Bibliography — 2004-07-12
This is my BibTeX bibliography, munged and massaged into XHTML 1.1, as of July 2004.
You'll find links to the BibTeX document under each entry, along with the original source (if it's available online and I've listed it). You have my permission to copy bits out of the BibTeX, or the formatted text below if you so wish. Go nuts.
Notice: I'm updating the bibliographic list, so hold your horses!
[1]
FOLDOC computing dictionary.
[2]
Protégé ontology editor and knowledge acquisition system.
[3]
W3C Semantic Web activity page.
Keywords: definition semantic web ontology working group
[4]
DAML+OIL (March 2001) reference description, 2001, W3C Note.
[5]
Google Frequently Asked Questions: Image Search, 2003.
[6]
Naming and addressing: URIs, URLs, …, 2003.
[7]
OWL implementations, 2003.
[8]
OWL Web Ontology Language guide, W3C Candidate Recommendation,
2003.
[9]
OWL Web Ontology Language Use Cases and Requirements, 2003.
[10]
RDF and RDF Schema, 2003.
[11]
OWL Web Ontology Language Overview, W3C Recommendation, 2004.
[12]
RDF Concepts and Abstract Syntax, W3C Recommendation, 2004.
[13]
RDF Primer, W3C Recommendation, 2004.
[14]
RDF Semantics, W3C Recommendation, 2004.
[15]
RDF Test Cases, W3C Recommendation, 2004.
[16]
RDF Vocabulary Description Language 1.0: RDF Schema, W3C
Recommendation, 2004.
[17]
Russell Lincoln Ackoff, From data to wisdom, Journal of Applied Systems
Analysis 16 (1989), 3–9.
[18]
, On learning and the systems that facilitate it, Center for
Quality of Management Journal 5 (1996), no. 2, 27–35.
[19]
Eytan Adar, David R. Karger, and Lynn Andrea Stein, Haystack: Per-user
information environments, Proceedings of the Conference on Information and
Knowledge Management (CIKM ’99) (Kansas City, MO, US), ACM Press, 1999,
pp. 413–422.
[20]
Kalyanpur Aditya, Jennifer Golbeck, Michael Grove, and James Hendler, An
RDF editor and portal for the Semantic Web, Position Papers of the
Semantic Authoring, Annotation & Knowledge Markup Workshop at ECAI 2002
(Lyon, France), University of Maryland, 2002.
[21]
Jun-ichi Akahani, Kaoro Hiramatsu, and Kiyoshi Kogure, Coordinating
heterogeneous information services based on approximate ontology
translation, Proceedings of the AAMAS-2002 Workshop on Agentcities:
Challenges in Open Agent Systems, 2002.
[22]
Jun-ichi Akahani, Kaoro Hiramatsu, and Tetsuji Satoh, Approximate query
reformulation for ontology integration, Proceedings of the 2nd International
Semantic Web Conference, NTT Communication Science Laboratories, 2003.
[23]
Varol Akman and Mehmet Surav, Contexts, oracles, and relevance, Working
Papers of the AAAI Fall Symposium on Formalizing Context (Menlo Park,
California, USA) (Sasa Buvač, ed.), American Association for Artificial
Intelligence, American Association for Artificial Intelligence, 1995,
pp. 23–30.
Keywords: context, contextual reasoning
[24]
Boanerges Aleman-Meza, Chris Halaschek, J. Budak Arpinar, and Amit Sheth,
Context-aware semantic association ranking, Proceedings of the First
International Workshop on Semantic Web and Databases, LSDIS Lab,
University of Georgia, 2003, pp. 33–50.
[25]
Juan Luis Alonso, Cesar Carranza, Pablo Castells, Borja Foncillas, Rubén
Lara, and Mariano Rico, Semantic Web technologies for economic and
financial information management, 2003.
Keywords: xbrl rixml newsml mddl swiftml ebxml ifx ofx marketsml finance
[26]
George Anastassakis, Tim Ritchings, and Themis Panayiotopoulos,
Multi-agent systems as intelligent virtual environments, Lecture Notes
in Artificial Intelligence 2174 (2001), 381–395.
[27]
Muthukkaruppan Annamalai and Leon Sterling, Guidelines for constructing
reusable domain ontologies, Proceedings of the AAMAS’03 Workshop on
Ontologies in Agent Systems (Melbourne, Australia), 2003, pp. 71–74.
Keywords: agentlab, ontologies, agent applications and ontologies
[28]
Grigoris Antoniou and Frank van Harmelen, Web Ontology Language: OWL,
Handbook on Ontologies in Information Systems (S. Staab and R. Studer,
eds.), Springer-Verlag, 2003.
[29]
Franz Baader, Ian Horrocks, and Ulrike Sattler, Description logics as
ontology languages for the Semantic Web, Festschrift in honor of Jörg
Siekmann (Dieter Hutter and Werner Stephan, eds.), Lecture Notes in
Artificial Intelligence, Springer, 2003.
The vision of a Semantic Web has recently drawn considerable attention, both from academia and industry. Description logics are often named as one of the tools that can support the Semantic Web and thus help to make this vision reality. In this paper, we describe what description logics are and what they can do for the Semantic Web. Descriptions logics are very useful for defining, integrating, and maintaining ontologies, which provide the Semantic Web with a common understanding of the basic semantic concepts used to annotate Web pages. We also argue that, without the last decade of basic research in this area, description logics could not play such an important role in this domain.
[30]
James Backhouse, Carol Hsu, and Jimmy Tseng, Cutting the Gordian Knot?
— a behavioural lens on PKI interoperability, Proceedings of the 4th
International Conference on Electronic Commerce (ICEC2002), 2002.
Keywords: PKI Interoperability, Semantic Analysis, Trust Mechanisms
[31]
K. S. Barber, M. MacMahon, R. McKay, A. Goel, D. C. Han, J. Kim, D. N. Lam, and
C. E. Martin, An agent infrastructure implementation for leveraging and
collaboration in operational and experimental environments, Proceedings of
the 5th International Conference on Autonomous Agents (Agents-2001)
Workshop on Infrastructure for Agents, MAS, and Scalable MAS (Austin,
TX), University of Texas at Austin, 2001, pp. 41–52.
Sensible Agents have been engineered to solve distributed problems in uncertain, and dynamic domains. Each Sensible Agent is composed of four modules (Action Planner, Perspective Modeler, Conflict Resolution Advisor, and Autonomy Reasoner). These modules endow Sensible Agents with the ability to plan, model, resolve conflicts, and change agent system organization. Two components provide a variety of user-oriented features: the Sensible Agent Run-Time Environment (SARTE) and the Sensible Agent Testbed. The SARTE provides facilities for instantiating Sensible Agents, deploying a Sensible Agent system and monitoring run-time operations. The capabilities of each module, respective Sensible Agents, and the multi-agent system must be tested and evaluated. The Sensible Agents Testbed consists of tools facilitating automated generation of parameters for experimental runs, deterministic simulation, agent configuration and data acquisition. Experimentation allows for analysis of agent behavior, as well as proving or disproving hypotheses. The Sensible Agent Testbed provides a solid infrastructure for configurable and repeatable multi-agent experiments. The described infrastructure facilitates the inclusion of the 3rd party implementations at any level of the architecture. This allows other researchers with specific algorithms or implementations to plug in their work while leveraging the remainder of the infrastructure for agent operation.
[32]
Mihai Barbuceanu and Mark S. Fox, The architecture of an agent building
shell, Tech. report, Enterprise Integration Laboratory, University of
Toronto, 1996.
Keywords: multi-agent systems, coordination, agent modeling, conflict management, information distribution, shells, description logics
[33]
Sean Bechhofer, The DIG description logic interface: DIG/1.1,
University of Manchester, Oxford Road, Manchester, M13 9PL, 2003.
[34]
Sean Bechhofer, Ian Horrocks, Carole Goble, and Robert Stevens, OilEd:
a reason-able ontology editor for the semantic web, Proceedings of KI2001,
Joint German/Austrian conference on Artificial Intelligence (Vienna),
Lecture Notes in Computer Science, no. 2174, Springer-Verlag, September 2001,
pp. 396–408.
[35]
Dave Beckett, Redland RDF library, 2003.
Keywords: toolkit, semantic web
[36]
Gene Bellinger, Durval Castro, and Anthony Mills, Data, information,
knowledge, and wisdom, 2004.
[37]
Bryan Robert Bennett and Babis Theodoulidis, Towards a notion of personal
ontology.
[38]
B. Berendt, A. Hotho, and G. Stumme, Towards Semantic Web mining,
Proceedings of the 1st International Semantic Web Conference (ISWC02),
2002.
[39]
Tim Berners-Lee, Information management: A proposal, 1989.
[40]
, Semantic Web roadmap, September 1998.
