Name
Abstract | ||
|---|---|---|
The traditional, expert-based process of knowledge acquisition is known to be both slow and costly. With the advent of theWeb 2.0, community-based approaches have appeared. These promise a similar or even higher level of information quantity by using the collaborative work of voluntary contributors. Yet, the community-driven approach yields new problems on its own, most prominently contributor motivation and data quality. Our former work [1] has shown, that the issue of contributor motivation can be solved by embedding the data collection activity into a gaming scenario. Additionally, good games are designed to be replayable and thus well suited to generate redundant datasets. In this paper we propose semantic view area clustering as a novel approach to aggregate semantically tagged objects to achieve a higher overall data quality. We also introduce the concept of semantic barriers as a method to account for interaction betwen spatial and semantic data. We also successfully evaluate our algorithm against a traditional clustering method. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-04617-9_88 | KI |
Keywords | Field | DocType |
community-driven approach yield,community-based approach,data collection activity,geospatial data collection,location-based game,semantic barrier,collaborative work,semantic data,data quality,semantic view area,contributor motivation,higher overall data quality,data collection,geospatial data | Geospatial analysis,Data collection,World Wide Web,Embedding,Data quality,Information retrieval,Computer science,Cluster analysis,Knowledge acquisition,Semantic data model | Conference |
Volume | ISSN | ISBN |
5803 | 0302-9743 | 3-642-04616-9 |
Citations | PageRank | References |
1 | 0.39 | 11 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sebastian Matyas | 1 | 93 | 8.52 |
| Peter Wullinger | 2 | 1 | 0.73 |
| Christian Matyas | 3 | 51 | 3.10 |