Emergent semantics in knowledge sifter

An evolutionary search agent based on Semantic Web services

Larry Kerschberg, Hanjo Jeong, Wooju Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

This paper addresses the various facets of emergent semantics in content retrieval systems such as Knowledge Sifter, an architecture and system based on the use of specialized agents to coordinate the search for knowledge in heterogeneous sources, including the Web, semi-structured data, relational data and the Semantic Web. The goal is to provide just-in-time knowledge to users based on their decision-making needs. There are three important factors that can assist in focusing the search: 1) the user's profile, consisting user preferences, biases, and query history, 2) the user's context to focus on the current activity, and 3) the user's information space, in which he may receive the information on specialized hardware with limited bandwidth, implying mat the knowledge must be filtered and tailored to die presentation medium. Emergent semantics in me context of Knowledge Sifter allow for evolutionary adaptive behavior. We present a meta-model that captures the agent operation and interactions, as well as the artifacts that are created and consumed during system operation. These are stored in a repository, and a collection of emergence agents are presented that perform emergence functions such as: data mining for patterns; concept discovery and evolution; user preferences tracking; collaborative filtering of user profiles; results ranking; and data source reputation and trust.

Original languageEnglish
Title of host publicationJournal on Data Semantics VI - Special Issue on Emergent Semantics
Pages187-209
Number of pages23
Publication statusPublished - 2006 Dec 1

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4090 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Fingerprint

Semantic Web Services
Semantic Web
Web services
Semantics
User Profile
User Preferences
Collaborative filtering
Data mining
Semistructured Data
Adaptive Behavior
Decision making
Collaborative Filtering
Hardware
Bandwidth
Metamodel
Facet
Repository
Ranking
Data Mining
Retrieval

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Kerschberg, L., Jeong, H., & Kim, W. (2006). Emergent semantics in knowledge sifter: An evolutionary search agent based on Semantic Web services. In Journal on Data Semantics VI - Special Issue on Emergent Semantics (pp. 187-209). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4090 LNCS).
Kerschberg, Larry ; Jeong, Hanjo ; Kim, Wooju. / Emergent semantics in knowledge sifter : An evolutionary search agent based on Semantic Web services. Journal on Data Semantics VI - Special Issue on Emergent Semantics. 2006. pp. 187-209 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{1e7b00def8c2451dbe5fbf2ad281b2ad,
title = "Emergent semantics in knowledge sifter: An evolutionary search agent based on Semantic Web services",
abstract = "This paper addresses the various facets of emergent semantics in content retrieval systems such as Knowledge Sifter, an architecture and system based on the use of specialized agents to coordinate the search for knowledge in heterogeneous sources, including the Web, semi-structured data, relational data and the Semantic Web. The goal is to provide just-in-time knowledge to users based on their decision-making needs. There are three important factors that can assist in focusing the search: 1) the user's profile, consisting user preferences, biases, and query history, 2) the user's context to focus on the current activity, and 3) the user's information space, in which he may receive the information on specialized hardware with limited bandwidth, implying mat the knowledge must be filtered and tailored to die presentation medium. Emergent semantics in me context of Knowledge Sifter allow for evolutionary adaptive behavior. We present a meta-model that captures the agent operation and interactions, as well as the artifacts that are created and consumed during system operation. These are stored in a repository, and a collection of emergence agents are presented that perform emergence functions such as: data mining for patterns; concept discovery and evolution; user preferences tracking; collaborative filtering of user profiles; results ranking; and data source reputation and trust.",
author = "Larry Kerschberg and Hanjo Jeong and Wooju Kim",
year = "2006",
month = "12",
day = "1",
language = "English",
isbn = "3540367128",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "187--209",
booktitle = "Journal on Data Semantics VI - Special Issue on Emergent Semantics",

}

Kerschberg, L, Jeong, H & Kim, W 2006, Emergent semantics in knowledge sifter: An evolutionary search agent based on Semantic Web services. in Journal on Data Semantics VI - Special Issue on Emergent Semantics. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4090 LNCS, pp. 187-209.

Emergent semantics in knowledge sifter : An evolutionary search agent based on Semantic Web services. / Kerschberg, Larry; Jeong, Hanjo; Kim, Wooju.

Journal on Data Semantics VI - Special Issue on Emergent Semantics. 2006. p. 187-209 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4090 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Emergent semantics in knowledge sifter

T2 - An evolutionary search agent based on Semantic Web services

AU - Kerschberg, Larry

AU - Jeong, Hanjo

AU - Kim, Wooju

PY - 2006/12/1

Y1 - 2006/12/1

N2 - This paper addresses the various facets of emergent semantics in content retrieval systems such as Knowledge Sifter, an architecture and system based on the use of specialized agents to coordinate the search for knowledge in heterogeneous sources, including the Web, semi-structured data, relational data and the Semantic Web. The goal is to provide just-in-time knowledge to users based on their decision-making needs. There are three important factors that can assist in focusing the search: 1) the user's profile, consisting user preferences, biases, and query history, 2) the user's context to focus on the current activity, and 3) the user's information space, in which he may receive the information on specialized hardware with limited bandwidth, implying mat the knowledge must be filtered and tailored to die presentation medium. Emergent semantics in me context of Knowledge Sifter allow for evolutionary adaptive behavior. We present a meta-model that captures the agent operation and interactions, as well as the artifacts that are created and consumed during system operation. These are stored in a repository, and a collection of emergence agents are presented that perform emergence functions such as: data mining for patterns; concept discovery and evolution; user preferences tracking; collaborative filtering of user profiles; results ranking; and data source reputation and trust.

AB - This paper addresses the various facets of emergent semantics in content retrieval systems such as Knowledge Sifter, an architecture and system based on the use of specialized agents to coordinate the search for knowledge in heterogeneous sources, including the Web, semi-structured data, relational data and the Semantic Web. The goal is to provide just-in-time knowledge to users based on their decision-making needs. There are three important factors that can assist in focusing the search: 1) the user's profile, consisting user preferences, biases, and query history, 2) the user's context to focus on the current activity, and 3) the user's information space, in which he may receive the information on specialized hardware with limited bandwidth, implying mat the knowledge must be filtered and tailored to die presentation medium. Emergent semantics in me context of Knowledge Sifter allow for evolutionary adaptive behavior. We present a meta-model that captures the agent operation and interactions, as well as the artifacts that are created and consumed during system operation. These are stored in a repository, and a collection of emergence agents are presented that perform emergence functions such as: data mining for patterns; concept discovery and evolution; user preferences tracking; collaborative filtering of user profiles; results ranking; and data source reputation and trust.

UR - http://www.scopus.com/inward/record.url?scp=38049041701&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=38049041701&partnerID=8YFLogxK

M3 - Conference contribution

SN - 3540367128

SN - 9783540367123

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 187

EP - 209

BT - Journal on Data Semantics VI - Special Issue on Emergent Semantics

ER -

Kerschberg L, Jeong H, Kim W. Emergent semantics in knowledge sifter: An evolutionary search agent based on Semantic Web services. In Journal on Data Semantics VI - Special Issue on Emergent Semantics. 2006. p. 187-209. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).