[41]
, Web design issues: What the Semantic Web can represent, 1998.
[42]
, Why RDF model is different from the XML model, 1998.
[44]
Tim Berners-Lee, Dan Connolly, Sean Palmer, and Mark Nottingham,
cwm — a general-purpose data processor for the semantic web,
2004.
[45]
Tim Berners-Lee, James Hendler, and Ora Lassila, The Semantic Web,
Scientific American 284 (2001), no. 5, 34–43.
A new form of Web content that is meaningful to computers will unleash a revolution of new possibilities.
[46]
Chris Bizer and Jeremy Carroll, Modelling context using named graphs,
Tech. report, Semantic Web Interest Group, 2004.
In this document we describe our ongoing work on using named graphs for modelling context within Semantic Web applications. We give a short introduction into named graphs and define our understanding of context. Afterwards we describe several use cases which show how named graphs could be utilised for capturing context.
[47]
Andrew P. Black, Post-Javaism, IEEE Internet Computing (2004),
93–96.
Keywords: Smalltalk, Java, Architecture, Modernism, Post-modernism
[48]
Alain Bonnet, Artificial intelligence: Promise and performance, Prentice
Hall, London, 1985.
[49]
Valerie Bönström, Annika Hinze, and Heinz Schweppe, Storing RDF
as a graph, Proceedings of the First Latin American Web Congress
(LA-WEB’03), Institute of Computer Science, Freie Universität Berlin,
Germany, IEEE Computer Society, 2003.
[50]
Per Bothner, XML challenges to programming language design,
Proceedings of the 1st European Lisp and Scheme Workshop (Pascal
Constanza, ed.), 2004.
[51]
Paolo Bouquet, Fausto Giunchiglia, Frank van Harmelen, Luciano Serafini, and
Heiner Stuckenschmidt, C-OWL: Contextualizing ontologies,
Proceedings of the 2nd International Semantic Web Conference,
Springer-Verlag, 2003.
Ontologies are shared models of a domain that encode a view which is common to a set of different parties. Contexts are local models that encode a party’s subjective view of a domain. In this paper we show how ontologies can be contextualized, thus acquiring certain useful properties that a pure shared approach cannot provide. We say that an ontology is contextualized or, also, that it is a contextual ontology, when its contents are kept local, and therefore not shared with other ontologies, and mapped with the contents of other ontologies via explicit (context) mappings. The result is Context OWL (C-OWL), a language whose syntax and semantics have been obtained by extending the OWL syntax and semantics to allow for the representation of contextual ontologies.
[52]
M Bozzano, G. Delzanno, M. Martelli, V. Mascardi, and F. Zini, Logic
Programming & Multi-Agent Systems: a Synergic Combination for Applications
and Semantics, The Logic Programming Paradigm: a 25-Year Perspective (K. R.
Apt, V. W. Marek, M. Truszczynski, and D. S. Warren, eds.), Springer Verlag,
1999, pp. 5–32.
[53]
Patrick Brézillon, Context in human-machine problem solving: A
survey, 1996.
Context appears in AI as a challenge for the coming years as shown by the various scientific events focusing on context held since 1995. However, context is already considered in other domains, as Natural Language, although through few aspects of context. We present in this paper a survey of the literature dealing directly and explicitly with context whatever the domain is. This permits to have a clear view on the context in AI. One of the conclusions of this survey is to point out the…
[54]
Dan Brickley and Libby Miller, Friend of a Friend vocabulary
specification, 2003.
[55]
Jeen Broekstra, Christiaan Fluit, Arjohn Kampman, Frank van Harmelen, Heiner
Stuckenschmidt, Ravinder Bhogal, Anthony Scerri, Anita de Waard, and Erik van
Mulligen, The Drug Ontology Project for Elsevier, Proceedings
of the WWW’04 workshop on Application Design, Development and
Implementation Issues in the Semantic Web (New York), May 2004.
Keywords: Elsevier, Aduna, Collexis, Erasmus, Drug Ontology Project, thesaurus, PubMed, Emtree, Embase, SeRQL, Sesame, browser, DOPE Browser, Spectacle Cluster Map, Spectacle, Cluster Map, SOAP, query, interoperability, syntactic, semantic, XML, RDF
[56]
Eric Browne, The myth of self-describing XML, September 2003.
[57]
Joanna J. Bryson, The behaviour-oriented design of modular agent
intelligence, Proceedings of Agent Technology and Software Engineering
(AgeS 02) (Jörg P. Müller, ed.), University of Bath, Springer,
2002.
[58]
, Where should complexity go? Cooperation in complex agents with
minimal communication, Innovative Concepts for Agent-Based Systems (Walt
Truszkowski, Chris Rouff, and Mike Hinchey, eds.), Springer, 2003,
pp. 298–313.
[59]
Joanna J. Bryson, David Martin, Sheila I. McIlraith, and Lynn Andrea Stein,
Agent-Based Composite Services in DAML-S: The Behavior-Oriented Design
of an Intelligent Semantic Web, Web Intelligence (Ning Zhong, Jiming Liu,
and Yiyu Yao, eds.), Springer, 2003, pp. 37–58.
[60]
Paul Buhler and José Vidal, Semantic Web services as agent
behaviours, Agentcities: Challenges in Open Agent Environments (B Burg,
J. Dale, T. Finin, H. Nakashima, L. Padgham, C. Sierra, and S. Willmott,
eds.), Springer, 2003, pp. 25–31.
[61]
Alexander Burger, Pico Lisp — a radical approach to application
development, Proceedings of the 1st European Lisp and Scheme Workshop
(Pascal Constanza, ed.), 2004.
[62]
William C. Burkett, The myths of “standard” data semantics, XML
Journal 3 (2002), no. 11.
[63]
Vannevar Bush, As we may think, The Atlantic Monthly (1945).
[64]
Tuan-Dung Cao and Fabien Gandon, Integrating external sources in a
corporate semantic web managed by a multi-agent system, Proceedings of the
AAAI Spring Symposium on Agent-mediated Knowledge Management (AMKM)
(Luder van Elst, Virginia Dignum, and Andreas Abecker, eds.), Lecture Notes
in Artificial Intelligence, vol. 2926, Springer, 2003.
We first describe a multi-agent system managing a corporate memory in the form of a corporate semantic web. We then focus on a newly introduced society of agents in charge of wrapping external HTML documents that are relevant to the activities of the organization, by extracting semantic Web annotations using tailored XSLT templates.
[65]
Leslie Carr, Sean Bechhofer, Carole Goble, and Wendy Hall, Conceptual
linking: Ontology-based open hypermedia, Proceedings of the Tenth
International World Wide Web Conference (WWW10) (Hong Kong), May 2001.
[66]
Harry Chen, Tim Finin, and Anupam Joshi, An intelligent broker for
context-aware systems, Adjunct Proceedings of Ubicomp 2003, University of
Maryland, October 2003.
[67]
James R. Chen, Nathalie Mathé, and Shawn Wolfe, Collaborative
information agents on the World Wide Web, Proceedings of the 3rd ACM
Conference of Digital Libraries, ACM Press, 1998, pp. 279–280.
Keywords: intelligent agents, information access, collaborative system, knowledge-base, world wide web
[68]
Liren Chen and Katia Sycara, WebMate: A personal agent for browsing and
searching, Proceedings of the 2nd International Conference on Autonomous
Agents and Multi Agent Systems, AGENTS ’98, ACM Press, 1998,
pp. 132–139.
[69]
Wendy S. Chen, Learning query behaviour in the Haystack system,
Master’s thesis, Department of Electrical Engineering and Computer Science,
Massachusetts Institute of Technology, Cambridge, MA, USA, 2000.
[70]
Dan Connolly, Semantic Web application integration: Travel tools,
2003.
[71]
Monica Crubézy, Problem solving with Protégé knowledge
bases, 2003.
[72]
Hamish Cunningham, Atanas Kiryakov, and Ying Ding, Call for
participation, human language technology workshop at ISWC 2003, 2003.
[73]
Francisco Curbera, William A. Nagy, and Sanjiba Weerawarana, Web
Services: Why and how, 2001.
[74]
Paul Davidsson, Emergent societies of information agents, Cooperative
Information Agents, 2000, pp. 143–153.
[75]
John Davies, Richard Weeks, and Uwe Krohn, QuizRDF: Search technology
for the Semantic Web, Proceedings of the 37th Annual Hawaii
International Conference on System Sciences (HICSS’04), BTexact
Technologies, IEEE Computer Society, 2004, pp. 112–119.
Keywords: QuizRDF, RDFerret, RDFferret, knowledge management, ontology, RDF, RDF(S), annotation, low threshold, high ceiling
[76]
Randall Davis, Howard Shrobe, and Peter Szolovits, What is a Knowledge
Representation?, AI Magazine 14 (1993), no. 1, 17–33.
