going on at the time, who I was trying to address and I will cover also some of the things that have followed since. So, before I did the paper, I worked on question answering. This all got started at a seminar at Harvard where {Susumu Kuno} became my thesis supervisor, suggested: "How would you do a natural language question answering system for a database?" And I said to myself: "Well, if you're gonna do that, you ought to know something about meaning." So I went off and read a lot of philosophy and took a lot of linguistics courses and it was really subtle, people had, at the time, syntax a little bit but meaning was really mysterious. The best I could come up with from the philosophy literature is this quote from [http://en.wikipedia.org/wiki/Rudolf_Carnap Carnap]: "To know the truth condition of a sentence is to know what is asserted by it, in usual terms its 'meaning' ." So the philosophers essentially say a term has a meaning based on some ability to decide when it's true and when it's not. That's what meaning is for them and when they do it, the truth conditions are an abstract mapping from possible worlds onto truth values. Instantiations of all possible predicates on all possible objects into truth values. And I said to myself how can this infinite thing be represented finitely in a reasoning head? And the only thing I know that can do that sort of thing is an abstract procedure, something like a computer program, a Turing machine, a Post production system. Something of that sort. So I proposed a theory of semantics, which I called procedural semantics, where the meaning is defined by abstract procedures for determining reference, verifying facts, computing values, including truth values and carrying out actions. And it's build on top of, not set theory or category theory or any sort of things that have subtle complexities in their foundations, but things that we understand pretty concretely like conditional expressions and computation of a value and "if then" and "while loops" and so forth, which I maintain is a much more solid foundation than set theory or category theory or even the foundations of real variables in mathematics. And it provides a principled connection between mental symbols, that this reasoning engine is carrying in its head, and the things that are out there in the world that they actually denote or mean. In fact this is the first semantic theory in history that provides a principled, causal relationship between meanings in the head and concrete objects in the physical world. And it was practical. I applied it to a task that I was set by a guy who worked for the NASA Manned Spacecraft Center, who had collected all of the first years work the Lunar rocks into a database. And he could get his [http://en.wikipedia.org/wiki/Fortran Fortran] programmer to write a question to answer a question if some scientist wrote him one and he wanted to know if he could get his programmer out of that loop. And at the time I had developed a theory of semantics and a system for doing this, so I got a contract and I build a system that actually answered questions on the first years worth of the Apollo 11 moon rocks. It was demonstrated live at the Second Annual Lunar Science Conference {1971} and it answered questions that people came up to it and typed in. And it did reasonably well at it, it was a very interesting system. And the interesting thing about it is that its meaning representation language is an extension of the predicate calculus with generalized quantifiers, quantifiers that include imperative operations and actual calculations and non standard things, not only the I/O operator, but also things like a prime number of objects, or more than a certain amount of objects etc. The basic structure was for some quantifier, governing a variable in some class such that some condition is true, do an action. It just looks like [http://en.wikipedia.org/wiki/ALGOL ALGOL], right?		Ok so, [[user:Marco]] asked me to tell you something about this paper and why I wrote it, what was going on at the time, who I was trying to address and I will cover also some of the things that have followed since. So, before I did the paper, I worked on question answering. This all got started at a seminar at Harvard where {Susumu Kuno} became my thesis supervisor, suggested: "How would you do a natural language question answering system for a database?" And I said to myself: "Well, if you're gonna do that, you ought to know something about meaning." So I went off and read a lot of philosophy and took a lot of linguistics courses and it was really subtle, people had, at the time, syntax a little bit but meaning was really mysterious. The best I could come up with from the philosophy literature is this quote from [http://en.wikipedia.org/wiki/Rudolf_Carnap Carnap]: "To know the truth condition of a sentence is to know what is asserted by it, in usual terms its 'meaning' ." So the philosophers essentially say a term has a meaning based on some ability to decide when it's true and when it's not. That's what meaning is for them and when they do it, the truth conditions are an abstract mapping from possible worlds onto truth values. Instantiations of all possible predicates on all possible objects into truth values. And I said to myself how can this infinite thing be represented finitely in a reasoning head? And the only thing I know that can do that sort of thing is an abstract procedure, something like a computer program, a Turing machine, a Post production system. Something of that sort. So I proposed a theory of semantics, which I called procedural semantics, where the meaning is defined by abstract procedures for determining reference, verifying facts, computing values, including truth values and carrying out actions. And it's build on top of, not set theory or category theory or any sort of things that have subtle complexities in their foundations, but things that we understand pretty concretely like conditional expressions and computation of a value and "if then" and "while loops" and so forth, which I maintain is a much more solid foundation than set theory or category theory or even the foundations of real variables in mathematics. And it provides a principled connection between mental symbols, that this reasoning engine is carrying in its head, and the things that are out there in the world that they actually denote or mean. In fact this is the first semantic theory in history that provides a principled, causal relationship between meanings in the head and concrete objects in the physical world. And it was practical. I applied it to a task that I was set by a guy who worked for the NASA Manned Spacecraft Center, who had collected all of the first years work the Lunar rocks into a database. And he could get his [http://en.wikipedia.org/wiki/Fortran Fortran] programmer to write a question to answer a question if some scientist wrote him one and he wanted to know if he could get his programmer out of that loop. And at the time I had developed a theory of semantics and a system for doing this, so I got a contract and I build a system that actually answered questions on the first years worth of the Apollo 11 moon rocks. It was demonstrated live at the Second Annual Lunar Science Conference {1971} and it answered questions that people came up to it and typed in. And it did reasonably well at it, it was a very interesting system. And the interesting thing about it is that its meaning representation language is an extension of the predicate calculus with generalized quantifiers, quantifiers that include imperative operations and actual calculations and non standard things, not only the I/O operator, but also things like a prime number of objects, or more than a certain amount of objects etc. The basic structure was for some quantifier, governing a variable in some class such that some condition is true, do an action. It just looks like [http://en.wikipedia.org/wiki/ALGOL ALGOL], right?

