Name
Abstract | ||
|---|---|---|
Dialogue inferencing is the knowledge-intensive process of inferring aspects of a user's problem from its partial description.
Conversational case-based reasoning (CCBR) systems, which interactively and incrementally elicit a user's problem description,
suffer from poor retrieval efficiency (i.e., they prompt the user with questions that the user has already implicitly answered)
unless they perform dialogue inferencing. The standard method for dialogue inferencing in CCBR systems requires library designers
to supply explicit inferencing rules. This approach is problematic (e.g., maintenance is difficult). We introduce an alternative
approach in which the CCBR system guides the library designer in building a domain model. This model and the partial problem
description are then given to a query retrieval system (PARKA-DB) to infer any implied answers during a conversation. In an
initial empirical evaluation in the NaCoDAE CCBR tool, our approach improved retrieval efficiency without sacrificing retrieval
precision.
|
Year | DOI | Venue |
|---|---|---|
1998 | 10.1007/BFb0056339 | EWCBR |
Keywords | Field | DocType |
conversational case-based reasoning,dialogue inferencing,domain model | Information system,Conversation,Information retrieval,Computer science,Decision support system,Artificial intelligence,Problem description,Case-based reasoning,Domain model,Distributed computing | Conference |
Volume | ISSN | ISBN |
1488 | 0302-9743 | 3-540-64990-5 |
Citations | PageRank | References |
41 | 3.08 | 10 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| David W. Aha | 1 | 4103 | 620.93 |
| Tucker Maney | 2 | 43 | 3.46 |
| Len Breslow | 3 | 247 | 24.59 |