[77]
Keith S. Decker, Anandeep Pannu, Katia Sycara, and Mike Williamson,
Designing behaviors for information agents, Proceedings of the First
International Conference on Autonomous Agents (Agents ’97) (New York)
(W. Lewis Johnson and Barbara Hayes-Roth, eds.), ACM Press, 1997,
pp. 404–412.
[78]
Keith S. Decker and Katia Sycara, Intelligent adaptive information
agents, Working Notes of the AAAI-96 Workshop on Intelligent Adaptive
Agents (Portland, OR) (Ibrahim Imam and Larry Kerschberg, eds.), 1996.
[79]
Keith S. Decker, Katia Sycara, and Mike Williamson, Middle-agents for the
internet, Proceedings of the 15th International Joint Conference on
Artificial Intelligence, 1997.
[80]
Michael Denny, Ontology building: A survey of editing tools, 2002.
[81]
Edsger W. Dijkstra, The programming task considered as an intellectual
challenge, 1969.
[82]
, On the cruelty of really teaching computing science, 1988.
[83]
Li Ding, Lina Zhou, and Timothy Finin, Trust based knowledge outsourcing
for Semantic Web agents, Proceedings of the 2003 IEEE/WIC International
Conference on Web Intelligence (Baltimore MD, USA) (Jiming Liu, Chunnian
Liu, Matthias Klusch, Ning Zhong, and Nick Cercone, eds.), IEEE Computer
Society, 2003, pp. 379–387.
Keywords: trust, semantic web, relationships, reputation, aggregate trust evaluations, trust model, inconsistency.
[84]
Ying Ding, IR and AI: The role of ontology, 2001.
Keywords: information retrieval, artificial intelligence, lightweight ontology, co-occurence theory
[85]
, Ontology: The enabler for the Semantic Web, Journal of
Information Science 27 (2001), no. 6.
[86]
Ying Ding and Rob Engels, IR and AI: Using co-occurrence theory to
generate lightweight ontologies, 2001.
[87]
Ying Ding, Dieter Fensel, Michel Klein, and Borys Omelayenko, The
Semantic Web: Yet another hip?, 2002.
[88]
Ying Ding and Schubert Foo, Ontology research and development part 1 -
a review of ontology generation, Journal of Information Science
28(2) (2001).
Keywords: ontology generation, ontology, knowledge representation
[89]
Edd Dumbill, Finding friends with XML and RDF: FOAF, 2002.
[90]
H. W. Egdorf, A new Lisp machine, Proceedings of the 1st European
Lisp and Scheme Workshop (Pascal Constanza, ed.), 2004.
[91]
Hendrik Eshuis, Semantics and verification of UML activity diagrams for
workflow modelling, 2002.
[92]
Matthew P. Evett, William A. Andersen, and James A. Hendler, PARKA
knowledge base.
Keywords: PARKA Knowledge Base
[93]
Dieter Fensel, Triple-based computing, Research Report 2004-05-31,
Digital Enterprise Research Institute (DERI), May 2004.
[94]
Dieter Fensel, James Hendler, Henry Lieberman, and Wolfgang Wahlster,
Spinning the Semantic Web, MIT Press, Cambridge, Massachusetts,
2003.
[95]
Richard Fikes, Patrick Hayes, and Ian Horrocks, OWL-QL — a language
for deductive query answering on the Semantic Web, Tech. Report
KSL-03-14, Knowledge Systems Laboratory, Stanford University,
Stanford, CA, 94305-9020, USA, 2003.
This paper discusses the issues involved in designing a query language for the Semantic Web and presents the OWL Query Language (OWL-QL) as a candidate standard language and protocol for query answering dialogues among Semantic Web computational agents using knowledge represented in the W3C’s Ontology Web Language (OWL). OWL-QL is a formal language and precisely specifies the semantic relationships among a query, a query answer, and the knowledge base(s) used to produce the answer. Unlike standard database and Web query languages, OWL-QL supports query-answering dialogues in which the answering agent may use automated reasoning methods to derive answers to queries, as well as dialogues in which the knowledge to be used in answering a query may be in multiple knowledge bases on the Semantic Web, and/or where those knowledge bases are not specified by the querying agent. In this setting, the set of answers to a query may be of unpredictable size and may require an unpredictable amount of time to compute.
[96]
Richard Fikes, Jessica Jenkins, and Qing Zhou, Including domain-specific
reasoners with reusable ontologies, Tech. Report KSL-03-05, Knowledge
Systems Laboratory, Stanford University, Stanford, CA,
94305-9020, USA, 2003.
In this paper, we extend the methodology for developing intelligent systems that promotes building knowledge bases by assembling reusable ontologies and knowledge base modules from on-line libraries to include associating domain-specific reasoners with reusable ontologies and incorporating such reasoners into an intelligent system as part of the knowledge assembly process. The methodology is based on an existing hybrid reasoning architecture that provides an API and functional specification for special-purpose reasoners. We illustrate the methodology by describing a reusable time ontology and an associated set of domain-specific reasoners for the vocabulary of relations defined in that ontology. The ontology is designed to upport large-scale knowledge system applications by providing an easily-understandable, flexible, formally defined, and effective means of representing knowledge about time and the standard components and properties of calendars. The domain-specific reasoners support “telling” and “asking” operations for temporal ordering relations on time points and the Allen relations on time intervals.
[97]
Tim Finin and Anupam Joshi, Agents, trust, and information access on the
Semantic Web, 2002, pp. 30–35.
[98]
Christiaan Fluit, Marta Sabou, and Frank van Harmelen, Supporting user
tasks through visualisation of light-weight ontologies, Handbook on
Ontologies in Information Systems (S. Staab and R. Studer, eds.),
Springer-Verlag, 2003.
[99]
Ian Foster, Carl Kesselman, Jeffrey M. Nick, and Steven Tuecke, Grid
services for distributed system integration, Computer 35 (2002),
no. 6, 37–46.
[100]
Stan Franklin and Art Graesser, Is it an agent, or just a program?: A
taxonomy for autonomous agents, Proceedings of the Third International
Workshop on Agent Theories, Architectures, and Languages, Institute for
Intelligent Systems, University of Memphis, Springer-Verlag, 1996.
The advent of software agents gave rise to much discussion of just what such an agent is, and of how they differ from programs in general. Here we propose a formal definition of an autonomous agent which clearly distinguishes a software agent from just any program. We also offer the beginnings of a natural kinds taxonomy of autonomous agents, and discuss possibilities for further classification. Finally, we discuss subagents and multiagent systems.
[101]
Flavius Frasincar, Geert-Jan Houben, Richard Vdovjak, and Peter Barna,
RAL: an algebra for querying RDF, Proceedings of the Third
International Conference on Web Information Systems Engineering (WISE),
Eindhoven University of Technology, IEEE Computer Society, 2002,
pp. 173–181.
[102]
Eric Freeman and David Gelernter, Lifestreams: A storage model for
personal data, SIGMOD Record (ACM Special Interest Group on Management
of Data) 25 (1996), no. 1, 80–86.
[103]
Frederico L. G. Freitas and Guilherme Bittencourt, An ontology-based
architecture for cooperative information agents, Proceedings of the 2003
International Joint Conference on Artificial Intelligence, vol. 18, 2003,
pp. 37–42.
[104]
Michael R. Genesereth and Nils J. Nilsson, Logical foundations of
artificial intelligence, Morgan Kaufmann Publishers Inc., Los Altos, CA,
1987.
[105]
Jeremy Gibbons, Metamorphisms and streaming algorithms.
Abstract: Unfolds generate data structures, and folds consume them. A hylomorphism is a fold after an unfold, generating then consuming a virtual data structure. A metamorphism is the opposite composition, an unfold after a fold; typically, it will convert from one data representation to another. In general, metamorphisms are less interesting than hylomorphisms: there is no automatic fusion to deforest the intermediate virtual data structure. However, under certain conditions fusion is possible: some of the work of the unfold can be done before all of the work of the fold is complete. This permits streaming metamorphisms, and among other things allows conversion of infinite data representations. We present the theory of metamorphisms and outline some examples.
[106]
Verena Giller, Manfred Tscheligi, Johann Schrammel, Peter Frölich, Birgit
Rabl, Robert Kosara, Silvia Miksch, and Helwig Hauser, Experimental
evaluation of semantic depth of field, a preattentive method for
Focus+Context visualization, Tech. report, CURE: Center for Usability
Engineering and Vienna University of Technology and VRVis Research
Center, 2002.
[107]
Fausto Giunchiglia and Chiara Ghidini, Local models semantics, or
contextual reasoning = locality + compatibility, Artificial Intelligence
127 (2001), no. 2, 221–259, earlier version presented at KR98.
