Name
Abstract | ||
|---|---|---|
Product developers frequently discuss topics related to their development project with others, but often use technical terms whose meanings are not clear to non-specialists. To provide non-experts with precise and comprehensive understanding of the know-who/know-how being discussed, the method proposed herein categorizes the messages using a taxonomy of the products being developed and a taxonomy of tasks relevant to those products. The instances in the taxonomy are products and/or tasks manually selected as relevant to system development. The concepts are defined by the taxonomy of instances. That proposed method first extracts phrases from discussion logs as data-driven instances relevant to system development. It then classifies those phrases to the concepts defined by taxonomy experts. The innovative feature of our method is that in classifying a phrase to a concept, say C, the method considers the associations of the phrase with not only the instances of C, but also with the instances of the neighbor concepts of C (neighbor is defined by the taxonomy). This approach is quite accurate in classifying phrases to concepts; the phrase is classified to C. not the neighbors of C, even though they are quite similar to C. Next, we attach a data-driven concept to C; the data-driven concept includes instances in C and a classified phrase as a data-driven instance. We analyze know-who and know-how by using not only human-defined concepts but also those data-driven concepts. We evaluate our method using the mailing-list of an actual project. It could classify phrases with twice the accuracy possible with the TF/iDF method, which does not consider the neighboring concepts. The taxonomy with data-driven concepts provides more detailed know-who/know-how than can be obtained from just the human-defined concepts themselves or from the data-driven concepts as determined by the TF/iDF method. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1587/transinf.E93.D.2717 | IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS |
Keywords | Field | DocType |
taxonomy, knowledge management, know-who/know-how | Know-how,Computer science,Phrase,Artificial intelligence,Natural language processing,System development | Journal |
Volume | Issue | ISSN |
E93D | 10 | 1745-1361 |
Citations | PageRank | References |
0 | 0.34 | 19 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Makoto Nakatsuji | 1 | 225 | 13.16 |
| Akimichi Tanaka | 2 | 47 | 4.90 |
| Takahiro Madokoro | 3 | 1 | 0.69 |
| Kenichiro Okamoto | 4 | 1 | 0.69 |
| Sumio Miyazaki | 5 | 5 | 1.69 |
| Tadasu Uchiyama | 6 | 42 | 5.11 |