Saturday, August 21, 2004
PyOpenGL -- The Python OpenGL Binding
JFIPA: FIPA XML-based Message support for Software Agents
Friday, August 20, 2004
Python Imaging Library Overview
Wednesday, August 18, 2004
Voltage Security - Enabling Trusted Channels of Communication
Document Understanding Conferences
Tuesday, August 17, 2004
Text Summarization Using Lexical Chains
The Generation of Representations of Word Meanings from Dictionaries
Semantic Knowledge Representation - Reference Information
NATURAL LANGUAGE PROCESSING
BEA WEBLOGIC JROCKIT™: JAVA FOR THE ENTERPRISE
xml2rfc
xml2rfc is a tool for implementing RFC 2629.
"This memo describes how to write a document for the I-D and RFC series using the Extensible Markup LanguageWorld Wide Web Consortium, Extensible Markup Language (XML) 1.0, February 1998.[1] (XML). This memo has three goals:
To describe a simple XML Document Type Definition (DTD) that is powerful enough to handle the simple formatting requirements of RFC-like documents whilst allowing for meaningful markup of descriptive qualities.
To describe software that processes XML source files, including a tool that produces documents conforming to RFC 2223Postel, J. and J. Reynolds, Instructions to RFC Authors, October 1997.[2], HTML format, and so on.
To provide the proof-of-concept for the first two goals (this memo was written using this DTD and produced using that software).
It is beyond the scope of this memo to discuss the political ramifications of using XML as a source format for RFC-like documents. Rather, it is simply noted that adding minimal markup to plain text:
allows the traditional production of textual RFC-like documents using familiar editors;
requires some, albeit minimal, additions to existing software environments; and,
permits information to be organized, searched, and retrieved using both unstructured and structured mechanisms. "
"This memo describes how to write a document for the I-D and RFC series using the Extensible Markup LanguageWorld Wide Web Consortium, Extensible Markup Language (XML) 1.0, February 1998.[1] (XML). This memo has three goals:
To describe a simple XML Document Type Definition (DTD) that is powerful enough to handle the simple formatting requirements of RFC-like documents whilst allowing for meaningful markup of descriptive qualities.
To describe software that processes XML source files, including a tool that produces documents conforming to RFC 2223Postel, J. and J. Reynolds, Instructions to RFC Authors, October 1997.[2], HTML format, and so on.
To provide the proof-of-concept for the first two goals (this memo was written using this DTD and produced using that software).
It is beyond the scope of this memo to discuss the political ramifications of using XML as a source format for RFC-like documents. Rather, it is simply noted that adding minimal markup to plain text:
allows the traditional production of textual RFC-like documents using familiar editors;
requires some, albeit minimal, additions to existing software environments; and,
permits information to be organized, searched, and retrieved using both unstructured and structured mechanisms. "
Monday, August 16, 2004
Semantic Theory and the Representation of Meaning
A Framework for Semantic Gossiping*
Global Semantic Interoperability
On Semantic Analysis and Consensus Building
World Wide Web Consortium Process Document
Algebraic Semiotics
Some Tenets of Cognitive Semantics
Some Tenets of Cognitive Semantics
"As an introduction, I want to contrast two approaches to formal semantics: one realistic and one cognitive. The fundamental difference concerns what kind of entities are the meanings of words. According to the realistic approach to semantics the meaning of an expression is something out there in the world. Cognitive semantics, on the other hand, identifies meanings of expressions with mental entities.
.....
The second paradigm of semantics is cognitivistic. The core idea of this approach is that meanings of expressions are mental. A semantics is seen as a mapping from the linguistic expressions to cognitive structures. Language itself is seen as part of the cognitive structure, and not an entity with independent standing. Within cognitive semantics the emphasis is on lexical meaning rather than on the meaning of sentences. This kind of semantics will be presented further in the following section."