In this paper we present a new semantics, called Local Models Semantics, and use it to provide a foundation to reasoning with contexts. This semantics captures and makes precise the two main intuitions underlying contextual reasoning: (i) reasoning is mainly local and uses only part of what is potentially available (e.g., what is known, the available inference procedures), this part is what we call context (of reasoning); however (ii) there is compatibility among the reasoning performed in different contexts. We validate our semantics by formalizing two important forms of contextual reasoning: reasoning with viewpoints and reasoning about belief.
[108]
Jeremy Goecks and Elizabeth Mynatt, Enabling privacy management in
ubiquitous computing environments through trust and reputation systems,
Proceedings of CSCW 2002 (New Orleans, LA, USA), GVU Center,
College of Computing, Georgia Tech, 2002.
[109]
Jennifer Golbeck, Michael Grove, Bijan Parsia, Aditya Kalyanpur, and James
Hendler, New tools for the Semantic Web, Proceedings of 13th
International Conference on Knowledge Engineering and Knowledge Management
EKAW02 (Siguenza, Spain), University of Maryland, 2002.
[110]
Jennifer Golbeck and James Hendler, Accuracy of metrics for inferring
trust and reputation in Semantic Web-based social networks, Proceedings of
EKAW’04, 2004.
[111]
Jennifer Golbeck, James Hendler, and Bijan Parsia, Trust networks on the
Semantic Web, Proceedings of Cooperative Intelligent Agents 2003
(Helsinki, Finland), University of Maryland, 2003.
[112]
Bernardo Cuenca Grau, A possible simplification of the Semantic Web
architecture, Proceedings of the Thirteenth International World Wide Web
Conference (WWW2004), University of Maryland, 2004.
Keywords: Semantic Web, Resource Description Framework (RDF), Resource Description Framework Schema (RDF-Schema), Ontology Web Language(OWL), Description Logics
[113]
Andrew Greenyer, The use of a learning classifier system JXCS, Tech.
report, The Database Group, Colston Tower, Colston Street,
Bristol, BS1 4UH, 2001.
[114]
Benjamin Grosof, How SW Rules + Ontologies Connect to Procedural Aspects
of SW Services, April 2003, presentation at Semantic Web Services
Language committee of Semantic Web Services Coalition.
[115]
Thomas R. Gruber, Toward principles for the design of ontologies used for
knowledge sharing, Technical Report KSL-93-04 (Revised), Knowledge
Systems Laboratory, Stanford University, 1993.
[116]
, A translation approach to portable ontology specifications,
Technical Report KSL-92-71 (Revised), Knowledge Systems Laboratory,
Stanford University, 1993.
[117]
Michael Grüninger and Mark S. Fox, Methodology for the design and
evaluation of ontologies, Proceedings of the Workshop on Basic
Ontological Issues in Knowledge Sharing, IJCAI-95, April 1995.
[118]
Nicola Guarino and Pierdaniele Giaretta, Ontologies and knowledge bases:
Towards a terminological clarification, Towards Very Large Knowledge Bases:
Knowledge Building and Knowledge Sharing (N Mars, ed.), IOS Press, 1995,
pp. 25–32.
[119]
Nicola Guarino, Claudio Masola, and Guido Vetere, OntoSeek:
Content-based access to the web, IEEE Intelligent Systems 14
(1999), no. 3, 70–80.
[120]
Ramanathan V. Guha, Contexts: A formalization and some applications,
Ph.D. thesis, Stanford University, 1991.
[121]
Ramanathan V. Guha and Patrick Hayes, LBase: Semantics for languages of
the Semantic Web, W3C Working Group Note, 2003.
[122]
Ramanathan V. Guha and Rob McCool, A system for integrating web services
into a global knowledge base.
[123]
, TAP: A Semantic Web platform, 2002.
[124]
, TAP homepage, 2002.
[125]
Ramanathan V. Guha, Rob McCool, and Eric Miller, Semantic Search
(TAP), 2002.
[126]
Erik Gyllenswärd, Mladen Kap, and Rikard Land, Information organizer
— a comprehensive view on reuse, Proceedings of the 4th International
Conference on Enterprise Information Systems (ICEIS), April 2002.
Keywords: Reuse, integration, legacy systems, Business Object Model, software components, extensible, lifecycle support
[127]
Volker Haarslev and Ralf Möller, Racer: An OWL reasoning agent for
the Semantic Web.
[128]
, Racer: A core inference engine for the Semantic Web,
Proceedings of the 2nd International Workshop on Evaluation of Ontology-based
Tools (Montreal, Canada) (York Sure and Oscar Corcho, eds.), CEUR
Workshop Proceedings, vol. 87, 2003.
[129]
, RACER User’s Guide and Reference Manual version 1.7.7,
2003.
[130]
Alon Y. Halevy, Zachary G. Ives, Peter Mork, and Igor Tatarinov, Piazza:
Data management infrastructure for Semantic Web applications, Proceedings
of the Twelfth International World Wide Web Conference (WWW2003), ACM
Press, 2003, pp. 556–567.
[131]
Stephen Harris and Nicholas Gibbins, Semantic Web storage with
3store, October 2003.
[132]
Jeff Heflin, James Hendler, and Sean Luke, Reading between the lines:
Using SHOE to discover implicit knowledge from the Web, Proceedings of
the AAAI-98 Workshop on AI and Information Integration, 1998.
[133]
James Hendler, Tim Berners-Lee, and Eric Miller,
Integrating applications
on the Semantic Web, Journal of the Institute of Electrical Engineers of
Japan
122 (2002), no. 10, 676–680, in Japanese; English from
http://www.w3.org/2002/07/swint.
Keywords: reuse, automated, web, semantic web, rdf, applications, web services, xml
[134]
Andreas Hess and Nick Kushmerick, Learning to attach semantic metadata to
web services, Proceedings of the 2nd International Semantic Web
Conference, Springer-Verlag, 2003, pp. 258–273.
[136]
Randall W. Hill, Jr, Perceptual grouping and attention in a multi-agent
world, International Conference on Autonomous Agents, 1999.
[137]
Ian Horrocks and Peter F. Patel-Schneider, Three theses of representation
in the Semantic Web, Proceedings of the Twelfth International World Wide
Web Conference (WWW2003), 2003, pp. 39–47.
[138]
, A proposal for an OWL rules language, Proceedings of the
Thirteenth International World Wide Web Conference (WWW2004), ACM
Press, 2004, To appear.
[139]
Ian Horrocks, Peter F. Patel-Schneider, and Frank van Harmelen, From
SHIQ and RDF to OWL: The making of a Web Ontology Language, Web
Semantics 1 (2003), no. 1, 7–26.
Keywords: ontologies, semantic web, description logics, frames, rdf
[140]
Ian Horrocks and Sergio Tessaris, Querying the semantic web: a formal
approach, Proceedings of the 13th International Semantic Web Conference
(ISWC 2002) (Ian Horrocks and James Hendler, eds.), Lecture Notes in Computer
Science, no. 2342, Springer-Verlag, 2002, pp. 177–191.
[141]
HP Labs, Semantic blogging demonstrator.
[142]
, Semantic Web tools: Jena Toolkit, Joseki, BrownSauce,
2003.
[143]
J. Hughes, Why Functional Programming Matters, Computer Journal
32 (1989), no. 2, 98–107.
[144]
Kit Hui, Stuart Chalmers, Peter Gray, and Alun Preece, Experience in
using RDF in agent-mediated knowledge architectures, Agent-Mediated
Knowledge Management: Papers from the 2003 AAAI Spring Symposium (Aberdeen,
AB24 3UE, Scotland), vol. SS-03-01, University of Aberdeen, AAAI
Press, 2003, pp. 88–89.
We report on experience with using RDF to provide a rich content language for use with FIPA agent toolkits, and on RDFS as a metadata language. We emphasise their utility for programmers working in agent applications and their value in Agent-Oriented Software Engineering. Agent applications covered include Intelligent Information Agents, and agents forming Virtual Organisations. We believe our experience vindicates more direct use of RDF, including use of RDF triples, in programming knowledge architectures for a variety of applications.
[145]
International Committee for Information Technology Standards, American
National Standard Dictionary of Information Technology.
[146]
International DOI Foundation, DOI glossary, 2003.
[147]
Aditya Kalyanpur, Semantic Markup, Ontology and RDF Editor.
[148]
, PhotoSMORE module for the Semantic Markup, Ontology and RDF
Editor, April 2003.
[149]
F. Kamperman and B. van Rijnsoever, Conditional access system
interoperability through software downloading, IEEE Transactions on
Consumer Electronics 47 (2001), no. 10, 47–54.
[150]
David R. Karger and Dennis Quan, Prerequisites for a personalizable user
interface, Proceedings of the Intelligent User Interfaces (IUI) 2004
Workshop on Behavior-based User Interface Customization, 2004.
