Typically available dialogue frameworks have adopted a semantic representation based on dialogue-acts and slot-value pairs. Despite its simplicity, this representation has disadvantages such as the lack of expressivity, scalability and explainability. We present WEBDial: a dialogue framework that relies on a graph formalism by using RDF triples instead of slot-value pairs. We describe its overall architecture and the graph-based semantic representation. We show its applicability from simple to complex applications, by varying the complexity of domains and tasks: from single domain and tasks to multiple domains and complex tasks.

Knowledge Representation (KR) and facet-analytical Knowledge Organization (KO) have been the two most prominent methodologies of data and knowledge modelling in the Artificial Intelligence community and the Information Science community, respectively. KR boasts of a robust and scalable ecosystem of technologies to support knowledge modelling while, often, underemphasizing the quality of its models (and model-based data). KO, on the other hand, is less technology-driven but has developed a robust framework of guiding principles (canons) for ensuring modelling (and model-based data) quality. This paper elucidates both the KR and facet-analytical KO methodologies in detail and provides a functional mapping between them. Out of the mapping, the paper proposes an integrated KO-enriched KR methodology with all the standard components of a KR methodology plus the guiding canons of modelling quality provided by KO. The practical benefits of the methodological integration has been exemplified through a prominent case study of KR-based image annotation exercise.

Crystalline materials, such as metals and semiconductors, nearly always contain a special defect type called dislocation. This defect decisively determines many important material properties, e.g., strength, fracture toughness, or ductility. Over the past years, significant effort has been put into understanding dislocation behavior across different length scales via experimental characterization techniques and simulations. This paper introduces the dislocation ontology (DISO), which defines the concepts and relationships related to linear defects in crystalline materials. We developed DISO using a top-down approach in which we start defining the most general concepts in the dislocation domain and subsequent specialization of them. DISO is published through a persistent URL following W3C best practices for publishing Linked Data. Two potential use cases for DISO are presented to illustrate its usefulness in the dislocation dynamics domain. The evaluation of the ontology is performed in two directions, evaluating the success of the ontology in modeling a real-world domain and the richness of the ontology.

A critical function of an organization is to foster the level of integration (coordination and cooperation) necessary to achieve its objectives. The need to coordinate and motivation to cooperate emerges from the myriad dependencies between an organization's members and their work. Therefore, to reason about solutions to coordination and cooperation problems requires a robust representation that includes the underlying dependencies. We find that such a representation remains missing from formal organizational models, and we leverage semantics to bridge this gap. Drawing on well-established organizational research and our extensive fieldwork with one of North America's largest municipalities, (1) we introduce an ontology, formalized in first-order logic, that operationalizes concepts like outcome, reward, and epistemic dependence, and their links to potential integration risks; and (2) present real-world applications of this ontology to analyze and support integration in complex government infrastructure projects. Our ontology is implemented and validated in both Z3 and OWL. Key features of our model include inferable dependencies, explainable coordination and cooperation risks, and actionable insights on how dependency structures within an organization can be altered to mitigate the risks. Conceptualizing real-world challenges like incentive misalignment, free-riding, and subgoal optimization in terms of dependency structures, our semantics-based approach represents a novel method for modelling and enhancing coordination and cooperation. Integrated within a decision-support system, our model may serve as an impactful aid for organizational design and effectiveness. More broadly, our approach underscores the transformative potential of semantics in deriving tangible, real-world value from existing organization theory.

Ontology revision aims to seamlessly incorporate a new ontology into an existing ontology and plays a crucial role in tasks such as ontology evolution, ontology maintenance, and ontology alignment. Similar to repair single ontologies, resolving logical incoherence in the task of ontology revision is also important and meaningful, because incoherence is a main potential factor to cause inconsistency and reasoning with an inconsistent ontology will obtain meaningless answers.To deal with this problem, various ontology revision approaches have been proposed to define revision operators and design ranking strategies for axioms in an ontology. However, they rarely consider axiom semantics which provides important information to differentiate axioms. In addition, pre-trained models can be utilized to encode axiom semantics, and have been widely applied in many natural language processing tasks and ontology-related ones in recent years.Therefore, in this paper, we study how to apply pre-trained models to revise ontologies. We first define four scoring functions to rank axioms based on a pre-trained model by considering various information from an ontology. Based on the functions, an ontology revision algorithm is then proposed to deal with unsatisfiable concepts at once. To improve efficiency, an adapted revision algorithm is designed to deal with unsatisfiable concepts group by group. We conduct experiments over 19 ontology pairs and compare our algorithms and scoring functions with existing ones. According to the experiments, our algorithms could achieve promising performance.

Knowledge Graphs (KGs) such as Resource Description Framework (RDF) data represent relationships between various entities through the structure of triples (). Knowledge graph embedding (KGE) is crucial in machine learning applications, specifically in node classification and link prediction tasks. KGE remains a vital research topic within the semantic web community. RDF-star introduces the concept of a quoted triple (QT), a specific form of triple employed either as the subject or object within another triple. Moreover, RDF-star permits a QT to act as compositional entities within another QT, thereby enabling the representation of recursive, hyper-relational KGs with nested structures. However, existing KGE models fail to adequately learn the semantics of QTs and entities, primarily because they do not account for RDF-star graphs containing multi-leveled nested QTs and QT-QT relationships. This study introduces RDF-star2Vec, a novel KGE model specifically designed for RDF-star graphs. RDF-star2Vec introduces graph walk techniques that enable probabilistic transitions between a QT and its compositional entities. Feature vectors for QTs, entities, and relations are derived from generated sequences through the structured skip-gram model. Additionally, we provide a dataset and a benchmarking framework for data mining tasks focused on complex RDF-star graphs. Evaluative experiments demonstrated that RDF-star2Vec yielded superior performance compared to recent extensions of RDF2Vec in various tasks including classification, clustering, entity relatedness, and QT similarity.