"As an introduction, I want to contrast two approaches to formal semantics: one realistic and one cognitive. The fundamental difference concerns what kind of entities are the meanings of words. According to the realistic approach to semantics the meaning of an expression is something out there in the world. Cognitive semantics, on the other hand, identifies meanings of expressions with mental entities.
.....
The second paradigm of semantics is cognitivistic. The core idea of this approach is that meanings of expressions are mental. A semantics is seen as a mapping from the linguistic expressions to cognitive structures. Language itself is seen as part of the cognitive structure, and not an entity with independent standing. Within cognitive semantics the emphasis is on lexical meaning rather than on the meaning of sentences. This kind of semantics will be presented further in the following section."
Why Information Science needs Cognitive Semantics
Why Information Science needs Cognitive Semantics - and what it has to offer in return.
"These notes describe an evolving personal view of developments in cognitive semantics over the past two decades. They are written for discussions and from the perspective of how these developments impact my work on the semantics of geographic information1. They summarize some of my limited knowledge of the works of Ron Langacker, George Lakoff, Mark Johnson, Anna Wierzbicka, Peter Gärdenfors, Mark Turner, Gilles Fauconnier, John Taylor, Len Talmy, and others, as I think they relate to (geographic) information science. No attempt is made at a complete coverage of these authors’ ideas about cognitive semantics. For example, I leave out the notion of virtuality (or fictiveness), which is a central theme in the works of Langacker, Talmy, and others. The spotlights on some findings of cognitive linguistics2 and semantics are meant to point readers to some key ideas. These ideas, more often than not, provide different foundations (and opportunities) for work on information semantics in databases, user interfaces, interoperability, and related areas. Many of them have previously been applied to information science by Joseph Goguen and others (see, for example, [1] and [2])3. My paraphrasings, hopefully, do not depart too far from the original ideas, though they often simplify them. The interpretations and applications are normally mine, and may often be strenuous (or even wrong) from a cognitive linguist’s point of view - not to mention that of a traditional linguist. Many subtleties are ignored or glossed over, for lack of space, understanding, perceived need, or a combination of these factors. More reading could have answered some of the questions raised, or shown some of my suggestions to be untenable. I am grateful if these blind spots are pointed out to me."
"These notes describe an evolving personal view of developments in cognitive semantics over the past two decades. They are written for discussions and from the perspective of how these developments impact my work on the semantics of geographic information1. They summarize some of my limited knowledge of the works of Ron Langacker, George Lakoff, Mark Johnson, Anna Wierzbicka, Peter Gärdenfors, Mark Turner, Gilles Fauconnier, John Taylor, Len Talmy, and others, as I think they relate to (geographic) information science. No attempt is made at a complete coverage of these authors’ ideas about cognitive semantics. For example, I leave out the notion of virtuality (or fictiveness), which is a central theme in the works of Langacker, Talmy, and others. The spotlights on some findings of cognitive linguistics2 and semantics are meant to point readers to some key ideas. These ideas, more often than not, provide different foundations (and opportunities) for work on information semantics in databases, user interfaces, interoperability, and related areas. Many of them have previously been applied to information science by Joseph Goguen and others (see, for example, [1] and [2])3. My paraphrasings, hopefully, do not depart too far from the original ideas, though they often simplify them. The interpretations and applications are normally mine, and may often be strenuous (or even wrong) from a cognitive linguist’s point of view - not to mention that of a traditional linguist. Many subtleties are ignored or glossed over, for lack of space, understanding, perceived need, or a combination of these factors. More reading could have answered some of the questions raised, or shown some of my suggestions to be untenable. I am grateful if these blind spots are pointed out to me."
Sunday, August 15, 2004
Metadata Jones and the Tower of Babel: The Challenge of Large-Scale Semantic Heterogeneity
Metadata Jones and the Tower of Babel: The Challenge of Large-Scale Semantic Heterogeneity
"ABSTRACT
The popularity and growth of the "Information SuperHighway" (e.g., the Web) have dramatically increased the number of information sources available for use and the opportunity for important new information-intensive applications (e.g., massive data warehouses, integrated supply chain management, global risk management, in-transit visibility). Unfortunately, there are significant challenges to be overcome regarding data extraction and data interpretation in order for this opportunity to be realized.
