How data is encoded in a knowledge graph directly influences what can be done with that data. A common problem with many encodings is that they have difficulty representing ambiguity and evolution inherent in many real-world data sets. The data encoding represented in this paper (called CARAT) is a graph-level description of our attempt to capture data that is contradictory, ambiguous and evolves over time (including deleting information). The data encoding relies on tracking assertions about subjects rather than directly tracking states. This encoding decision resolves many issues our team had experienced using other data encodings but produces a a larger graph. This is a preliminary presentation of our CARAT data encoding and our experience using it.

Curating knowledge from multiple siloed sources that contain both structured and unstructured data is a major challenge in many real-world applications. Pattern matching and querying represent fundamental tasks in modern data analytics that leverage this curated knowledge. The development of such applications necessitates overcoming several research challenges, including data extraction, named entity recognition, data modeling, and designing query interfaces. Moreover, the explainability of these functionalities is critical for their broader adoption.The emergence of Large Language Models (LLMs) has accelerated the development lifecycle of new capabilities. Nonetheless, there is an ongoing need for domain-specific tools tailored to user activities. The creation of such digital assistants has gained considerable traction in recent years, with LLMs offering a promising avenue to develop such assistants utilizing domain-specific knowledge and assumptions.In this context, we introduce an advanced query and reasoning system, GraphAide, which constructs a knowledge graph (KG) from diverse sources and allows to query and reason over the resulting KG. GraphAide harnesses both the KG and LLMs to rapidly develop domain-specific digital assistants. It integrates design patterns from retrieval augmented generation (RAG) and the semantic web to create an agentic LLM application. GraphAide underscores the potential for streamlined and efficient development of specialized digital assistants, thereby enhancing their applicability across various domains.

In the real world, data does not come neatly packaged. Instead, it typically comes as small updates from many sources with different conventions. Building a single, cohesive knowledge-base to work from requires merging small updates from many different sources. This paper outlines methods we have investigated for scoring merging routines. Given a challenge problem consisting of a large knowledge-base and a set of smaller documents, algorithms are asked to identify alignment points between the smaller document and the knowledge base. This paper surveys options for evaluating such algorithms, providing notes on strengths, weaknesses and considerations for interpretation.

The subgraph matching problem is a fundamental problem in graph theory that is known to be NP-complete. In this study, performers were asked to develop algorithms to search for semantic property graphs that were subgraphs of a large knowledge graph. The templates provided contained structural information about the subgraphs and some attributes for each node and edge. There also exists a similarity measure between a set of attribute values that occurs on every node and edge. Algorithms performed well in the case where an exact match existed, but performers were also provided templates that had noise added such that there existed no match in the knowledge graph. Performers were asked to find the closest matches to those noisy subgraphs. To evaluate performance on this task, we developed a version of the graph edit distance algorithm to measure the cost of editing the template graph so that it is isomorphic in structure and attributes to the performer submission.

The major part of graph generation is deriving connections (links) between entities (nodes). In this paper, a novel technique RAKE-IDF is introduced for key-phrase extraction across multiple documents. There are two main parts to this work: First is a Contextual Lexicon Generation generation step (in this case, White Collar Crime Lexicon) and second is a text-to-graph generation process. The text-graph generation is an ensemble model of state of the art semantic role labeling models with our key-phrase extraction technique: RAKE-IDF. These two parts are combined by augmenting standard graph with lexicon-generation derived elements to ensure interesting components are represented.

Network simulation is essential to test adversarial search problems for privacy preservation and benchmarking purposes. Different generative models have been developed for single-channel, homogeneous networks that model social networks, communication, and co-authorship. Modeling multichannel networks simultaneously with correlated channel attributes at scale compounds complexity, and including adversarial signals across channels creates a second set of burdens. We present a methodology to employ a suite of generation tools to produce realistic large-scale synthetic activity graphs with embed an adversarial activity. We describe our technical process and how we employ subject matter experts (SMEs) to improve the adversarial signal. We discuss challenges in multi-channel modeling and scalability. We also discuss challenges for high fidelity multi-channel network generation at billion-edges scale.

Testing is an integral part of all software systems. In human/machine teaming systems, user inputs must be included in testing. For interactive streaming analytic systems, the user inputs must be contextualized in the dynamic data environment. We introduce a test harness for handling interacting human and machine data streams within interactive streaming analytic systems. We define the four key processes that comprise the test harness: Clock, Journal, Replay, and Database. We define a working library of replay patterns, which are modified human input streams. These can be leveraged to characterize the system behaviors and performance. Implications and applications of the test harness for test and evaluation of human/machine systems are identified. Test harness introduces a modeling and simulation approach for broader test and evaluation of human/machine systems to bootstrap costly user studies in support of validation and verification efforts.

It is impossible to directly visualize all of the items of a large dataset at once. Often, the number of items exceeds the number of pixels. Since direct representation is not a reliable option, a variety of methods have been developed for dealing with indirect representation. Such methods include clustering and intelligent filtering to reduce the number of items being considered in the first place. However, these techniques impose a high computational and interpretation costs. The alternative is to employ techniques to directly deal with the over-plotting that occurs. that occurs when there are too many items to display without overlapping. Over-plotting techniques include alpha composition, color weaving and selective plotting. Each of these has variants that yield different cognitive or computational optimizations. Unfortunately, most advanced over-plotting techniques are wrapped up in specific libraries. Experimenting with different techniques is cumbersome because they have not been provided with uniform interfaces or in a single runtime. This paper presents Abstract Rendering, a recasting of the rendering process that enables concise expression of many over-plotting techniques. Furthermore, the Abstract Rendering formulation yields efficient execution strategies. Combined, it is practical to explore different over-plotting techniques for large data without requiring significant alteration to existing pipelines.