	For some quantified variable in a class that so the condition is true, test some other condition. And there is a TEST function and a PRINTOUT function, and with this process you can hook up to any kind of database no matter how it's structured. You have an interface from your natural language processing to this universal, procedural, operational-based meaning representation language. And then you define the primitives in this language with functions. Functions that can access the database or in extreme cases can go out in a warehouse and count bolts in a bin. So it's an extremely powerful semantic framework. Ok so, this theory permits a computer to understand in a single, uniformal way the meanings of conditions to be tested, questions to be answered and actions to be carried out. And it permits a very general purpose system for language understanding that can be used with lots of different databases and more importantly can actually cross databases and produce queries that integrate information from multiple databases in a uniform semantic framework. Without having to get those databases into some common paradigm. And as I said it can actually perceive an act on objects in the external world. And it can perceive and reason about these meaningful, meaning objects themselves, which are procedures as some kind of program inside the machines head. Ok so, there is a semantics, a theory of what things mean, but it doesn't directly address some of the things which you like to do with your meaning in the meaning representation. It's quite powerful, it's very expressive, it can compute things, but it doesn't have a bunch of associative things that you and I have in our heads. That let you go from one node to another node and pick up new things. So what I wanted was a system that was comparably well-founded, comparably expressive but also had the associativity that we humans have for following facts from one thing to another and has the capability of supporting the reasoning operations you need to do in a way that's efficient and scales.		For some quantified variable in a class that so the condition is true, test some other condition. And there is a TEST function and a PRINTOUT function, and with this process you can hook up to any kind of database no matter how it's structured. You have an interface from your natural language processing to this universal, procedural, operational-based meaning representation language. And then you define the primitives in this language with functions. Functions that can access the database or in extreme cases can go out in a warehouse and count bolts in a bin. So it's an extremely powerful semantic framework. Ok so, this theory permits a computer to understand in a single, uniformal way the meanings of conditions to be tested, questions to be answered and actions to be carried out. And it permits a very general purpose system for language understanding that can be used with lots of different databases and more importantly can actually cross databases and produce queries that integrate information from multiple databases in a uniform semantic framework. Without having to get those databases into some common paradigm. And as I said it can actually perceive an act on objects in the external world. And it can perceive and reason about these meaningful, meaning objects themselves, which are procedures as some kind of program inside the machines head. Ok so, there is a semantics, a theory of what things mean, but it doesn't directly address some of the things which you like to do with your meaning in the meaning representation. It's quite powerful, it's very expressive, it can compute things, but it doesn't have a bunch of associative things that you and I have in our heads. That let you go from one node to another node and pick up new things. So what I wanted was a system that was comparably well-founded, comparably expressive but also had the associativity that we humans have for following facts from one thing to another and has the capability of supporting the reasoning operations you need to do in a way that's efficient and scales.