[151]
Greg Karvounarakis, RDF query languages: A state-of-the-art, Tech.
report, Institute of Computer Science, FORTH, Vassilika Vouton, P.O.
Box 1385, GR 711 10, Heraklion, Greece, 2002.
[153]
Brian W. Kernighan and P. J. Plauger, Software tools, Addison-Wesley,
Reading, Mass., 1976.
[154]
Brian Kettler, Position paper for the International Semantic Web
Workshop 2001: Building a Semantic Web for the intelligence community,
Tech. report, ISX Corporation, 2001.
Keywords: espionage, semantic web
[155]
, Update on the Horus project: Building the Semantic Web for
Intelink, 2002.
Keywords: Horus espionage intelink darpa imo bbn daml isx corporation
[156]
Gregor Kiczales, John Lamping, Anurag Menhdhekar, Chris Maeda, Cristina Lopes,
Jean-Marc Loingtier, and John Irwin, Aspect-oriented programming,
Proceedings European Conference on Object-Oriented Programming (Mehmet
Aksit and Satoshi Matsuoka, eds.), vol. 1241, Springer-Verlag, Berlin,
Heidelberg, and New York, 1997, pp. 220–242.
This paper reports on our work developing programming techniques that make it possible to clearly express those programs that OOP (and POP) fail to support. We present an analysis of why some design decisions have been so difficult to cleanly capture in actual code. We call the issues these decisions address aspects, and show that the reason they have been hard to capture is that they cross-cut the system’s basic functionality. We present the basis for a new programming technique, called…
[157]
Graham Klyne, Contexts for RDF information modelling, October 2000.
This memo describes some experimental work that is being undertaken with the goal of simplifying the application of RDF to a number of information modelling problems, particularly involving relationships between physical-world objects and trust modelling. It is our goal that, by using contexts, a degree of modularity can be introduced that will ease the construction of RDF information models.
[158]
N Koiso-Kanttila, Consumers on the web: Identification of usage
patterns, April 2003.
Keywords: usage patterns consumers web identification
[159]
Daniel Kuokka and Larry Harada, Supporting information retrieval via
matchmaking, AAAI Spring Symposium on Information Gathering, Lockheed
Research Labs, 1995.
[160]
Patrick Lambrix, Description logics.
[161]
John Lamping and Ramana Rao, Laying out and visualizing large trees using
a hyperbolic space, Proceedings of the ACM Symposium on User Interface
Software and Technology, ACM Press, 1994, pp. 13–14.
[162]
Rikard Land and Ivica Crnkovic, Software systems integration and
architectural analysis — a case study, Proceedings of the International
Conference on Software Maintenance (ICSM) (Amsterdam, Netherlands),
2003.
Software systems no longer evolve as separate entities but are also integrated with each other. The purpose of integrating software systems can be to increase user-value or to decrease maintenance costs. Different approaches, one of which is software architectural analysis, can be used in the process of integration planning and design.
This paper presents a case study in which three software systems were to be integrated. We show how architectural reasoning was used to design and compare integration alternatives. In particular, four different levels of the integration were discussed (interoperation, a so-called Enterprise Application Integration, an integration based on a common data model, and a full integration). We also show how cost, time to delivery and maintainability of the integrated solution were estimated.
On the basis of the case study, we analyze the advantages and limits of the architectural approach as such and conclude by outlining directions for future research: how to incorporate analysis of cost, time to delivery, and risk in architectural analysis, and how to make architectural analysis more suitable for comparing many aspects of many alternatives during development. Finally we outline the limitations of architectural analysis.
Keywords: Architectural Analysis, Enterprise Application Integration, Information Systems, Legacy Systems, Software Architecture, Software Integration
[163]
Ora Lassila, Enabling Semantic Web programming by integrating RDF and
Common Lisp, Tech. report, Nokia Research Center, 5 Wayside Road,
Burlington, Massachusetts, USA, 2001.
This paper introduces Wilbur, an RDF and DAML toolkit implemented in Common Lisp. Wilbur exposes the RDF data model as a frame-based representation system; an object-oriented view of frames is adopted, and RDF data is integrated with the host language by addressing issues of input/output, data structure compatibility, and error signaling. Through seamless integration we have achieved a programming system well suited for building Semantic Web applications.
[164]
Mikko Laukkanen, Kim Viljanen, Mikko Apiola, Petri Lindgren, and Eero
Hyvönen, Towards ontology-based yellow page services, Proceedings
of the Thirteenth International World Wide Web Conference (WWW2004),
2004.
This paper discusses the possibilities of the Semantic Web technologies in both annotating services and delivering relevant services to end-users. We propose an ontology based mechanism for both advertising and finding the services. The essential parts of the system are ontologies for describing and storing service advertisements, a semantic service finder for the end-user, and a semantic service annotation editor for service providers.
[165]
Fernanda Lima and Daniel Schwabe, Application modeling for the Semantic
Web, Proceedings of the First Latin American Web Congress (LA-WEB’03),
PUC-RIO, IEEE Computer Society, 2003, pp. 93–102.
In this article we present a method for the design and implementation of Web Applications for the Semantic Web. Based on the “Object Oriented Hypermedia Design Method” approach, we used ontology concepts to define an application conceptual model, extending the expressive power of the original method. The navigational model definitions use a query language capable of querying both schema and instances, enabling the specification of flexible access structures. Additionally, we propose the use of faceted access structures to improve the selection of navigational objects organized by multiple criteria. Finally, we present an implementation architecture that allows the direct use of the application specifications when deriving a final application implementation.
[166]
Jimmy Lin, Dennis Quan, Vineet Sinha, Karun Bakshi, David Huynh, Boris Katz,
and David R. Karger, What makes a good answer? the role of context in
question answering, Proceedings of INTERACT 2003, 2003.
[167]
Craig Linn, Semantic reliability in distributed systems: Ontology issues
and system engineering, Proceedings of the 2003 IEEE/WIC International
Conference on Web Intelligence (Jiming Liu, Chunnian Liu, Matthias Klusch,
Ning Zhong, and Nick Cercone, eds.), IEEE Computer Society, 2003,
pp. 292–300.
Keywords: semantic reliability, semantic mismatch, variance, semiotics, signs, distributed systems, web services, reliability, ontologies.
[168]
Chris Lucas, Classifier, IFS, L-Systems, and beyond.
[169]
Sean Luke and James Heflin, SHOE 1.0 proposed specification.
[170]
Sean Luke, Lee Spector, David Rager, and James Hendler, Ontology-based
web agents, Proceedings of the First International Conference on Autonomous
Agents (Agents’97) (Marina del Rey, CA, USA) (W Lewis Johnson and Barbara
Hayes-Roth, eds.), ACM Press, 1997, pp. 59–68.
[171]
Carsten Lutz, Description logics, 2004.
[172]
Bernardo Magnini, Luciano Serafini, and Manuela Speranza, Linguistic
based matching of local ontologies, Proceedings of the AAAI-02 Workshop on
Meaning Negotiation (MeaN-02), 2002.
[173]
Thomas W. Malone, Kenneth R. Grant, Franklin A. Turbak, Stephen A. Brobst, and
Michael D. Cohen, Intelligent information-sharing systems,
Communications of the ACM 30 (1987), no. 5, 390–402.
[174]
Daniel J. Mandell and Sheila A. McIlraith, Adapting BPEL4WS for the
Semantic Web: The bottom-up approach to web service interoperation,
Proceedings of the 2nd International Semantic Web Conference (Stanford,
CA, 94305-9020, USA), 2003.
Towards the ultimate goal of seamless interaction among networked programs and devices, industry has developed orchestration and process modeling languages such as XLANG, WSFL, and recently BPEL4WS. Unfortunately, these efforts leave us a long way from seamless interoperation. Researchers in the Semantic Web community have taken up this challenge proposing top-down approaches to achieve aspects of Web Service interoperation. Unfortunately, many of these efforts have been disconnected from emerging industry standards, particularly in process modeling. In this paper we take a bottom-up approach to integrating Semantic Web technology into Web services. Building on BPEL4WS, we present integrated Semantic Web technology for automating customized, dynamic binding of Web services together with interoperation through semantic translation. We discuss the value of semantically enriched service interoperation and demonstrate how our framework accounts for user-defined constraints while gaining potentially successful execution pathways in a practically motivated example. Finally, we provide an analysis of the forward-looking limitations of frameworks like BPEL4WS, and suggest how such specifications might embrace semantic technology at a fundamental level to work towards fully automated Web service interoperation.
[175]
Peter Marendy, A review of World Wide Web searching techniques,
focusing on hits and related algorithms that utilise the link topology of the
World Wide Web to provide the basis for a structure based search
technology.