Data Extraction: One problem is the difficulty in easily and automatically extracting very specific data elements from Web sites for use by operational systems. New technologies, such as XML and Web Querying/Wrapping, offer possible solutions to this problem.
Data Interpretation: Another serious problem is the existence of heterogeneous contexts, whereby each SOURCE of information and potential RECEIVER of that information may operate with a different context, leading to large-scale semantic heterogeneity. A context is the collection of implicit assumptions about the context definition (i.e., meaning) and context characteristics (i.e., quality) of the information. As a simple example, whereas most US universities grade on a 4.0 scale, MIT uses a 5.0 scale – posing a problem if one is comparing student GPA’s. Another typical example might be the extraction of price information from the Web: but is the price in Dollars or Yen (If dollars, is it US dollars or Hong Kong dollars), does it include taxes, does it include shipping, etc. – and does that match the receiver’s assumptions?
In this paper, examples of important context challenges will be presented and the critical role of metadata, in the form of context knowledge, will be discussed."
"ABSTRACT
The popularity and growth of the "Information SuperHighway" (e.g., the Web) have dramatically increased the number of information sources available for use and the opportunity for important new information-intensive applications (e.g., massive data warehouses, integrated supply chain management, global risk management, in-transit visibility). Unfortunately, there are significant challenges to be overcome regarding data extraction and data interpretation in order for this opportunity to be realized.
Data Extraction: One problem is the difficulty in easily and automatically extracting very specific data elements from Web sites for use by operational systems. New technologies, such as XML and Web Querying/Wrapping, offer possible solutions to this problem.
Data Interpretation: Another serious problem is the existence of heterogeneous contexts, whereby each SOURCE of information and potential RECEIVER of that information may operate with a different context, leading to large-scale semantic heterogeneity. A context is the collection of implicit assumptions about the context definition (i.e., meaning) and context characteristics (i.e., quality) of the information. As a simple example, whereas most US universities grade on a 4.0 scale, MIT uses a 5.0 scale – posing a problem if one is comparing student GPA’s. Another typical example might be the extraction of price information from the Web: but is the price in Dollars or Yen (If dollars, is it US dollars or Hong Kong dollars), does it include taxes, does it include shipping, etc. – and does that match the receiver’s assumptions?
In this paper, examples of important context challenges will be presented and the critical role of metadata, in the form of context knowledge, will be discussed."
Information Retrieval (Z39.50): Application Service Definition and Protocol Specification
Information Retrieval (Z39.50): Application Service Definition and Protocol Specification is the latest copy (as far as I can tell) of the specification itself.
"Abstract: This standard defines a client/server based service and protocol for Information Retrieval. It specifies procedures and formats for a client to search a database provided by a server, retrieve database records, and perform related information retrieval functions. The protocol addresses communication between information retrieval applications at the client and server; it does not address interaction between the client and the end-user."
"Abstract: This standard defines a client/server based service and protocol for Information Retrieval. It specifies procedures and formats for a client to search a database provided by a server, retrieve database records, and perform related information retrieval functions. The protocol addresses communication between information retrieval applications at the client and server; it does not address interaction between the client and the end-user."
Z39.50 Resources Directory
Z39.50 Resources Directory is a web site devoted to resources about Z39.50, an international standard for information retrieval across heterogenious data sources.
"ANSI/NISO Z39.50 is a communications protocol for information retrieval in a client/server environment. Widely used in bibliographic and digital library applications, Z39.50 represents a significant body of experience relating to bibliographic data retrieval and the specification of interoperable semantics for community-specific metadata attribute sets."
"ANSI/NISO Z39.50 is a communications protocol for information retrieval in a client/server environment. Widely used in bibliographic and digital library applications, Z39.50 represents a significant body of experience relating to bibliographic data retrieval and the specification of interoperable semantics for community-specific metadata attribute sets."