Marco: /* Transcript */

2025-01-06T16:02:37Z

Transcript

← Older revision		Revision as of 16:02, 6 January 2025
Line 23:		Line 23:

	==Transcript==		==Transcript==
	Ok so, Marco asked me to tell you something about this paper and why I wrote it, what was going on at the time, who I was trying to address and I will cover also some of the things that have followed since. So, before I did the paper, I worked on question answering. This all got started at a seminar at Harvard where {Susumu Kuno} became my thesis supervisor, suggested: "How would you do a natural language question answering system for a database?" And I said to myself: "Well, if you're gonna do that, you ought to know something about meaning." So I went off and read a lot of philosophy and took a lot of linguistics courses and it was really subtle, people had, at the time, syntax a little bit but meaning was really mysterious. The best I could come up with from the philosophy literature is this quote from [http://en.wikipedia.org/wiki/Rudolf_Carnap Carnap]: "To know the truth condition of a sentence is to know what is asserted by it, in usual terms its 'meaning' ." So the philosophers essentially say a term has a meaning based on some ability to decide when it's true and when it's not. That's what meaning is for them and when they do it, the truth conditions are an abstract mapping from possible worlds onto truth values. Instantiations of all possible predicates on all possible objects into truth values. And I said to myself how can this infinite thing be represented finitely in a reasoning head? And the only thing I know that can do that sort of thing is an abstract procedure, something like a computer program, a Turing machine, a Post production system. Something of that sort. So I proposed a theory of semantics, which I called procedural semantics, where the meaning is defined by abstract procedures for determining reference, verifying facts, computing values, including truth values and carrying out actions. And it's build on top of, not set theory or category theory or any sort of things that have subtle complexities in their foundations, but things that we understand pretty concretely like conditional expressions and computation of a value and "if then" and "while loops" and so forth, which I maintain is a much more solid foundation than set theory or category theory or even the foundations of real variables in mathematics. And it provides a principled connection between mental symbols, that this reasoning engine is carrying in its head, and the things that are out there in the world that they actually denote or mean. In fact this is the first semantic theory in history that provides a principled, causal relationship between meanings in the head and concrete objects in the physical world. And it was practical. I applied it to a task that I was set by a guy who worked for the NASA Manned Spacecraft Center, who had collected all of the first years work the Lunar rocks into a database. And he could get his [http://en.wikipedia.org/wiki/Fortran Fortran] programmer to write a question to answer a question if some scientist wrote him one and he wanted to know if he could get his programmer out of that loop. And at the time I had developed a theory of semantics and a system for doing this, so I got a contract and I build a system that actually answered questions on the first years worth of the Apollo 11 moon rocks. It was demonstrated live at the Second Annual Lunar Science Conference {1971} and it answered questions that people came up to it and typed in. And it did reasonably well at it, it was a very interesting system. And the interesting thing about it is that its meaning representation language is an extension of the predicate calculus with generalized quantifiers, quantifiers that include imperative operations and actual calculations and non standard things, not only the I/O operator, but also things like a prime number of objects, or more than a certain amount of objects etc. The basic structure was for some quantifier, governing a variable in some class such that some condition is true, do an action. It just looks like [http://en.wikipedia.org/wiki/ALGOL ALGOL], right?		Ok so, [[Marco]] asked me to tell you something about this paper and why I wrote it, what was going on at the time, who I was trying to address and I will cover also some of the things that have followed since. So, before I did the paper, I worked on question answering. This all got started at a seminar at Harvard where {Susumu Kuno} became my thesis supervisor, suggested: "How would you do a natural language question answering system for a database?" And I said to myself: "Well, if you're gonna do that, you ought to know something about meaning." So I went off and read a lot of philosophy and took a lot of linguistics courses and it was really subtle, people had, at the time, syntax a little bit but meaning was really mysterious. The best I could come up with from the philosophy literature is this quote from [http://en.wikipedia.org/wiki/Rudolf_Carnap Carnap]: "To know the truth condition of a sentence is to know what is asserted by it, in usual terms its 'meaning' ." So the philosophers essentially say a term has a meaning based on some ability to decide when it's true and when it's not. That's what meaning is for them and when they do it, the truth conditions are an abstract mapping from possible worlds onto truth values. Instantiations of all possible predicates on all possible objects into truth values. And I said to myself how can this infinite thing be represented finitely in a reasoning head? And the only thing I know that can do that sort of thing is an abstract procedure, something like a computer program, a Turing machine, a Post production system. Something of that sort. So I proposed a theory of semantics, which I called procedural semantics, where the meaning is defined by abstract procedures for determining reference, verifying facts, computing values, including truth values and carrying out actions. And it's build on top of, not set theory or category theory or any sort of things that have subtle complexities in their foundations, but things that we understand pretty concretely like conditional expressions and computation of a value and "if then" and "while loops" and so forth, which I maintain is a much more solid foundation than set theory or category theory or even the foundations of real variables in mathematics. And it provides a principled connection between mental symbols, that this reasoning engine is carrying in its head, and the things that are out there in the world that they actually denote or mean. In fact this is the first semantic theory in history that provides a principled, causal relationship between meanings in the head and concrete objects in the physical world. And it was practical. I applied it to a task that I was set by a guy who worked for the NASA Manned Spacecraft Center, who had collected all of the first years work the Lunar rocks into a database. And he could get his [http://en.wikipedia.org/wiki/Fortran Fortran] programmer to write a question to answer a question if some scientist wrote him one and he wanted to know if he could get his programmer out of that loop. And at the time I had developed a theory of semantics and a system for doing this, so I got a contract and I build a system that actually answered questions on the first years worth of the Apollo 11 moon rocks. It was demonstrated live at the Second Annual Lunar Science Conference {1971} and it answered questions that people came up to it and typed in. And it did reasonably well at it, it was a very interesting system. And the interesting thing about it is that its meaning representation language is an extension of the predicate calculus with generalized quantifiers, quantifiers that include imperative operations and actual calculations and non standard things, not only the I/O operator, but also things like a prime number of objects, or more than a certain amount of objects etc. The basic structure was for some quantifier, governing a variable in some class such that some condition is true, do an action. It just looks like [http://en.wikipedia.org/wiki/ALGOL ALGOL], right?

