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 language | English |
|---|---|
| Title of host publication | Journal on Data Semantics VI - Special Issue on Emergent Semantics |
| Pages | 187-209 |
| Number of pages | 23 |
| Publication status | Published - 2006 Dec 1 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 4090 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
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All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)
Cite this
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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 proceeding › Conference 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 -