[176]
Ryusuke Masuoka, Bijan Parsia, and Yannis Labrou, Task computing — the
Semantic Web meets pervasive computing, Proceedings of the 2nd
International Semantic Web Conference, Springer-Verlag, 2003,
pp. 866–881.
[177]
John McCarthy and Sassa Buvac, Formalizing context (expanded
notes), 81 (1998), 13–50.
[178]
Deborah L. McGuinness, Ontologies come of age, Spinning the Semantic
Web (Dieter Fensel, James Hendler, Henry Lieberman, and Wolfgang Wahlster,
eds.), MIT Press, Cambridge, Massachusetts, 2003, pp. 171–194.
[179]
Sheila A. McIlraith, Tran Cao Son, and Honglei Zeng, Semantic Web
services, IEEE Intelligent Systems 16 (2001), no. 2, 46–53.
[180]
Donald P. McKay, Jon Pastor, Robin McEntire, and Tim Finin, An
architecture for information agents, Advanced Planning Technologies
(A. Tate, ed.), AAAI Press, Menlo Park, CA, USA, 1996.
[181]
Filippo Menczer, Richard K. Belew, and Wolfram Willuhn, Artificial life
applied to adaptive information agents, AAAI Spring Symposium on
Information Gathering, 1995.
We propose a model, inspired by recent artificial life theory, applied to the problem of retrieving information from a large, distributed collection of documents such as the World Wide Web. A population of agents is evolved under density dependent selection for the task of locating information for the user. The energy necessary for survival is obtained from both environment and user in exchange for appropriate information. By competing for relevant documents, the agents robustly adapt to their information environment and are allocated to efficiently exploit shared resources. We illustrate the roles played by document locality, adaptive search strategies, and relevance feedback, in the information gathering process.
[182]
Ralf Möller, Ronald Cornet, and Volker Haarslev, Graphical interfaces
for Racer: Querying DAML+OIL and RDF documents, Proceedings of the
2003 International Workshop on Description Logics (DL-2003 (Diego
Calvanese, Giuseppe De Giacomo, and Enrico Franconi, eds.), CEUR Workshop
Proceedings, vol. 81, 2003, pp. 255–259.
Keywords: RICE, A-boxes, T-boxes, OilEd
[183]
Ralf Möller and Volker Haarslev, Description logics for the Semantic
Web: Racer as a basis for building agent systems, KI — Zeitschrift
für Künstliche Intelligenz (special issue on Semantic Web)
(2003), no. 3, 10–15.
[184]
Tamara Munzner, H3: Laying out large directed graphs in 3D hyperbolic
space, Tech. report, Stanford University, 360 Gates Bldg 3B, Stanford, CA,
94305, 1997.
[185]
, Drawing large graphs with H3Viewer and Site Manager (system
demonstration), LNCS: GD ’98: Symposium on Graph Drawing, Stanford
University, Springer-Verlag, 1998.
[186]
, Exploring large graphs in 3D hyperbolic space, IEEE
Computer Graphics and Applications 18 (1998), no. 4, 18–23.
Drawing graphs as nodes connected by links is visually compelling but computationally difficult. Hyperbolic space and spanning trees can reduce visual clutter, speed up layout, and provide fluid interaction.
[187]
Bonnie A. Nardi, Steve Whittaker, and Erin Bradner, Interaction and
outeraction: instant messaging in action, Proceedings of the 2000 ACM
Conference on Computer Supported Cooperative Work, ACM Press, 2000,
pp. 79–88.
[188]
Bonnie A. Nardi, Steve Whittaker, Ellen Isaacs, Mike Creech, Jeff Johnson, and
John Hainsworth, ContactMap: Integrating communication and
information through visualizing personal social networks, Communications of
the ACM (2002).
[189]
National Library of Medicine, Unified Medical Language System
(UMLS), 2003.
Keywords: medical ontology
[190]
Mark E. J. Newman, The structure and function of complex networks, SIAM
Review 45 (2003), 167–256.
Inspired by empirical studies of networked systems such as the Internet, social networks, and biological networks, researchers have in recent years developed a variety of techniques and models to help us understand or predict the behavior of these systems. Here we review developments in this field, including such concepts as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks.
[191]
Nielsen//NetRatings, 2002.
[192]
Mikael Nilsson, Matthias Palmér, and Ambjörn, Semantic Web
metadata for e-learning — some architectural guidelines, Proceedings of
the Eleventh International World Wide Web Conference (WWW2002), May 2002.
Keywords: meta-data, e-learning, knowledge community, p2p
[193]
Rolf Nossum and Luciano Serafini, Multicontext logic for semigroups of
contexts, Proceedings of the Artificial Intelligence, Automated Reasoning,
and Symbolic Computation Joint International Conference 2002 (J Calmet,
B. Benhamou, O. Caprotti, L. Henocque, and V. Sorge, eds.), Springer-Verlag,
2002, p. 90.
Keywords: Integration of Logical Reasoning and Computer Algebra, Logic and Symbolic Computing, Reasoning
[194]
N F. Noy, M. Sintek, S. Decker, M. Crubezy, R. W. Ferguson, and M. A. Musen,
Creating Semantic Web contents with Protege-2000, IEEE Intelligent
Systems 16 (2001), no. 2, 60–71.
[195]
Natalya F. Noy and Deborah L. McGuinness, Ontology development 101: A
guide to creating your first ontology, Tech. Report KSL-01-05, Knowledge
Systems Laboratory, Stanford University, Stanford, CA, 94305,
USA, March 2001.
Ontologies have become core components of many large applications yet the training material has not kept pace with the growing interest. This paper addresses the issues of why one would build an ontology and presents a methodology for creating ontologies based on declarative knowledge representation systems. It leverages the two authors experiences building and maintaining ontologies in a number of ontology environments including Protege-2000, Ontolingua, and Chimaera. It presents the methodology by example utilizing a tutorial wines knowledge base example. While it is aimed at users of frame-based systems, it can be useful for building ontologies in any object-centered system.
[196]
Jay F. Nunamaker, Jr., Nicholas C. Romano, Jr., and Robert Owen Briggs, A
framework for collaboration and knowledge management, Proceedings of the
34th Hawai’i International Conference on System Sciences (Washington D.C.,
USA), IEEE Computer Society, January 2001, pp. 1060-1072.
Two of the most heralded recent Information Technology (IT) advances are Knowledge Management (KMS) and Collaborative Information Systems (CIS), yet neither has become a mainstream part of how many companies and knowledge workers (KWs) accomplish real work on a daily basis. We propose here two conceptual hierarchies for each of the new technologies that we believe independently may provide a structure for organizations and individuals to assess their current level of capability in each area. Further, we assert that the two hierarchies are complementary and can be integrated to provide a framework for IT capability in terms of Intellectual Bandwidth (IB.) In this paper we describe the two hierarchies and then present the integrated framework and introduce the concept of IB as the sum of an organizations’ CIS and KMS capabilities. Finally, we map sample technologies into the framework and explain how the technologies enable individuals, teams and organizations to achieve various levels of KIS and CIS capability. Future research in this area will focus on developing and validating constructs and measures of IB in terms of both KMS and CIS.
[197]
Daniel Oberle, Steffen Staab, Rudi Studer, and Raphael Volz, Supporting
application development in the semantic web, ACM Transactions on Internet
Technology 4 (2004), no. 4, To appear.
[198]
Jörg Ontrup and Helge Ritter, Hyperbolic self-organizing maps for
semantic navigation, Proceedings of NIPS*2001 (D-33501 Bielefeld,
Germany), 2001.
Keywords: semantic space, tesselation, hyperbolic plane, gaussian curvature, neighborhood, categorisation
[199]
Gultekin Ozsoyoglu and Abdullah Al-Hamdani, Web information resource
discovery: Past, present and future, Proceedings of the 18th International
Symposium on Computer and Information Sciences (Cleveland, Ohio) (Adnan
Yazici and Cevat Sener, eds.), Case Western Reserve University,
Springer-Verlag, 2003, pp. 9–18.
[200]
Luigi Pagliarini, Ariel Dolan, Filippo Menczer, and Henrik Hautop Lund,
ALife meets Web: Lessons learned, Lecture Notes in Computer
Science 1434 (1998).
[201]
S. K. Pal, V. Talwar, and P. Mitra, Web mining in soft computing
framework: Relevance, 2002.
[202]
Matthias Palmér, Ambjörn Naeve, and Fredrik Paulsson, The SCAM
framework: Helping Semantic Web applications to store and access
metadata, Proceedings of the 1st European Semantic Web Symposium, May
2004.
In this paper we discuss the design of the SCAM framework, which aims to simplify the storage and access of metadata for a variety of different applications that can be built on top of it. A basic design principle of SCAM is the aggregation of metadata into two kinds of sets of different granularity (SCAM records and SCAM contexts). These sets correspond to the typical access needs of an applications with regard to metadata, and they constitute the foundation upon which access control is provided.