	For some quantified variable in a class that so the condition is true, test some other condition. And there is a TEST function and a PRINTOUT function, and with this process you can hook up to any kind of database no matter how it's structured. You have an interface from your natural language processing to this universal, procedural, operational-based meaning representation language. And then you define the primitives in this language with functions. Functions that can access the database or in extreme cases can go out in a warehouse and count bolts in a bin. So it's an extremely powerful semantic framework. Ok so, this theory permits a computer to understand in a single, uniformal way the meanings of conditions to be tested, questions to be answered and actions to be carried out. And it permits a very general purpose system for language understanding that can be used with lots of different databases and more importantly can actually cross databases and produce queries that integrate information from multiple databases in a uniform semantic framework. Without having to get those databases into some common paradigm. And as I said it can actually perceive an act on objects in the external world. And it can perceive and reason about these meaningful, meaning objects themselves, which are procedures as some kind of program inside the machines head. Ok so, there is a semantics, a theory of what things mean, but it doesn't directly address some of the things which you like to do with your meaning in the meaning representation. It's quite powerful, it's very expressive, it can compute things, but it doesn't have a bunch of associative things that you and I have in our heads. That let you go from one node to another node and pick up new things. So what I wanted was a system that was comparably well-founded, comparably expressive but also had the associativity that we humans have for following facts from one thing to another and has the capability of supporting the reasoning operations you need to do in a way that's efficient and scales.		For some quantified variable in a class that so the condition is true, test some other condition. And there is a TEST function and a PRINTOUT function, and with this process you can hook up to any kind of database no matter how it's structured. You have an interface from your natural language processing to this universal, procedural, operational-based meaning representation language. And then you define the primitives in this language with functions. Functions that can access the database or in extreme cases can go out in a warehouse and count bolts in a bin. So it's an extremely powerful semantic framework. Ok so, this theory permits a computer to understand in a single, uniformal way the meanings of conditions to be tested, questions to be answered and actions to be carried out. And it permits a very general purpose system for language understanding that can be used with lots of different databases and more importantly can actually cross databases and produce queries that integrate information from multiple databases in a uniform semantic framework. Without having to get those databases into some common paradigm. And as I said it can actually perceive an act on objects in the external world. And it can perceive and reason about these meaningful, meaning objects themselves, which are procedures as some kind of program inside the machines head. Ok so, there is a semantics, a theory of what things mean, but it doesn't directly address some of the things which you like to do with your meaning in the meaning representation. It's quite powerful, it's very expressive, it can compute things, but it doesn't have a bunch of associative things that you and I have in our heads. That let you go from one node to another node and pick up new things. So what I wanted was a system that was comparably well-founded, comparably expressive but also had the associativity that we humans have for following facts from one thing to another and has the capability of supporting the reasoning operations you need to do in a way that's efficient and scales.

James.anderson: /* Transcript */

2022-02-12T22:00:51Z

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James.anderson: /* Transcript */

2022-02-12T21:27:31Z

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James.anderson at 23:29, 9 February 2022

2022-02-09T23:29:46Z

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James.anderson at 01:44, 6 February 2022

