Name
Abstract | ||
|---|---|---|
As knowledge increases tremendously each and every day, there is a need for means to manage and organize it, so as to utilize it when needed. For example, for finding solutions to technical/engineering problems. An alternative for achieving this goal is through knowledge mapping that aims at indexing the knowledge. Nevertheless, searching for knowledge in such maps is still a challenge. In this paper, we propose an algorithm for knowledge searching over maps created by ME-MAP, a mapping approach we developed. The algorithm is a greedy one that aims at maximizing the similarity between a query and existing knowledge encapsulated in ME-maps. We evaluate the efficiency of the algorithm in comparison to an expert judgment. The evaluation indicates that the algorithm achieved high performance within a bounded time. Though additional examination is required, the sought algorithm can be easily adapted to other modeling languages for searching models. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/978-3-030-89022-3_20 | CONCEPTUAL MODELING, ER 2021 |
Keywords | DocType | Volume |
Conceptual modeling, Matching, Searching | Conference | 13011 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Maxim Bragilovski | 1 | 0 | 0.34 |
| Yifat Makias | 2 | 0 | 0.34 |
| Moran Shamshila | 3 | 0 | 0.34 |
| Roni Stern | 4 | 335 | 49.62 |
| Arnon Sturm | 5 | 410 | 44.76 |