Knowledge Graph semantic enhancement of input data for improving AI

Intelligent systems designed using machine learning algorithms require a large number of labeled data. Background knowledge provides complementary, real world factual information that can augment the limited labeled data to train a machine learning algorithm. The term Knowledge Graph (KG) is in vogue as for many practical applications, it is convenient and useful to organize this background knowledge in the form of a graph. Recent academic research and implemented industrial intelligent systems have shown promising performance for machine learning algorithms that combine training data with a knowledge graph. In this article, we discuss the use of relevant KGs to enhance input data for two applications that use machine learning -- recommendation and community detection. The KG improves both accuracy and explainability

Evaluating the Deductive Competence of Large Language Models

The development of highly fluent large language models (LLMs) has prompted increased interest in assessing their reasoning and problem-solving capabilities. We investigate whether several LLMs can solve a classic type of deductive reasoning problem from the cognitive science literature. The tested LLMs have limited abilities to solve these problems in their conventional form. We performed follow up experiments to investigate if changes to the presentation format and content improve model performance. We do find performance differences between conditions; however, they do not improve overall performance. Moreover, we find that performance interacts with presentation format and content in unexpected ways that differ from human performance. Overall, our results suggest that LLMs have unique reasoning biases that are only partially predicted from human reasoning performance.Comment: 7 pages, 7 figures, under revie

Expanding the Set of Pragmatic Considerations in Conversational AI

Despite considerable performance improvements, current conversational AI systems often fail to meet user expectations. We discuss several pragmatic limitations of current conversational AI systems. We illustrate pragmatic limitations with examples that are syntactically appropriate, but have clear pragmatic deficiencies. We label our complaints as "Turing Test Triggers" (TTTs) as they indicate where current conversational AI systems fall short compared to human behavior. We develop a taxonomy of pragmatic considerations intended to identify what pragmatic competencies a conversational AI system requires and discuss implications for the design and evaluation of conversational AI systems.Comment: Pre-print version of paper that appeared at Multidisciplinary Perspectives on COntext-aware embodied Spoken Interactions (MP-COSIN) workshop at IEEE RO-MAN 202

Defining and Detecting Toxicity on Social Media: Context and Knowledge are Key

As the role of online platforms has become increasingly prominent for communication, toxic behaviors, such as cyberbullying and harassment, have been rampant in the last decade. On the other hand, online toxicity is multi-dimensional and sensitive in nature, which makes its detection challenging. As the impact of exposure to online toxicity can lead to serious implications for individuals and communities, reliable models and algorithms are required for detecting and understanding such communications. In this paper We define toxicity to provide a foundation drawing social theories. Then, we provide an approach that identifies multiple dimensions of toxicity and incorporates explicit knowledge in a statistical learning algorithm to resolve ambiguity across such dimensions

Creating a common trajectory: Shared decision making and distributed cognition in medical consultations

The growing literature on shared decision making and patient centered care emphasizes the patient’s role in clinical care, but research on clinical reasoning almost exclusively addresses physician cognition. In this article, we suggest clinical cognition is distributed between physicians and patients and assess how distributed clinical cognition functions during interactions between medical professionals and patients with Multiple Sclerosis (MS). A combination of cognitive task analysis and discourse analysis reveals the distribution of clinical reasoning between 24 patients and 3 medical professionals engaged in MS management. Findings suggest that cognition was distributed between patients and physicians in all major tasks except for the interpretation of MRI results. Otherwise, patients and physicians collaborated through discourse to develop a common trajectory to guide clinical reasoning. The patients’ role in clinical cognition expands the concept of patient-centered care and suggests the need to optimize physician-patient distributed cognition rather than physician cognition in isolation

Requesting Distant Robotic Action: An Ontology for Naming and Action Identification for Planning on the Mars Exploration Rover Mission.

This paper focuses on the development of a naming convention and the use of abbreviated names and a related ontology for science work and distant robotic action that comprise requests for a robotic rover during the NASA Mars Exploration Rover (MER) mission, run by the Jet Propulsion Laboratory (JPL). We demonstrate how abbreviated names and an associated ontology support sharing and identifying information among teams and software tools. An ontology of distant action must take into account a dynamic environment, changing in response to physical events and intentional actions, and reflect the influence of context on the meaning of action. The nascent domain of Martian tele-robotic science, in which specialists request work from a rover moving through a distant landscape, as well as the need to consider the interdisciplinary teams involved in completing that work, required an empirical approach. The formulation of this ontology used ethnographic methods and grounded theory to study human behavior and work practice with software tools

Towards Efficient Scoring of Student-generated Long-form Analogies in STEM

Switching from an analogy pedagogy based on comprehension to analogy pedagogy based on production raises an impractical manual analogy scoring problem. Conventional symbol-matching approaches to computational analogy evaluation focus on positive cases, and challenge computational feasibility. This work presents the Discriminative Analogy Features (DAF) pipeline to identify the discriminative features of strong and weak long-form text analogies. We introduce four feature categories (semantic, syntactic, sentiment, and statistical) used with supervised vector-based learning methods to discriminate between strong and weak analogies. Using a modestly sized vector of engineered features with SVM attains a 0.67 macro F1 score. While a semantic feature is the most discriminative, out of the top 15 discriminative features, most are syntactic. Combining these engineered features with an ELMo-generated embedding still improves classification relative to an embedding alone. While an unsupervised K-Means clustering-based approach falls short, similar hints of improvement appear when inputs include the engineered features used in supervised learning