2022-02-06T01:44:05Z

← Older revision		Revision as of 01:44, 6 February 2022
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	For some quantified variable in a class that so the condition is true, test some other condition. And there is a TEST function and a PRINTOUT function, and with this process you can hook up to any kind of database no matter how it's structured. You have an interface from your natural language processing to this universal, procedural, operational-based meaning representation language. And then you define the primitives in this language with functions. Functions that can access the database or in extreme cases can go out in a warehouse and count bolts in a bin. So it's an extremely powerful semantic framework. Ok so, this theory permits a computer to understand in a single, uniformal way the meanings of conditions to be tested, questions to be answered and actions to be carried out. And it permits a very general purpose system for language understanding that can be used with lots of different databases and more importantly can actually cross databases and produce queries that integrate information from multiple databases in a uniform semantic framework. Without having to get those databases into some common paradigm. And as I said it can actually perceive an act on objects in the external world. And it can perceive and reason about these meaningful, meaning objects themselves, which are procedures as some kind of program inside the machines head. Ok so, there is a semantics, a theory of what things mean, but it doesn't directly address some of the things which you like to do with your meaning in the meaning representation. It's quite powerful, it's very expressive, it can compute things, but it doesn't have a bunch of associative things that you and I have in our heads. That let you go from one node to another node and pick up new things. So what I wanted was a system that was comparably well-founded, comparably expressive but also had the associativity that we humans have for following facts from one thing to another and has the capability of supporting the reasoning operations you need to do in a way that's efficient and scales.		For some quantified variable in a class that so the condition is true, test some other condition. And there is a TEST function and a PRINTOUT function, and with this process you can hook up to any kind of database no matter how it's structured. You have an interface from your natural language processing to this universal, procedural, operational-based meaning representation language. And then you define the primitives in this language with functions. Functions that can access the database or in extreme cases can go out in a warehouse and count bolts in a bin. So it's an extremely powerful semantic framework. Ok so, this theory permits a computer to understand in a single, uniformal way the meanings of conditions to be tested, questions to be answered and actions to be carried out. And it permits a very general purpose system for language understanding that can be used with lots of different databases and more importantly can actually cross databases and produce queries that integrate information from multiple databases in a uniform semantic framework. Without having to get those databases into some common paradigm. And as I said it can actually perceive an act on objects in the external world. And it can perceive and reason about these meaningful, meaning objects themselves, which are procedures as some kind of program inside the machines head. Ok so, there is a semantics, a theory of what things mean, but it doesn't directly address some of the things which you like to do with your meaning in the meaning representation. It's quite powerful, it's very expressive, it can compute things, but it doesn't have a bunch of associative things that you and I have in our heads. That let you go from one node to another node and pick up new things. So what I wanted was a system that was comparably well-founded, comparably expressive but also had the associativity that we humans have for following facts from one thing to another and has the capability of supporting the reasoning operations you need to do in a way that's efficient and scales.
	So, I looked at Semantic Networks. And a Semantic Network is essentially a network of concepts connected by links used for some kind of associative access. And again I had the same question what do these links mean and what do they have to do with semantics? And I concluded pretty quickly that they had nothing to do with semantics but I finally inured myself to calling it that nonetheless because that's what everyone called it. And the kinds of things people where doing at the time were, lots of them were psychologists and they were looking to try to mirror the things that the human mind does that are associative. And one example from Collins and Quillian, Collins is a psychologist that was at [http://en.wikipedia.org/wiki/BBN_Technologies Bolt, Beranek and Newman], Ross Quillian is ostensibly the guy you coined the phrase 'Semantic Network', although arguably it was used by somebody else { [http://en.wikipedia.org/wiki/Margaret_Masterman Margaret Masterman] at Cambridge University (1961) } in England before he did. But he looked at statements like: a bird is an animal; animal has skin; a Canary is a bird; a bird has feathers and measured how long it took people to answer questions like this: "Does a canary have feathers?" or "Does a canary have skin?." And his theory was that this is coming from inheritance in the Semantic Network. And a Canary is a bird, which is an animal, and we know that animals have skin, and birds have feathers, having feathers is a more specific kind of fact than having skin and therefore it takes a little longer to find that a Canary has skin than it takes to figure out that it has feathers because the path is longer. And they actually made psychological experiments and measured the time that correlated with these predictions. I don't think the theory was ... well psychologist have a tendency to jump to the conclusion if the data is consistent with the theory then the theory is right without asking are there alternative theories that could explain the same data.		So, I looked at Semantic Networks. And a Semantic Network is essentially a network of concepts connected by links used for some kind of associative access. And again I had the same question what do these links mean and what do they have to do with semantics? And I concluded pretty quickly that they had nothing to do with semantics but I finally inured myself to calling it that nonetheless because that's what everyone called it. And the kinds of things people where doing at the time were, lots of them were psychologists and they were looking to try to mirror the things that the human mind does that are associative. And one example from Collins and Quillian, Collins is a psychologist that was at [http://en.wikipedia.org/wiki/BBN_Technologies Bolt, Beranek and Newman], Ross Quillian is ostensibly the guy you coined the phrase 'Semantic Network', although arguably it was used by somebody else { [http://en.wikipedia.org/wiki/Margaret_Masterman Margaret Masterman] at Cambridge University (1961) } in England before he did. But he looked at statements like: a bird is an animal; animal has skin; a Canary is a bird; a bird has feathers and measured how long it took people to answer questions like this: "Does a canary have feathers?" or "Does a canary have skin?." And his theory was that this is coming from inheritance in the Semantic Network. And a Canary is a bird, which is an animal, and we know that animals have skin, and birds have feathers, having feathers is a more specific kind of fact than having skin and therefore it takes a little longer to find that a Canary has skin than it takes to figure out that it has feathers because the path is longer. And they actually made psychological experiments and measured the time that correlated with these predictions. I don't think the theory was ... well psychologist have a tendency to jump to the conclusion if the data is consistent with the theory then the theory is right without asking are there alternative theories that could explain the same data.
