Trajectory prediction estimates an entity's future path from its historical movements, enabling applications in autonomous navigation, robotics, and mobility analytics. This highlight paper presents TrajLearn, a deep generative framework that models higher-order mobility flows using a hexagonal spatial representation. TrajLearn combines a Transformer-based architecture with a constrained beam search to explore multiple plausible paths while preserving spatial continuity. Experiments on real-world datasets show up to 40% accuracy improvement over state-of-the-art models. We also propose a hierarchical mixed-resolution mapping algorithm that adaptively applies finer granularity to high-activity regions while using coarser resolution elsewhere, optimizing storage and computation. TrajLearn provides a scalable and reproducible foundation for accurate trajectory prediction in dynamic spatial environments.

Tabular data is widespread on the web and in enterprise data lakes. Recently, there has been increasing interest in developing algorithms for matching tabular data with knowledge graphs. This involves learning correspondences between tabular entities such as cells, rows, and columns and entities in the knowledge graph. Such semantic annotation of tabular entities has numerous applications such as entity disambiguation, knowledge graph expansion, error detection and repair in tabular data, and more. A key first step for all these applications is the lookup function that matches a query string to a candidate set of knowledge graph entities. Despite the importance of entity lookup, current implementations are not optimized, not robust to misspellings, and ignore semantic relationships. To address these problems, we represent each entity as an embedding - a compact vector representation that is cognizant of syntactic and semantic similarities and supports fast lookup. We propose, EMBLOOKUP, a novel and efficient approach for learning such an embedding. EMBLOOKUP is based on deep metric learning with triplet loss and supports accurate and efficient lookup of knowledge graph entities. We conduct extensive experiments that demonstrate that EMBLOOKUP achieves 1–2 orders of magnitude speedup while being tolerant to many types of errors in the query and data. We demonstrate the generality of EMBLOOKUP over diverse application scenarios in semantic table annotation, entity disambiguation, and data repair.

Abstract Knowledge graphs represented as RDF datasets are integral to many machine learning applications. RDF is supported by a rich ecosystem of data management systems and tools, most notably RDF database systems that provide a SPARQL query interface. Surprisingly, machine learning tools for knowledge graphs do not use SPARQL, despite the obvious advantages of using a database system. This is due to the mismatch between SPARQL and machine learning tools in terms of data model and programming style. Machine learning tools work on data in tabular format and process it using an imperative programming style, while SPARQL is declarative and has as its basic operation matching graph patterns to RDF triples. We posit that a good interface to knowledge graphs from a machine learning software stack should use an imperative, navigational programming paradigm based on graph traversal rather than the SPARQL query paradigm based on graph patterns. In this paper, we present RDFFrames, a framework that provides such an interface. RDFFrames provides an imperative Python API that gets internally translated to SPARQL, and it is integrated with the PyData machine learning software stack. RDFFrames enables the user to make a sequence of Python calls to define the data to be extracted from a knowledge graph stored in an RDF database system, and it translates these calls into a compact SPQARL query, executes it on the database system, and returns the results in a standard tabular format. Thus, RDFFrames is a useful tool for data preparation that combines the usability of PyData with the flexibility and performance of RDF database systems.

Knowledge graphs represented in RDF are becoming increasingly popular and are essential to many machine learning applications. A rich ecosystem of RDF data management systems and tools has evolved over the years, most notably RDF database management systems that support the SPARQL query language. Surprisingly, machine learning tools for knowledge graphs typically do not use SPARQL despite the obvious advantages of using a database system. This is due to the mismatch between SPARQL and machine learning tools in terms of expected data model and interface style. Machine learning tools work on data in tabular format and process it using imperative relational API calls, while SPARQL matches graph patterns to RDF triples. To access knowledge graphs for machine learning, we observe that it is more natural to use a navigational paradigm based on graph traversal rather than the SPARQL paradigm based on triple patterns. We demonstrate RDFFrames, a framework that bridges the gap between machine learning tools and RDF database systems by offering the usability and flexibility of machine learning tools together with the performance of a database system. RDFFrames enables the user to make a sequence of Python calls to define the data to be extracted from a knowledge graph stored in an RDF database system, and it translates these calls into a compact SPARQL query, executes it on the database system, and returns the results in a standard tabular format.

Graph data mining is defined as searching in an input graph for all subgraphs that satisfy some property that makes them interesting to the user. Examples of graph data mining problems include frequent subgraph mining, counting motifs, and enumerating cliques. These problems differ from other graph processing problems such as PageRank or shortest path in that graph data mining requires searching through an exponential number of subgraphs. Most current parallel graph analytics systems do not provide good support for graph data mining. One notable exception is Arabesque, a system that was built specifically to support graph data mining. Arabesque provides a simple programming model to express graph data mining computations, and a highly scalable and efficient implementation of this model, scaling to billions of subgraphs on hundreds of cores. This demonstration will showcase the Arabesque system, focusing on the end-user experience and showing how Arabesque can be used to simply and efficiently solve practical graph data mining problems that would be difficult with other systems.