This paper presents an automatic method for understanding and interpreting the semantics of unannotated web images. We observe that the relations between objects in an image carry important semantics about the image. To capture and describe such semantics, we propose Object Relation Network (ORN), a graph model representing the most probable meaning of the objects and their relations in an image. Guided and constrained by an ontology, ORN transfers the rich semantics in the ontology to image objects and the relations between them, while maintaining semantic consistency (e.g., a soccer player can kick a soccer ball, but cannot ride it). We present an automatic system which takes a raw image as input and creates an ORN based on image visual appearance and the guide ontology. We demonstrate various useful web applications enabled by ORNs, such as automatic image tagging, automatic image description generation, and image search by image.

This paper presents a novel ranking method for complex semantic relationship (semantic association) search based on user preferences. The authors’ method employs a learning-to-rank algorithm to capture each user’s preferences. Using this, it automatically constructs a personalized ranking function for the user. The ranking function is then used to sort the results of each subsequent query by the user. Query results that more closely match the user’s preferences gain higher ranks. Their method is evaluated using a real-world RDF knowledge base created from Freebase linked-open-data. The experimental results show that the authors’ method significantly improves the ranking quality in terms of capturing user preferences, compared with the state-of-the-art.

Data management and analysis has become an integral component in the area of reservoir engineering. An important metric that determines the overall effectiveness of data analysis is data quality. Data provenance, the metadata that pertains to the derivation history of data objects, has emerged as an invaluable asset in evaluating data quality. The reservoir facilities and software systems that collect provenance information are often distributed, thus making it difficult to analyze provenance data. Our primary contribution in this paper is an approach for provenance information integration in reservoir engineering.