	So, at any rate, I saw people all around me using notations that had links and had labels on them, had nodes that had labels on them and calling them 'Semantic Networks' and doing all kinds of things with them. And I thought they're not doing anything with meaning and you can't really use these representations to do anything unless you become more rigorous about them. At that time there was a conference that was organized down at [http://en.wikipedia.org/wiki/Woods_Hole,_Massachusetts Woods Hole] and I went there and heard people like [http://en.wikipedia.org/wiki/Roger_Schank Roger Schank] and a bunch of other people talking about what they were doing. And I kind of ~~spelled it~~ off on all these problems that I saw. And [http://en.wikipedia.org/wiki/Daniel_G._Bobrow Danny Bobrow] took me and holed me up in the basement of Xerox Parc with a Bravo machine and got me to write it all down and that become the "What's in a Link" paper, which ended up being dedicated to [http://en.wikipedia.org/wiki/Jaime_Carbonell Jaime Carbonell], who was my boss at the time and who unfortunately died tragically just passed away while driving down the street. Fortunately he pulled over to the side of the road before he crashed with his wife. So, the book was dedicated [http://en.wikipedia.org/wiki/Jaime_Carbonell Jaime], and in some sense this paper was dedicated to him. So here are some of the things that I saw going all around me, people said the meaning is just all the concepts that are connected to it in this web. "That's what the meaning is!" Well there might be some truth to it but it doesn't give you any meat to start with. "Whatever the system does with its input is its semantics!" Again there may be some element of truth to that but that's not what semantics is really about. "There is no difference between syntax and semantics!" this was one of [http://en.wikipedia.org/wiki/Roger_Schank Roger Schank's] claims. He illustrated it with the example: "I saw the Grand Canyon flying to the New York", which I take as proof that he is wrong. Because if he was right you would just understand what that meant, and nobody would ~~love~~. It's the fact that syntax gives you the wrong model first that makes it fun, that makes it interesting and surprising. And then there is "Semantics is in the eye of the beholder!" this is actually pretty accurate for the Semantic Networks that people are actually using. The semantics is all in the words that you put on the labels. Ross Quillian at one point had a semantic network system implemented in [http://en.wikipedia.org/wiki/LISP LISP] and he decided to remove the labels and the resulting network, when printed out, was just a giant nest of parentheses. So it's right, the semantics was all in the labels. So, in the paper I ~~characterized~~ two populations of people that address semantics, and I called one the linguist and the other the philosopher. Somewhat unfair not everybody is in these centroids, but these are caricatures. The linguist is principally interested in a way to represent the different meanings of a sentence, that's fundamentally ambiguous. So they are looking for some notation in which if a sentence has two readings I can write down the two readings and say this is this one and this is that one, here is why they are different. And if a sentence has no meaning because it's nonsense of some sort they want to some criteria that lets them say that's nonsense because of this. The philosopher on the other hand is interested in the truth conditions of predicates which he postulates in his model already as unambiguous because he ~~defines~~ them. He is interested in soundness and completeness of logical deduction systems etc. These are two parts of the problem, but even put together they don't cover the whole problem. So, the linguist is interested in thing like: "I saw the man from the park" it has got two possible interpretations "I was in the park and I saw him from there" and the other is "He is from the park and I saw him in the deli or wherever", they are very fond of the asterisk which use to mark a sentence as ungrammatical. And they would say things like "The amoeba hit John with a hammer" is ungrammatical because of semantic features. The instrumental 'with' construction used here requires that its agent be + agent. And amoebas are not marked +agent because they don't have brains. So, they call that semantics but the machinery they use to do this theory is the fundamentally same kind of theory you would use if it were syntactic. So they are basically using syntactic mechanisms. It legitimately would be called semantic if you actually followed amoeba out some reference of what amoeba meant. And from your knowledge of real amoebas you can infer that they can't use hammers except on [http://en.wikipedia.org/wiki/Star_Treck Star Trek], you know, where maybe they're really big and they can. So, I was found the interesting things about these to be in what context, what will have to be true in that context in order for this to be a sensible utterance. And if you're actually constructing that entire model than you are doing something semantic and there would be a semantic feature. But the mechanisms they had in mind didn't go that much. OK so, the philosophers are interested in reasoning, the philosophers perspective is: a model consists of ~~an assignment~~ of truth values to every possible instantiation of every predicate over a universe of individuals. And this abstract model is like the ~~idealized~~ point mass on frictionless surface that physicists use. This is the thing that they created in order to proof that the reasoning systems were sound and complete. So with this as an abstract model you can ~~proof~~ that if I have a reasoning system, a set of rules, a set of steps for deducing things, one from another and I can show that this reasoning system assigns validity only to those things that a true in every possible model and if I can show it only assigns validity to things that are true in every possible model then I got a good reasoning systems. And that was an extremely productive thing to do, and based on that they were able to show that first order logic is something complete but problematic, and set theory got issues and actually the foundations of mathematics are not all that foundational and so a lot came of it. So what's still missing though, if we go back to the dictionary definition of semantics: is the relationship between symbols and what they denote or mean. And if you think about what the logician did with their model theory, they gave us a very good account of the semantics of IF THEN, AND, OR and NOT and the quantifiers. But they don't do anything for the semantics of snow is white. And that requires you to have something else. And the something else I proposed was procedural semantics. Snow is white, you have to know what snow is. OK, if you can build a robot and he can say this is snow, and he says it properly, and he can measure the color spectrum, and can say this is white and he can do that properly then he can test whether snow is white or not, than you actually got the semantics that can really do that. So the meaning of a proposition is a procedure for determining if it is true, the meaning of an action is the ability to do it and/or tell if it has been done, and as I said this can provide a principled sensorimotor connection to the actual physical world.		