Contextualizing a Collection: Compositional, Morphological, and Trade Network Insights from an Iron Age Collection of Rare Southeast Asian Glass Ornaments

Though Iron Age (500 BCE-500 CE) Southeast Asian glass ornament research is a well-established field, previous studies have almost exclusively examined glass beads comprising the majority of glass ornament assemblages at Iron Age Southeast Asian sites. When mentioned, other ornament types (e.g., rings, bangles, and earrings) are typically fragmented or in otherwise poor condition. This study is one of the first to report on the elemental compositions of complete rare glass ornaments—specifically, a collection of seven complete glass earrings, five complete glass bangles, and a single spiral ornament. These objects were donated to the University of Oregon for repatriation to Cambodia and are believed to originate from the site of Phum Snay, Cambodia. Using primarily LA-ICP-MS compositional data, we contextualize this collection within the corpus of glass ornaments that circulated in Iron Age Southeast Asia as well as contemporaneous glass trade networks and associated spheres of influence using compositional analysis of six of these objects. Results from this analysis identified multiple glass types and subtypes, including potash glass and soda glass. This research is ultimately relevant for its novel compositional and morphological data and insights into the circulation of these rare ornaments within regional exchange networks

The Integration of Artificial Intelligence and Ontologies: Transformations in Knowledge Representation and Application

Artificial Intelligence (AI) is reshaping the landscape of knowledge representation. There is an increasingly strong bidirectional relationship, between AI techniques and ontologies. AI techniques revolutionized traditional, manual ontology development and contribute to automated ontology construction, while ontologies enhance the performance of AI systems and their semantic accuracy. Through a comprehensive review of current literature, this paper aims to examine: i) how Machine Learning (ML) techniques contribute to the automated construction, refinement, and validation of ontologies; ii) the most widely used and effective ML approaches for ontology construction; iii) how domain-specific requirements influence the selection and adaptation of AI techniques for building and applying ontologies; iv) how ontologies enhance the interpretability, explainability, and reliability of AI. This overview highlights the integration between AI and ontology engineering across different domains and indicates that so far successful AI-ontology integration typically follows a collaborative model, in which AI acts as an intelligent assistant to human experts, combining computational efficiency with critical domain knowledge. Ethical concerns, such as bias and hallucinations, remain pressing challenges that require standardized frameworks and careful considerations. However, this reciprocal relationship between AI and ontologies points to the development of more dynamic, adaptive, and complete ontologies

A Domain Analytic View of Interdisciplinary Studies

We perform a domain analysis of two recent volumes, The Encyclopedia of Interdisciplinarity and Transdisciplinarity, and The Handbook of Interdisciplinary Teaching and Administration. These volumes provide a useful snapshot of a field that is global in scope and draws on scholars with backgrounds in numerous academic fields. We identify most-cited authors, co-citation patterns, and most common publication outlets and dates of citations. One remarkable result is the dominance of publication outlets in the Handbook by one journal. More generally, our results support the idea that there is a shared global conversation but nevertheless a divergence in citation patterns within that global conversation. Our analysis of the most common terms in Abstracts and Keywords supports the general conclusion of one shared conversation, but yet with some notable differences

Artificial Intelligence in Knowledge Representation, Organization and Discovery: Key Competencies and Design Considerations

skills and competencies to navigate the ethical challenges inherent in these technologies. There are several ethical concerns associated with the application of AI technologies, including the accuracy of AI-generated information, biases embedded in AI training data, privacy and surveillance risks, and potential negative impact of AI on career paths.. Ethical application of artificial intelligence technologies requires thoughtful design of knowledge representation and organization systems to ensure transparency, explainability, contextualization, and critical reflection throughout the technology- driven processes of knowledge representation, organization and discovery. This paper examines the essential skills, competencies, and design considerations required for the ethical application of AI technologies in knowledge representation, organization and discovery