[203]
Sean B. Palmer, RDFe — a schema-aware RDF editor, October 2003.
[204]
Terry R. Payne, Massimo Paolucci, Rahul Singh, and Katia Sycara,
Communicating agents in open multi agent systems, Proceedings of First
GSFC/JPL Workshop on Radical Agent Concepts (WRAC), 2002, pp. 365–371.
[205]
Terry R. Payne, Rahul Singh, and Katia Sycara, RCal: A case study on
Semantic Web agents, Tech. report, Carnegie Mellon University, 2002.
[206]
Asunción Gómez Pérez, Michael Grüninger, Heiner Stuckenschmidt,
and Michael F. Uschold (eds.), Proceedings of the IJCAI-01 workshop
on ontologies and information sharing, August 2001.
[207]
Zhun Qiu, Milind Tambe, and Hyuckchul Jung, Towards flexible negotiation
in teamwork, Proceedings of the Third Annual Conference on Autonomous
Agents, 1999, pp. 400–401.
[208]
Dennis Quan, Karun Bakshi, David Huynh, and David R. Karger, User
interfaces for supporting multiple categorization, Proceedings of INTERACT
2003, MIT CSAIL, 2003.
[209]
Dennis A. Quan, Designing end user information environments built on
semistructured data models, Ph.D. thesis, Department of Electrical
Engineering and Computer Science, Massachusetts Institute of Technology,
Cambridge, MA, USA, 2003.
[210]
, The friendly Semantic Web (presentation), 2003, presentation
at World Wide Web 2003 Developer’s Day.
[211]
Dennis A. Quan, David Huynh, and David R. Karger, Haystack: A platform
for authoring end user Semantic Web applications, Proceedings of the 2nd
International Semantic Web Conference (Cambridge, MA, USA), MIT
CSAIL, 2003.
[212]
Dennis A. Quan, David Huynh, David R. Karger, and Robert Miller, User
interface continuations, Proceedings of the Sixteenth Annual ACM Symposium
on User Interface Software and Technology (UIST 2003), MIT CSAIL, 2003.
[213]
Dennis A. Quan, David F. Huynh, Vineet Sinha, and David R. Karger,
Metadata-supported agent infrastructure, Proceedings of the 2002
Student Oxygen Workshop (Cambridge, MA, USA), MIT Artificial
Intelligence Laboratory, 2002.
[214]
Dennis A. Quan, David F. Huynh, Vineet Sinha, David R. Karger, and Marina
Zhurakhinskaya, Basic concepts for managing semi-structured information
in Haystack, Proceedings of the 2002 Student Oxygen Workshop
(Cambridge, MA, USA), MIT CSAIL, 2002.
[215]
Anand Ranganathan, Ontologies in a pervasive computing environment,
2003.
[216]
Jinghai Rao, Toward the composition of Semantic Web services,
Proceedings of the Second International Workshop on Grid and Cooperative
Computing (Shanghai, China), Lecture Notes in Computer Science, no. 3033,
Springer-Verlag, December 2003.
This paper introduces a method for automatic composition of semantic web services using linear logic theorem proving. The method uses semantic web service language (DAML-S) for external presentation of web services, and, internally, the services are presented by extralogical axioms and proofs in linear logic. Linear logic(LL)[2], as a resource conscious logic, enables us to define the attributes of web services formally (in particular, qualitative and quantitative value of non-functional attributes). The subtyping rules that are used for semantic reasoning are presented as linear logic implication. We propose a system architecture where the DAML-S parser, linear logic theorem prover and semantic reasoner can work together. This architecture has been implemented in Java programming language.
[217]
William J. Rapaport, Implementation is semantic interpretation, Tech.
Report 97–15, State University of New York at Buffalo, USA, 1997.
What is the computational notion of “implementation”? It is not individuation, instantiation, reduction, or supervenience. It is, I suggest, semantic interpretation.
[218]
Chris Reed, Timothy J. Norman, and Nicholas R. Jennings, Negotiating the
semantics of agent communication languages, Computational Intelligence
18 (2002), no. 2.
This paper presents a formal framework and outlines a method that autonomous agents can use to negotiate the semantics of their communication language at run-time. Such an ability is needed in open multi-agent systems so that agents can ensure they understand the implications of the utterances that are being made and so that they can tailor the meaning of the primitives to best fit their prevailing circumstances. To this end, the semantic space framework provides a systematic means of classifying the primitives along multiple relevant dimensions. This classification can then be used by the agents to structure their negotiation (or semantic fixing) process so that they converge to the mutually agreeable semantics that are necessary for coherent social interactions.
[219]
Stephen L. Reed and Douglas B. Lenat, Mapping ontologies into Cyc,
Proceedings of the AAAI 2002 Conference Workshop on Ontologies for the
Semantic Web (Edmonton, Canada), Cycorp, Inc., July 2002.
[220]
Carlo Revelli, The role of intelligent agents: a difficult definition
(extract from “Strategic Internet Intelligence”, Dunod, 2000), 2000.
[221]
Christophe Rhodes, Maintaining portable Lisp programs, 2004.
Keywords: read-time feature conditionals, portability, common lisp, features
[222]
Elaine Rich and Kevin Knight, Artificial intelligence, 2nd ed., McGraw
Hill, New York, 1991.
[223]
Jonathan Robie, Lars Marius Garshol, Steve Newcomb, Matthew Fuchs, Libby
Miller, Dan Brickley, Vassilis Christophides, and Gregorius Karvounarakis,
The Syntactic Web: Syntax and semantics on the Web, Markup
Languages: Theory & Practice 3 (2002), no. 4, 411–440.
XQuery is a query language designed to allow queries across the many kinds of information that are represented in XML. Although topic maps and RDF can also be represented in XML, many have held that their many possible syntactic forms make them extremely difficult to query using an XML query language, and that they can only be queried using special-purpose query languages with built-in knowledge of their semantics, including the ability to exploit RDF schema information. This talk shows that XQuery can, in fact, be used to solve the kinds of queries for which RDF and topic map query languages were designed, though with a loss of type safety.
The approach taken is to transform instances of RDF and topic maps to a syntactic representation that closely models their underlying logical models, and to use function libraries written in XQuery to directly support operations specific to RDF or topic maps. Schema level information is also incorporated in this representation, and is supported in the library, so queries can exploit type hierarchies and perform joins across predicates.
Information from other XML sources can also be queried together with information from topic maps and RDF. For instance, a query on a topic maps that searches for Shakespeare plays mentioned in Italian operas can also query the plays themselves — represented in XML — to determine which Italian cities are mentioned in them. Syntax is not the opposite of semantics, it is a medium for semantics.
[224]
Kerry Rodden and Kenneth R. Wood, How do people manage their digital
photographs?, Proceedings of the Conference on Human Factors in Computing
Systems, ACM Press, 2003, pp. 409–416.
[225]
Daniela Rus, Robert Gray, and David Kotz, Transportable information
agents, Journal of Intelligent Information Systems (1997), no. 9, 215–238.
[226]
Barry Schwartz, The tyranny of choice, Scientific American (2004).
Logic suggests that having options allows people to select precisely what makes them happiest. But, as studies show, abundant choice often makes for misery.
[227]
Richard B. Segal and Jeffrey O. Kephart, MailCat: An intelligent
assistant for organizing e-mail, Tech. report, IBM Thomas J. Watson
Research Center, Yorktown Heights, NY 10598, 1999.
[228]
Jeffrey S. Shell, Taking control of the panopticon: Privacy
considerations in the design of attentive user interfaces, Proceedings of
the CSCW 2002 Conference on Computer Supported Collaborative Work, ACM
Press, 2002.
[229]
Amit Sheth, Ontology-driven integration and analysis for semantic
applications in business intelligence and national security, 2003, invited
talk for Ontology and Semantic Web Technical Exchange Meeting, MITRE,
McLean, VA.
[230]
Amit Sheth, Budak Arpinar, and Vipul Kashyap, Relationships at the heart
of Semantic Web: Modeling, discovering, and exploiting complex semantic
relationships, Tech. report, LSDIS Lab, University of Georgia, Athens,
GA, 2002.
[231]
Amit Sheth and Cartic Ramakrishnan, Semantic (Web) technology in
action: Ontology driven information systems for search, integration and
analysis, IEEE Data Engineering Bulletin, Special issue on Making the
Semantic Web Real (2003), To appear.
[232]
Rahul Singh, Katia Sycara, and Terry R. Payne, Distributed AI,
schedules, and the Semantic Web, XML Journal 3 (2002), no. 11,
40–45.
[233]
Barry Smith and Christopher Welty, Ontology: Towards a new synthesis,
FOIS (2001).
