Explainability and interpretability are cornerstones of frontier and next-generation artificial intelligence (AI) systems. This is especially true in recent systems, such as large language models (LLMs), and more broadly, generative AI. On the other hand, adaptability to new domains, contexts, or scenarios is also an important aspect for a successful system. As such, we are particularly interested in how we can merge these two efforts, that is, investigating the design of transferable and interpretable neurosymbolic AI systems. Specifically, we focus on a class of systems referred to as ''Agentic Retrieval-Augmented Generation'' systems, which actively select, interpret, and query knowledge sources in response to natural language prompts. In this paper, we systematically evaluate how different conceptualizations and representations of knowledge, particularly the structure and complexity, impact an AI agent (in this case, an LLM) in effectively querying a triplestore. We report our results, which show that there are impacts from both approaches, and we discuss their impact and implications.

Educational, learning, and training materials have become extremely commonplace across the Internet. Yet, they frequently remain disconnected from each other, fall into platform silos, and so on. One way to overcome this is to provide a mechanism to integrate the material and provide cross-links across topics. In this paper, we present the Curriculum KG Ontology, which we use as a framework for the dense interlinking of educational materials, by first starting with organizational and broad pedagogical principles. We provide a materialized graph for the Prototype Open Knowledge Network use-case, and validate it using competency questions sourced from domain experts and educators.

KnowWhereGraph is one of the largest fully publicly available geospatial knowledge graphs. It includes data from 30 layers on natural hazards (e.g., hurricanes, wildfires), climate variables (e.g., air temperature, precipitation), soil properties, crop and land-cover types, demographics, and human health, various place and region identifiers, among other themes. These have been leveraged through the graph by a variety of applications to address challenges in food security and agricultural supply chains; sustainability related to soil conservation practices and farm labor; and delivery of emergency humanitarian aid following a disaster. In this paper, we introduce the ontology that acts as the schema for KnowWhereGraph. This broad overview provides insight into the requirements and design specifications for the graph and its schema, including the development methodology (modular ontology modeling) and the resources utilized to implement, materialize, and deploy KnowWhereGraph with its end-user interfaces and public query SPARQL endpoint.

The previously introduced Modular Ontology Modeling methodology (MOMo) attempts to mimic the human analogical process by using modular patterns to assemble more complex concepts. To support this, MOMo organizes organizes ontology design patterns into design libraries, which are programmatically queryable, to support accelerated ontology development, for both human and automated processes. However, a major bottleneck to large-scale deployment of MOMo is the (to-date) limited availability of ready-to-use ontology design patterns. At the same time, Large Language Models have quickly become a source of common knowledge and, in some cases, replacing search engines for questions. In this paper, we thus present a collection of 104 ontology design patterns representing often occurring nouns, curated from the common-sense knowledge available in LLMs, organized into a fully-annotated modular ontology design library ready for use with MOMo.

The Metadata Object Description Schema (MODS) was developed to describe bibliographic concepts and metadata and is maintained by the Library of Congress. Its authoritative version is given as an XML schema based on an XML mindset which means that it has significant limitations for use in a knowledge graphs context. We have therefore developed the Modular MODS Ontology (MMODS-O) which incorporates all elements and attributes of the MODS XML schema. In designing the ontology, we adopt the recent Modular Ontology Design Methodology (MOMo) with the intention to strike a balance between modularity and quality ontology design on the one hand, and conservative backward compatibility with MODS on the other.

We present an ontology design pattern for modeling Names as part of Roles, to capture scenarios where an Agent performs different Roles using different Names associated with the different Roles. Examples of an Agent performing a Role using different Names are rather ubiquitous, e.g., authors who write under different pseudonyms, or different legal names for citizens of more than one country. The proposed pattern is a modified merger of a standard Agent Role and a standard Name pattern stub.

The interactions between the Sun, interplanetary space, near Earth space environment, the Earth's surface, and the power grid are, perhaps unsurprisingly, very complicated. The study of such requires the collaboration between many different organizations spanning the public and private sectors. Thus, an important component of studying space weather is the integration and analysis of heterogeneous information. As such, we have developed a modular ontology to drive the core of the data integration and serve the needs of a highly interdisciplinary community. This paper presents our preliminary modular ontology, for space weather research, as well as demonstrate a method for adaptation to a particular use-case, through the use of existential rules and explicit typing.

Discovering causal structures among latent factors from observed data is a particularly challenging problem, in which many empirical researchers are interested. Despite its success in certain degrees, existing methods focus on the single-domain observed data only, while in many scenarios data may be originated from distinct domains, e.g. in neuroinformatics. In this paper, we propose Multi-Domain Linear Non-Gaussian Acyclic Models for LAtent Factors (abbreviated as MD-LiNA model) to identify the underlying causal structure between latent factors (of interest), tackling not only single-domain observed data but multiple-domain ones, and provide its identification results. In particular, we first locate the latent factors and estimate the factor loadings matrix for each domain separately. Then to estimate the structure among latent factors (of interest), we derive a score function based on the characterization of independence relations between external influences and the dependence relations between multiple-domain latent factors and latent factors of interest, enforcing acyclicity, sparsity, and elastic net constraints. The resulting optimization thus produces asymptotically correct results. It also exhibits satisfactory capability in regimes of small sample sizes or highly-correlated variables and simultaneously estimates the causal directions and effects between latent factors. Experimental results on both synthetic and real-world data demonstrate the efficacy of our approach.

With headlines everywhere focusing on disposable plastics and air travel emissions, it's clear that our individual, everyday purchasing choices--from what we eat to how we get around--impact the world around us. But how about what we wear? According to the UN Alliance for Sustainable Fashion, apparel manufacturing produces 20% of the world's water waste and up to 10% of its carbon output and sends more than 21 billion tons of textiles to landfills each year. But it's also a $2.4 trillion dollar industry that employs more than 60 million people worldwide. Considering this scale and impact, the industry is at a crossroads, devising disruptive technologies, rethinking business models, and searching for innovation at every step -- design, production, distribution, and reuse.

A Japanese construction company recently debuted two designs for robot workers that could make up for the growing lack of human construction workers. In a report from The Daily Mail, these construction robots will only be working during evening hours. One of the robots demonstrated by Shimizu Corp. is already being used in several Japanese construction sites. Called Robo-Buddy, the automaton lifted a bunch of wooden boards before hauling them to the nearest elevator. The Robo-Buddy and its partner, the Robo-Welder, featured robotic arms that can twist and turn to fit in various spaces. Shimizu expected to start deploying them en masse in the latter half of 2018.