So, at any rate, I saw people all around me using notations that had links and had labels on them, had nodes that had labels on them and calling them 'Semantic Networks' and doing all kinds of things with them. And I thought they're not doing anything with meaning and you can't really use these representations to do anything unless you become more rigorous about them. At that time there was a conference that was organized down at [http://en.wikipedia.org/wiki/Woods_Hole,_Massachusetts Woods Hole] and I went there and heard people like [http://en.wikipedia.org/wiki/Roger_Schank Roger Schank] and a bunch of other people talking about what they were doing. And I kind of spouted off on all these problems that I saw. And [http://en.wikipedia.org/wiki/Daniel_G._Bobrow Danny Bobrow] took me and holed me up in the basement of Xerox Parc with a Bravo machine and got me to write it all down and that become the "What's in a Link" paper, which ended up being dedicated to [http://en.wikipedia.org/wiki/Jaime_Carbonell Jaime Carbonell], who was my boss at the time and who unfortunately died tragically just passed away while driving down the street. Fortunately he pulled over to the side of the road before he crashed with his wife. So, the book was dedicated [http://en.wikipedia.org/wiki/Jaime_Carbonell Jaime], and in some sense this paper was dedicated to him. So here are some of the things that I saw going all around me, people said the meaning is just all the concepts that are connected to it in this web. "That's what the meaning is!" Well there might be some truth to it but it doesn't give you any meat to start with. "Whatever the system does with its input is its semantics!" Again there may be some element of truth to that but that's not what semantics is really about. "There is no difference between syntax and semantics!" this was one of [http://en.wikipedia.org/wiki/Roger_Schank Roger Schank's] claims. He illustrated it with the example: "I saw the Grand Canyon flying to the New York", which I take as proof that he is wrong. Because if he was right you would just understand what that meant, and nobody would laugh. It's the fact that syntax gives you the wrong model first that makes it fun, that makes it interesting and surprising. And then there is "Semantics is in the eye of the beholder!" this is actually pretty accurate for the Semantic Networks that people are actually using. The semantics is all in the words that you put on the labels. Ross Quillian at one point had a semantic network system implemented in [http://en.wikipedia.org/wiki/LISP LISP] and he decided to remove the labels and the resulting network, when printed out, was just a giant nest of parentheses. So it's right, the semantics was all in the labels. So, in the paper I characterised two populations of people that address semantics, and I called one the linguist and the other the philosopher. Somewhat unfair, not everybody is in these centroids, but these are caricatures. The linguist is principally interested in a way to represent the different meanings of a sentence, that's fundamentally ambiguous. So they are looking for some notation in which if a sentence has two readings I can write down the two readings and say this is this one and this is that one, here is why they are different. And if a sentence has no meaning because it's nonsense of some sort they want to some criteria that lets them say that's nonsense because of this. The philosopher on the other hand is interested in the truth conditions of predicates which he postulates in his model already as unambiguous, because he has defined them. He is interested in soundness and completeness of logical deduction systems etc. These are two parts of the problem, but even put together they don't cover the whole problem. So, the linguist is interested in thing like: "I saw the man from the park" it has got two possible interpretations "I was in the park and I saw him from there" and the other is "He is from the park and I saw him in the deli or wherever", they are very fond of the asterisk which use to mark a sentence as ungrammatical. And they would say things like "The amoeba hit John with a hammer" is ungrammatical because of semantic features. The instrumental 'with' construction used here requires that its agent be +agent. And amoebas are not marked +agent because they don't have brains. So, they call that semantics but the machinery they use to do this theory is the fundamentally same kind of theory you would use if it were syntactic. So they are basically using syntactic mechanisms. It legitimately would be called semantic if you actually followed amoeba out some reference of what amoeba meant. And from your knowledge of real amoebas you can infer that they can't use hammers - except on [http://en.wikipedia.org/wiki/Star_Treck Star Trek], you know, where maybe they're really big and they can. So, I was found the interesting things about these to be in what context, what will have to be true in that context in order for this to be a sensible utterance. And if you're actually constructing that entire model than you are doing something semantic and there would be a semantic feature. But the mechanisms they had in mind didn't go that much. OK so, the philosophers are interested in reasoning, the philosophers perspective is: a model consists of a set of assignments of truth values to every possible instantiation of every predicate over a universe of individuals. And this abstract model is like the idealised point mass on frictionless surface that physicists use. This is the thing that they created in order to proof that the reasoning systems were sound and complete. So with this as an abstract model you can prove that if I have a reasoning system, a set of rules, a set of steps for deducing things, one from another and I can show that this reasoning system assigns validity only to those things that a true in every possible model and if I can show it only assigns validity to things that are true in every possible model then I got a good reasoning systems. And that was an extremely productive thing to do, and based on that they were able to show that first order logic is something complete but problematic, and set theory got issues and actually the foundations of mathematics are not all that foundational and so a lot came of it. So what's still missing though, if we go back to the dictionary definition of semantics: is the relationship between symbols and what they denote or mean. And if you think about what the logician did with their model theory, they gave us a very good account of the semantics of IF THEN, AND, OR and NOT and the quantifiers. But they don't do anything for the semantics of snow is white. And that requires you to have something else. And the something else I proposed was procedural semantics. Snow is white, you have to know what snow is. OK, if you can build a robot and he can say this is snow, and he says it properly, and he can measure the color spectrum, and can say this is white and he can do that properly then he can test whether snow is white or not, than you actually got the semantics that can really do that. So the meaning of a proposition is a procedure for determining if it is true, the meaning of an action is the ability to do it and/or tell if it has been done, and as I said this can provide a principled sensorimotor connection to the actual physical world.