[234]
Brian Starr, Mark S. Ackerman, and Michael Pazzani, Do I care? — tell
me what’s changed on the web, AAAI Spring Symposium (Stanford, CA,
USA), 1996.
We describe the Do-I-Care agent, which uses machine learning to detect “interesting” changes to Web pages previously found to be relevant. Because this agent focuses on changes to known pages rather than discovering new pages, we increase the likelihood that the information found will be interesting. The agent’s accuracy in finding interesting changes and in learning is improved by exploiting regularities in how pages are changed. Additionally, these agents can be used collaboratively by cascading them and by propagating interesting findings to other users’ agents.
[235]
Susan Stepney, Review and overview of “Building Large Knowledge-Based
Systems: Representation and Inference in the Cyc Project” by Douglas B.
Lenat and R. V. Guha, 1991.
[236]
Umberto Straccia, Distributed search in the Semantic Web, Proceedings
of the 2003 International Workshop on Description Logics (DL-2003 (Pisa,
Italy) (Diego Calvanese, Giuseppe De Giacomo, and Enrico Franconi, eds.),
CEUR Workshop Proceedings, ISTI-CNR, 2003.
Keywords: combining results,
[237]
Heiner Stuckenschmidt, Exploiting partially shared ontologies for
multi-agent communication, Cooperative Information Agents (M. Klusch,
ed.), Springer, 2002.
In has been argued that ontologies play a key role in multiagent communication because they provide and define a shared vocabulary to be used in the course of communication. In real-life scenarios, however, the situation where two agents completely share a vocabulary is rather an exception. More often, each agent uses its own vocabulary specified in a private ontology that is not known by other agents. In this paper we propose a solution to this problem for the situation, where agents share at least parts of their vocabulary. We argue that the assumption of a partially shared vocabulary is valid and sketch an approach for re-formulating terms from the private part of an agent’s ontology into a shared part thus enabling other agents to understand them. We further describe how the approach can be implemented using existing technology and proof the correctness of the re-formulation with respect to the semantics of the ontology-language DAML+OIL.
[238]
Heiner Stuckenschmidt, Frank van Harmelen, Anita de Waard, Anthony Scerri,
Ravinder Bhogal, Jan van Buel, Ian Crowlesmith, Christiaan Fluit, Arjohn
Kampman, Jeen Broekstra, and Erik van Mulligen, Exploring large
document repositories with RDF technology: The DOPE project, IEEE
Intelligent Systems 19 (2004), no. 3, 34–40.
This thesaurus-based search system uses automatic indexing, RDF-based querying, and concept-based visualization of results to support exploration of large online document repositories.
[239]
SWAD-Europe, Using RDFS or OWL as a schema language for validating
RDF, 2004.
[240]
Katia Sycara, Keith S. Decker, Anandeep Pannu, Mike Williamson, and Dajun Zeng,
Distributed intelligent agents, IEEE Expert 11 (1996),
no. 6, 36–46.
[241]
Roy Tennant (ed.), XML in libraries, Neal-Schuman Publishers, New
York, 2002.
[242]
Frank van Harmelen, How the Semantic Web will change KR: challenges
and opportunities for a new research agenda, The Knowledge Engineering
Review 17 (2002), no. 1.
The advent of the Semantic Web is rapidly making available a semantically much richer layer of machine accessible contents on top of the existing infrastructure. This will provide a much richer habitat for agents then the current WWW. However, many of the assumptions that underly current Knowledge Representation (KR) techniques as used in agent technology are no longer valid when KR techniques are deployed in a large scale and open environment such as the Semantic Web promises to be. In this short note we discuss some of the assumptions underlying current KR technology that will have to be revised when applied to the Semantic Web, and we discuss some of the research challenges and new opportunities that arise out of such a revision. We accompany each point with some references to relevant agent literature, without claiming these to be either exhaustive or the most authoritative references.
[243]
Maria Vargas-Vera, Enrico Motta, and John Domingue, AQUA: An
ontology-driven question answering system, 2003.
[244]
Kim H. Veltman, Towards a Semantic Web for culture, Journal of Digital
Information 4 (2004), no. 4.
Keywords: Culture, Sowa, John Sowa, Semiotics, Linguistics, Ontology, Systematics, Greek Principles, Knowledge Representation
[245]
David Vernet and Elisabet Golobardes, An unsupervised learning approach
for case-based classifier systems, Tech. report, Enginyeria i Arquitectura
La Salle, Universitat Ramon Llull, Pg. Bonanova 8, 08022 Barcelona,
Spain, 2002.
Keywords: mean sphere, mean k-means, unsupervised learning, clustering, training.
[246]
[http://www.crystaliz.com/logicware/mubot.html] “The term agent is used to represent two orthogonal concepts. The first is the agent’s ability for autonomous execution. The second is the agent’s ability to perform domain oriented reasoning.”
[247]
W3C/IETF URI Planning Interest Group, URIs, URLs, and URNs:
Clarifications and Recommendations 1.0, 2001.
[248]
Holger Wache and Heiner Stuckenschmidt, Practical context transformation
for information system interoperability, Proceedings of the Third
International and Interdisciplinary Conference on Modeling and Using Context
(CONTEXT2001) (Varol Akman, Paolo Bouquet, Richmond Thomason, and Roger A.
Young, eds.), Lecture Notes in Artificial Intelligence, no. 2116, Center
for Computing Technologies, University of Bremen, Springer, 2001,
pp. 367–381.
This paper discusses the use of contextual reasoning, i.e.context transformation for achieving semantic interoperability in het-erogeneousinformation systems. We introduce terminological contextsand their explication in terms of formal ontologies. Using a real-worldexample, we compare two practical approaches for context transforma-tionone based on transformation rule, the other of re-classification ofinformation entities in a different terminological context. We argue thatboth approaches supplement each other and develop a unifying theory ofcontext transformation. A sound and complete context transformationcalculus is presented that covers both transformation approaches.
[249]
Fons Wijnhoven, Edwin van den Belt, Eddy Verbruggen, and Paul van der Vet,
Internal data market services: An ontology-based architecture and its
evaluation, Informing Science 6 (2003), 259–271.
On information markets, many suppliers and buyers of information goods exchange values. Some of these goods are data, whose value is created in buyer interactions with data sources. These interactions are enabled by data market services (DMS). DMS give access to one or several data sources. The major problems with the creation of information value in these contexts are (1) the quality of information re-trievals and related queries, and (2) the complexity of matching information needs and supplies when different semantics are used by source systems and information buyers. This study reports about a prototype DMS (called CIRBA), which employs an ontology-based information retrieval system to solve semantic problems for a DMS. The DMS quality is tested in an experiment to assess its quality from a user perspective against a traditional data warehouse (with SQL) solution. The CIRBA solution gave substantially higher user satisfaction than the data warehouse alternative.
[250]
Andrew B. Williams and Zijian Ren, Agents teaching agents to share
meaning, Proceedings of the Fifth International Conference on Autonomous
Agents (Jörg P. Müller, Elisabeth Andre, Sandip Sen, and Claude
Frasson, eds.), Department of Electrical and Computer Engineering,
University of Iowa, ACM Press, 2001, pp. 465–472.
Keywords: information agents, knowledge acquisition and management, adaptation and learning
[251]
Mary S. Woodley, Gail Clement, and Pete Winn, Dublin Core Metadata
Initiative Glossary, 2003.
[252]
Michael J. Wooldridge and Nicholas R. Jennings, Intelligent agents:
Theory and practice, Knowledge Engineering Review 10 (1995), no. 2,
115–152.
The concept of an agent has become important in both Artificial Intelligence (AI) and mainstream computer science. Our aim in this paper is to point the reader at what we perceive to be the most important theoretical and practical issues associated with the design and construction of intelligent agents. For convenience, we divide these issues into three areas (though as the reader will see, the divisions are at times somewhat arbitrary). Agent theory is concerned with the question of what an…
[253]
XBRL International, eXtensible Business Reporting Language homepage,
2003.
[254]
Takashi Yukawa, Sen Yoshida, and Kazuhiro Kuwabara, A collaborative
personal repository system and its information retrieval scheme, IEICE
Transactions Inf. & Syst. (Special Issue on Text Processing for
Information Access) E86-D (2003), no. 9, 1788–1795.
[255]
Osmar R. Zaïane, From resource discovery to knowledge discovery on
the internet, 1998.
[256]
Marina Zhurakhinskaya, Belief layer for Haystack, Master’s thesis,
Department of Electrical Engineering and Computer Science, Massachusetts
Institute of Technology, Cambridge, MA, USA, 2002.
[257]
Floriano Zini and Leon Sterling, Designing ontologies for agents, 1999.
Keywords: ontologies, multi-agent systems
