Before revision:  In addition, most of embedding-based EA methods either fuse different features on representation-level and generate unified entity embedding for alignment, which potentially causes information loss, or aggregate features on outcome-level with hand-tuned weights, which is not practical with increasing numbers of features. To tackle these deficiencies , we propose a collective embedding-based EA frameworkwith adaptive feature fusion mechanism .
After revision:  To fill this gap , we propose a collective embedding-based EA frameworkwith adaptive feature fusion mechanism .