Creating knowledge bases and ontologies is a time consuming task that relies on a manual curation. AI/NLP approaches can assist expert curators in populating these knowledge bases, but current approaches rely on extensive training data, and are not able to populate arbitrary complex nested knowledge schemas. Here we present Structured Prompt Interrogation and Recursive Extraction of Semantics (SPIRES), a Knowledge Extraction approach that relies on the ability of Large Language Models (LLMs) to perform zero-shot learning (ZSL) and general-purpose query answering from flexible prompts and return information conforming to a specified schema. Given a detailed, user-defined knowledge schema and an input text, SPIRES recursively performs prompt interrogation against GPT-3+ to obtain a set of responses matching the provided schema. SPIRES uses existing ontologies and vocabularies to provide identifiers for all matched elements. We present examples of use of SPIRES in different domains, including extraction of food recipes, multi-species cellular signaling pathways, disease treatments, multi-step drug mechanisms, and chemical to disease causation graphs. Current SPIRES accuracy is comparable to the mid-range of existing Relation Extraction (RE) methods, but has the advantage of easy customization, flexibility, and, crucially, the ability to perform new tasks in the absence of any training data. This method supports a general strategy of leveraging the language interpreting capabilities of LLMs to assemble knowledge bases, assisting manual knowledge curation and acquisition while supporting validation with publicly-available databases and ontologies external to the LLM. SPIRES is available as part of the open source OntoGPT package: https://github.com/ monarch-initiative/ontogpt.

Recent advances in natural language processing (NLP) owe their success to pre-training language models on large amounts of unstructured data. Still, there is an increasing effort to combine the unstructured nature of LMs with structured knowledge and reasoning. Particularly in the rapidly evolving field of biomedical NLP, knowledge-enhanced language models (KELMs) have emerged as promising tools to bridge the gap between large language models and domain-specific knowledge, considering the available biomedical knowledge graphs (KGs) curated by experts over the decades. In this paper, we develop an approach that uses lightweight adapter modules to inject structured biomedical knowledge into pre-trained language models (PLMs). We use two large KGs, the biomedical knowledge system UMLS and the novel biochemical ontology OntoChem, with two prominent biomedical PLMs, PubMedBERT and BioLinkBERT. The approach includes partitioning knowledge graphs into smaller subgraphs, fine-tuning adapter modules for each subgraph, and combining the knowledge in a fusion layer. We test the performance on three downstream tasks: document classification,question answering, and natural language inference. We show that our methodology leads to performance improvements in several instances while keeping requirements in computing power low. Finally, we provide a detailed interpretation of the results and report valuable insights for future work.

The escalating complexity of modern computing frameworks has resulted in a surge in the cybersecurity vulnerabilities reported to the National Vulnerability Database (NVD) by practitioners. Despite the fact that the stature of NVD is one of the most significant databases for the latest insights into vulnerabilities, extracting meaningful trends from such a large amount of unstructured data is still challenging without the application of suitable technological methodologies. Previous efforts have mostly concentrated on software vulnerabilities; however, a holistic strategy incorporates approaches for mitigating vulnerabilities, score prediction, and a knowledge-generating system that may extract relevant insights from the Common Weakness Enumeration (CWE) and Common Vulnerability Exchange (CVE) databases is notably absent. As the number of hardware attacks on Internet of Things (IoT) devices continues to rapidly increase, we present the Hardware Vulnerability to Weakness Mapping (HW-V2W-Map) Framework, which is a Machine Learning (ML) framework focusing on hardware vulnerabilities and IoT security. The architecture that we have proposed incorporates an Ontology-driven Storytelling framework, which automates the process of updating the ontology in order to recognize patterns and evolution of vulnerabilities over time and provides approaches for mitigating the vulnerabilities. The repercussions of vulnerabilities can be mitigated as a result of this, and conversely, future exposures can be predicted and prevented. Furthermore, our proposed framework utilized Generative Pre-trained Transformer (GPT) Large Language Models (LLMs) to provide mitigation suggestions.

The term life sciences refers to the disciplines that study living organisms and life processes, and include chemistry, biology, medicine, and a range of other related disciplines. Research efforts in life sciences are heavily data-driven, as they produce and consume vast amounts of scientific data, much of which is intrinsically relational and graph-structured. The volume of data and the complexity of scientific concepts and relations referred to therein promote the application of advanced knowledge-driven technologies for managing and interpreting data, with the ultimate aim to advance scientific discovery. In this survey and position paper, we discuss recent developments and advances in the use of graph-based technologies in life sciences and set out a vision for how these technologies will impact these fields into the future. We focus on three broad topics: the construction and management of Knowledge Graphs (KGs), the use of KGs and associated technologies in the discovery of new knowledge, and the use of KGs in artificial intelligence applications to support explanations (explainable AI). We select a few exemplary use cases for each topic, discuss the challenges and open research questions within these topics, and conclude with a perspective and outlook that summarizes the overarching challenges and their potential solutions as a guide for future research.