面向知识丰富的路径计算
Towards Knowledge-Enriched Path Computation
摘要：
通常由导航系统提供的方向和路径通常是考虑绝对度量导出的，例如，在基础道路网络中查找最短路径。借助众包地理空间数据，我们的目标是获得路径，不仅最大限度地减少距离，而且还导致通过更受欢迎的地区使用用户产生的知识。我们从旅游博客中提取空间关系，如“附近”或“旁边”，定义成对的兴趣点（POI）之间的接近度，并使用概率模型量化这些关系。随后，我们创建一个关系图，其中每个节点对应于一个PoI，每个边描述相应PoI之间的空间连接。使用贝叶斯推理，我们得到一个概率测度的空间接近根据人群。将此措施应用于相应的道路网络，我们得到了一个改变的成本函数，它不完全依赖于距离，并丰富了一个实际的道路网络考虑众包空间关系。最后，我们提出了两个路由算法上丰富的道路网络。为了评估我们的方法，我们使用Flickr照片数据作为受欢迎程度的基本事实。我们的实验结果-基于真实世界的数据集-表明计算的路径w.r.t.\我们的替代成本函数在路径长度方面产生了有竞争力的解决方案，同时还提供了更“流行”的路径，使得路由选择更容易并且为用户提供更多信息。
英文摘要：
Directions and paths, as commonly provided by navigation systems, are usually derived considering absolute metrics, e.g., finding the shortest path within an underlying road network. With the aid of crowdsourced geospatial data we aim at obtaining paths that do not only minimize distance but also lead through more popular areas using knowledge generated by users. We extract spatial relations such as "nearby" or "next to" from travel blogs, that define closeness between pairs of points of interest (PoIs) and quantify each of these relations using a probabilistic model. Subsequently, we create a relationship graph where each node corresponds to a PoI and each edge describes the spatial connection between the respective PoIs. Using Bayesian inference we obtain a probabilistic measure of spatial closeness according to the crowd. Applying this measure to the corresponding road network, we obtain an altered cost function which does not exclusively rely on distance, and enriches an actual road networks taking crowdsourced spatial relations into account. Finally, we propose two routing algorithms on the enriched road networks. To evaluate our approach, we use Flickr photo data as a ground truth for popularity. Our experimental results -- based on real world datasets -- show that the paths computed w.r.t.\ our alternative cost function yield competitive solutions in terms of path length while also providing more "popular" paths, making routing easier and more informative for the user.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
