Collective Intelligence for Semantic and Knowledge Grid
J.UCS Special Issue
Jason J. Jung
(Yeungnam University, Korea
Ngoc Thanh Nguyen
(Wroclaw University of Technology, Poland
Recently, grid computing has been regarded as the most promising paradigm
to interconnect heterogeneous computing environments. Main goal of this
grid computing paradigm is to share local but limited resources with others
to solve very complex problems [Foster 2003]. Especially,
semantics and knowledge are playing an important role of building an efficient
grid platform to share information and knowledge with each other [de
Roure et al. 2005]. A variety of domains, e.g., business [Zhen
and Jiang 2008, Jung 2008], chemistry [Taylor
et al. 2006], information retrieval [Jung 2007],
and biomedical areas [Tsiknakis et al. 2008], have
been attempting to employ this semantic grid platform.
However, there are several hurdles that they have to overcome in common,
e.g., semantic heterogeneity (e.g., inconsistency and conflict) between
information sources on a grid. In order to efficiently deal with the hurdles
and implement the semantic grid platform, there have been representative
approaches, e.g., web services (S-OGSA [Corcho et al.
2006]), metadata, ontologies and reasoning.
More particularly, collective intelligence is the latest buzzword to
take into account how to find any opportunities to link individual intelligence
as well as how to apply the collective intelligence to various problems.
In this issue, we are focusing on the semantic and knowledge grid platforms
(as well as distributed platforms) for building and exploiting collective
intelligence. Main topics of interests are noted, as follows;
Ontology models and ontology engineering
Ontology mapping (alignment, matching, and merging)
Consensus theory (conflict resolution and negotiation)
Knowledge representation and discovery
Social network analysis (user clustering and community identification)
Hence, the aim of this special issue is to bring together researchers and
practitioners in areas of knowledge and intelligence, semantics, and grid
computing to share their visions, research achievements and solutions to
real applications, to resolve the challenge issues and to establish worldwide
cooperative research and development.
Knowledge management systems
Data mining applications
Semantic Web service applications
Other applications (e.g., e-commerce and e-learning)
This issue is composed of two parts, as selected from the studies presented
in two international events. For the first part of this issue, we selected
two papers from The First International Workshop on Collective Intelligence
on Semantic and Knowledge Grid (CISKGrid 2007), which were held in Beijing,
The first paper, entitled "Ranking
Retrieval Systems with Partial Relevance Judgements" (Shengli Wu and
Fabio Crestani), claims that in distributed information retrieval architecture
partial relevance judgements can be integrated. They discuss how to rank
retrieval systems in the condition of partial relevance judgments, which
is common in major retrieval evaluation events such as TREC conferences
and NTCIR workshops.
The second paper "Query
Transformation Based on Semantic Centrality in Semantic Social Network"
(Jason J. Jung) is focusing on semantic social network platform. He proposes
a new measurement of semantic centrality, i.e., the power of semantic bridging
on semantic peer-to-peer environment. Thereby, semantically cohesive user
subgroups are built so that semantic affinities between peers can be computed.
As second part of this issue, we have selected seven papers from International
Symposium on Agent and Multi-agent System: Technology and Applications
(AMSTA 2007), which were successfully held in Wroclaw, Poland.
The third paper in this issue is "Schema
Mappings and Agents Actions in P2P Data Integration System" (Grazyna
Brzykcy, Jerzy Bartoszek, and Tadeusz Pankowski). It proposes a novel mapping
method between distributed schema to support automatic communication between
The fourth paper is entitled "An
Improved Multi-Agent Simulation Methodology for Modelling and Evaluating
Wireless Communication Systems Resource Allocation Algorithms" (Panagiotis
Minas Papazoglou, Dimitrios Alexios Karras, and Rallis Constantine Papademetriou).
