In this work we describe an entity resolution project performed at Yad Vashem, the central repository of Holocaust-era information. The Yad Vashem dataset is unique with respect to classic entity resolution, by virtue of being both massively multi-source and by requiring multi-level entity resolution. With today's abundance of information sources, this project sets an example for multi-source resolution on a big-data scale. We discuss a set of requirements that led us to choose the MFIBlocks entity resolution algorithm in achieving the goals of the application. We also provide a machine learning approach, based upon decision trees to transform soft clusters into ranked clustering of records, representing possible entities. An extensive empirical evaluation demonstrates the unique properties of this dataset, highlighting the shortcomings of current methods and proposing avenues for future research in this realm.

With the advent of the Internet, it is desirable to interpret and extract useful information from the Web. One major challenge in Web interface interpretation is to discover the semantic structure underlying a Web interface. Many heuristic approaches have been developed to discover and group semantically related interface objects. However, those approaches cannot solve the problem of nonuniformity satisfactorily and are not able to tag the semantic role of each object. Distinct from existing approaches, this paper develops a robust and formal approach to recovering interface semantics using graph grammars. Because of the distinct capability of spatial specifications in the abstract syntax, the spatial graph grammar (SGG) is selected to perform the semantic grouping and interpretation of segmented screen objects. Instead of analyzing HTML source codes, we apply an efficient image-processing technology to recognize atomic interface objects from the screenshot of an interface and produce a spatial graph, which records significant spatial relations among recognized objects. A spatial graph is more concise than its corresponding document object model structure and, thus, facilitates interface analysis and interpretation. Based on the spatial graph, the SGG parser recovers the hierarchical relations among interface objects.

A configuration management database (CMDB) can be considered to be a large graph representing the IT infrastructure entities and their inter-relationships. Mining such graphs is challenging because they are large, complex, and multi-attributed, and have many repeated labels. These characteristics pose challenges for graph mining algorithms, due to the increased cost of sub graph isomorphism (for support counting), and graph isomorphism (for eliminating duplicate patterns). The notion of pattern frequency or support is also more challenging in a single graph, since it has to be defined in terms of the number of its (potentially, exponentially many) embeddings. We present CMDB-Miner, a novel two-step method for mining infrastructure patterns from CMDB graphs. It first samples the set of maximal frequent patterns, and then clusters them to extract the representative infrastructure patterns. We demonstrate the effectiveness of CMDB-Miner on real-world CMDB graphs.

Human observers understand the content of an image intuitively. Based upon image content, they perform many image-related tasks, such as creating slide shows and photo albums, and organizing their image archives. For example, to select photos for an album, people assess image quality based upon the main objects in the image. They modify colors in an image based upon the color of important objects, such as sky, grass or skin. Serious photographers might modify each object separately. Photo applications, in contrast, use low-level descriptors to guide similar tasks. Typical descriptors, such as color histograms, noise level, JPEG artifacts and overall sharpness, can guide an imaging application and safeguard against blunders. However, there is a gap between the outcome of such operations and the same task performed by a person. We believe that the gap can be bridged by automatically understanding the content of the image. This paper presents algorithms for automatic tagging of perceptual objects in images, including sky, skin, and foliage, which constitutes an important step toward this goal.