We propose an adaptive rendering approach for large-scale skyline characterization and matching with applications to automated geo-tagging of photos and images. Given an image, our system automatically extracts the skyline and then matches it to a database of reference skylines extracted from rendered images using digital elevation data (DEM). The sampling density of these rendering locations d...

In this paper we present the ontology matching system LogMap 2, a much improved version of its predecessor LogMap. LogMap 2 supports user interaction during the matching process, which is essential for use cases requiring very accurate mappings. Interactivity, however, imposes very strict scalability requirements; we are able to satisfy these requirements by providing real-time user response ev...

Biomedical systems have been using ontology matching as a primary technique for heterogeneity resolution. However, the natural intricacy and vastness of biomedical data have compelled biomedical ontologies to become large-scale and complex; consequently, biomedical ontology matching has become a computationally intensive task. Our parallel heterogeneity resolution system, i.e., SPHeRe, is built...

MIDI files, when paired with corresponding audio recordings, can be used as ground truth for many music information retrieval tasks. We present a system which can efficiently match and align MIDI files to entries in a large corpus of audio content based solely on content, i.e., without using any metadata. The core of our approach is a convolutional network-based cross-modality hashing scheme wh...

Supervised training procedures for semantic parsers produce high-quality semantic parsers, but they have difficulty scaling to large databases because of the sheer number of logical constants for which they must see labeled training data. We present a technique for developing semantic parsers for large databases based on a reduction to standard supervised training algorithms, schema matching, a...

Many existing ontology matching tools are not well scalable. In this paper, we present the Malasco system, which serves as a framework for reusing existing, non-scalable matching systems on large-scale ontologies. The results achieved with different combinations of partitioning and matching tools are discussed, and optimization techniques are examined. It is shown that the loss of result qualit...

As a mechanism to efficiently support group communications, multicasting, faces a serious state scalability problem when there are large numbers of groups in the network. Recently, a novel solution called Aggregated Multicast has been proposed, in which multiple groups can share one delivery tree. A key problem in Aggregated Multicast is group-to-tree matching (i.e., assigning groups to proper ...

Heterogeneous multimedia data are widely encountered in many applications, such as photo-sketch face recognition, still image to video face recognition, cross-modality image synthesis, cross media retrieval, etc. With the ubiquitous use of digital imaging devices, mobile terminals and social networks, there are lots of heterogeneous and homogeneous data from multiple sources, e.g., news media w...

Instance Matching aims to discover the linkage between different descriptions of real objects across heterogeneous data sources. With the rapid development of Semantic Web, especially of the linked data, automatically instance matching has been become the fundamental issue for ontological data sharing and integration. Instances in the ontologies are often in large scale, which contains millions...

A profile matching algorithm takes as input a user profile of one social network and returns, if existing, the profile of the same person in another social network. Such methods have immediate applications in Internet marketing, search, security, and a number of other domains, which is why this topic saw a recent surge in popularity. In this paper, we present a user identity resolution approach...