This work investigates how to model more abstract entities involved in
WCS operation, and especially the concurrent network procedures (services)
for conducting efficient resource allocation in wireless communication
The fifth paper, entitled "An
Agent-Based Solution for Dynamic Supply Chain Management" (Vedran Podobnik,
Ana Petric, and Gordan Jezic), is attacking a business-oriented application
(i.e., supply chain management). It has proposed a mutli-agent coordination
framework, called CrocodileAgent, for efficiently managing dynamic supply
The sixth paper, "A
Knowledge Discovery Agent for a Topology Bit-map in Ad Hoc Mobile Networks"
(SungSoo Lee, HangKon Kim, and ChongGun Kim), proposes a knowledge discovery
agent for an effective routing method that uses simple bit-map topology
information with Ad-hoc On Demand Distance Vector (AODV) protocol. The
agents can collect topology information and aggregate it as a bit-map to
figure out all available paths from a source to a destination.
The seventh paper is "Formalizing
Agent-Based English Auctions Using Finite State Process Algebra" (Amelia
B?dic? and Costin B?dic?). It introduces a formal framework based on finite
state process algebra to take into account modeling and analysis of interaction
protocols during agent-based negotiations.
The eighth paper, entitled "Reinforcement
Learning on a Futures Market Simulator" (Koichi Moriyama, Mitsuhiro
Matsumoto, Ken-ichi Fukui, Satoshi Kurihara, and Masayuki Numao), presents
the futures market simulator U-Mart (Unreal Market as an Artificial Research
Testbed) to construct various reinforcement learner models and compare
The ninth paper, entitled "Structural
Performance Evaluation of Multi-Agent Systems" (Dariusz Król
and Micha? Zelmozer), proposes the new metrics on distributed object systems
to evaluate a variety of types of distributed systems.
As a final remark, this set of papers provides a perspective on issues
and experiences in not only collective intelligence on semantic grid platforms
but also cooperation and collaboration on multi-agent systems. We are much
indebted to the referees for their excellent work in suggesting ways to
improve the previous versions of these research contributions. Especially,
we thank Prof. GeunSik Jo, Prof. ChulMo Koo, and Dr. YoungShin Han for
their great efforts to review and edit this special issue.
We also want to thank Dr. Hermann Maurer, Editor-in-Chief of the journal,
for accepting our proposal for this special issue and Mag. Dana Kaiser,
assistant editor of the journal, for kindly helping us with the details
of this issue.
Jason J. Jung
Ngoc Thanh Nguyen
[Corcho et al. 2006] Corcho, O., Alper, P., Kotsiopoulos,
I., Missier, P., Bechhofer, S.,and Goble, C.: An overview of s-ogsa: A
reference semantic grid architecture. Journal of Web Semantics, 4(2):102-115,
[de Roure et al. 2005] de Roure, D.,
Jennings, N.R., and Shadbolt, N.R.: The Semantic Grid: Past, Present,
and Future. Proceedings of the IEEE, 93(3):669-681, 2005.
[Foster 2003] Foster, I.: The Grid: Computing
without bounds. Scientific American, 288(4):78-85, 2003.
[Jung 2008] Jung, J.J.: Taxonomy alignment for
interoperability between heterogeneous virtual
organizations. Expert Systems with Applications, 36(2):to
[Jung 2007] Jung, J.J.: Ontological framework
based on contextual mediation for collaborative information
retrieval. Information Retrieval, 10(1):85-109, 2007.
[Taylor et al. 2006] Taylor, K.R., Essex, J.W.,
Frey, J.G., Mills, H.R., Hughes, G., and Zaluska, E.J.: The semantic
grid and chemistry: Experiences with combechem. Journal of Web
Semantics, 4(2):84-101, 2006.
[Tsiknakis et al. 2008] Tsiknakis, M.,
Brochhausen, M., Nabrzyski, J., Pucacki, J., Sfakianakis, S.G.,
Potamias, G., Desmedt, C., and Kafetzopoulos, D.: A semantic grid
infrastructure enabling integrated access and analysis of multilevel
biomedical data in support of postgenomic clinical trials on
cancer. IEEE Transactions on Information Technology in
Biomedicine, 12(2):205-217, 2008.
[Zhen and Jiang 2008] Zhen L. and Jiang, Z.-H.:
Innovation-oriented knowledge query in knowledge grid. Journal of Information
Science and Engineering, 24(2):601-613, 2008.