OBITUARY: DR DAMIAN EVANS, ARCHAEOLOGIST OF ANGKOR (1975–2023): OBITUARY, DR DAMIAN EVANS

We mourn the passing of Damian Evans, who died after a short battle with an aggressive cancer on 12 September 2023, in Paris, France. He was a major scholar of the Angkorian Empire and his work will influence generations to come

Cosine Similarity Indexing of Word Embeddings Using Knowledge Organization Systems

This paper proposes a new technique for cosine similarity indexing in the era of large language models (LLMs). It investigates how knowledge organization systems (KOS) can be used to index the latent spaces which LLMs produce. A latent space is a multidimensional feature space used by a model to encode the context of data items. In the case of an LLM, a typical latent space is a word embedding, which gives every word a “position” in a multidimensional feature space, where the features are opaque, and not human-readable. This work asks: can indexing such latent spaces with KOSs help make LLMs more explainable? It builds on previous work in latent semantic indexing for information retrieval models to see if similar techniques can be used to bridge KOSs and LLMs. It also investigates how this method can be applied to improving the performance of multilingual information retrieval. A cross-lingual ontology (called Horapollo) is used to index two latent spaces containing Wikipedia articles written in English and Arabic. Then, the distance between equivalent articles in both spaces are taken, raising questions about the use of KOSs for multilingual and transdisciplinary information retrieval tasks in the era of semantic search

Census.gov Data, from Paper Tables to APIs: A Retrieval Augmented Generation Domain Analysis

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Obituary of Darlene R. Moore (January 19, 1938 to June 22, 2024)

Darlene Moore was instrumental in advancing the history and prehistory of the Mariana Islands during a career that spanned 40 years. She created the first female owned and operated archaeological consulting firm in the Pacific, mentored archaeology students on Guam, and contributed to documenting, evaluating, and interpreting archaeological sites, with an emphasis on ceramic and agricultural production.&nbsp

Large Language Models (LLMs) and Cataloging: Exploring How ChatGPT and Copilot Assign Subject Headings and Call Numbers

Large Language Models (LLMs) have demonstrated some facility in language- and knowledge-intensive tasks that require domain knowledge, such as writing and computer programming. These syntactic facilities suggest that general purpose LLMs might be able to perform subject cataloging tasks like assigning subject headings and class numbers. This paper investigates how two commercially available LLMs (ChatGPT and Copilot) assign subject headings using the Library of Congress Subject Headings (LCSH) and the Sears List of Subject Headings, class numbers using Library of Congress Classification (LCC) and Dewey Decimal Classification (DDC), item numbers using Cutter numbers, and MARC fields for subject headings and call numbers. The paper finds that the LLMs show promise as automated catalogers, but exhibit numerous shortcomings, including: lacking specificity, using unauthorized terms, incorrectly assembling synthetic headings, assigning inaccurate headings and classes, and formatting MARC records incorrectly. Based on these findings, potential cataloging applications for current LLMs are primarily as aides and teaching tools, not as fully automated cataloging solutions. Additionally, collections considering LLMs for cataloging tasks should be aware of issues associated with these technologies, including environmental harm, de-skilling, intellectual theft, and bias

Interlinking the Intrinsic Value: Re-Conceptualization of Organizing Cultural Heritage Using Smart Data

This paper introduces the Cultural Heritage Conceptual Framework (CHCF), a smart data–driven model designed to semantically interlink cultural heritage resources across diverse institutions. Although cultural heritage comprises vast and heterogeneous social assets curated by various institutions, their intellectual and intrinsic values often remain underutilized because of the traditional unit-based rigid metadata approaches. Drawing on the concept of smart data, this study constructed a conceptual framework named Lead Data, which consists of three core categories such as Area, Subject, and Collection, and applied semantic facet structure to enhance the interlinking of cultural heritage resources at the metadata level. By extracting conceptually significant elements from existing metadata standards and organizing them as facets, the CHCF can function as a mediator that interoperates the metadata about cultural heritage resources. This smart data-based framework is expected to support advanced knowledge discovery, support interdisciplinary research, and improves accessibility within cultural heritage environments