	Ok, back to links. So, people have used links to represent attribute and value. That's what most of you think of in an RDF network, but there are other things like links between attributes and some predicate that supposed to be true of their value. Or relations in your objects etc. So let me walk you through some of the examples from the "What's in a Link" paper. We start with something very simple:		Ok, back to links. So, people have used links to represent attribute and value. That's what most of you think of in an RDF network, but there are other things like links between attributes and some predicate that supposed to be true of their value. Or relations in your objects etc. So let me walk you through some of the examples from the "What's in a Link" paper. We start with something very simple:

James.anderson at 01:34, 6 February 2022

2022-02-06T01:34:04Z

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Marco: /* What's in a Link, Revisited - William A. Woods */

2021-10-09T14:08:35Z

What's in a Link, Revisited - William A. Woods

← Older revision		Revision as of 14:08, 9 October 2021
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	==What's in a Link, Revisited - William A. Woods==		==What's in a Link, Revisited - William A. Woods==

	In this Lotico session William discusses ideas about representing meaning in computer representations based on his classical paper "What's in a Link: Foundations for Semantic Networks" - 1975 and his review "Meaning and Links " in 2007 in a historical context.		In this Lotico session William discusses ideas about representing meaning in computer representations based on his now classical paper "What's in a Link: Foundations for Semantic Networks" - 1975 and his review "Meaning and Links " in 2007 in a historical context.

	William A. Woods is a Distinguished Software Engineer at ITA Software, Cambridge, MA and a member of the Semantic Web Meetup Group. http://parsecraft.com		William A. Woods is a Distinguished Software Engineer at ITA Software, Cambridge, MA and a member of the Semantic Web Meetup Group. http://parsecraft.com