It is commonly known that the conjunctive query entailment problem for certain extensions of (the well-known ontology language) ALC is computationally harder than their knowledge base satisfiability problem while for others the complexities coincide, both under the standard and the finite-model semantics. We expose a uniform principle behind this divide by identifying a wide class of (finitely) locally-forward description logics, for which we prove that (finite) query entailment problem can be solved by a reduction to exponentially many calls of the (finite) knowledge base satisfiability problem. Consequently, our algorithm yields tight ExpTime upper bounds for locally-forward logics with ExpTime-complete knowledge base satisfiability problem, including logics between ALC and µALCHbregQ (and more), as well as ALCSCC with global cardinality constraints, for which the complexity of querying remained open. Moreover, to make our technique applicable in future research, we provide easy-to-check sufficient conditions for a logic to be locally-forward based several versions of the on model-theoretic notion of unravellings. Together with existing results, this provides a nearly complete classification of the “benign” vs. “malign” primitive modelling features extending ALC, missing out only the Self operator. We then show a rather counter-intuitive result, namely that the conjunctive entailment problem for ALCSelf is exponentially harder than for ALC. This places the seemingly innocuous Self operator among the “malign” modelling features, like inverses, transitivity or nominals.

This study aims to comprehend linguistic and socio-demographic features, encompassing English language styles, conveyed sentiments, and lexical diversity within spatial online social media review data. To this end, we undertake a case study that scrutinizes reviews composed by two distinct and demographically diverse groups. Our analysis entails the extraction and examination of various statistical, grammatical, and sentimental features from these two groups. Subsequently, we leverage these features with machine learning (ML) classifiers to discern their potential in effectively differentiating between the groups. Our investigation unveils substantial disparities in certain linguistic attributes between the two groups. When integrated into ML classifiers, these attributes exhibit a marked efficacy in distinguishing the groups, yielding a macro F1 score of approximately 0.85. Furthermore, we conduct a comparative evaluation of these linguistic features with word n-gram-based lexical features in discerning demographically diverse review data. As expected, the n-gram lexical features, coupled with fine-tuned transformer-based models, show superior performance, attaining accuracies surpassing 95\% and macro F1 scores exceeding 0.96. Our meticulous analysis and comprehensive evaluations substantiate the efficacy of linguistic and sentimental features in effectively discerning demographically diverse review data. The findings of this study provide valuable guidelines for future research endeavors concerning the analysis of demographic patterns in textual content across various social media platforms.

Manual ontology construction takes time, resources, and domain specialists. Supporting a component of this process for automation or semi-automation would be good. This project and dissertation provide a Formal Concept Analysis and WordNet framework for learning concept hierarchies from free texts. The process has steps. First, the document is Part-Of-Speech labeled, then parsed to produce sentence parse trees. Verb/noun dependencies are derived from parse trees next. After lemmatizing, pruning, and filtering the word pairings, the formal context is created. The formal context may contain some erroneous and uninteresting pairs because the parser output may be erroneous, not all derived pairs are interesting, and it may be large due to constructing it from a large free text corpus. Deriving lattice from the formal context may take longer, depending on the size and complexity of the data. Thus, decreasing formal context may eliminate erroneous and uninteresting pairs and speed up idea lattice derivation. WordNet-based and Frequency-based approaches are tested. Finally, we compute formal idea lattice and create a classical concept hierarchy. The reduced concept lattice is compared to the original to evaluate the outcomes. Despite several system constraints and component discrepancies that may prevent logical conclusion, the following data imply idea hierarchies in this project and dissertation are promising. First, the reduced idea lattice and original concept have commonalities. Second, alternative language or statistical methods can reduce formal context size. Finally, WordNet-based and Frequency-based approaches reduce formal context differently, and the order of applying them is examined to reduce context efficiently.

Clinicians are increasingly looking towards machine learning to gain insights about patient evolutions. We propose a novel approach named Multi-Modal UMLS Graph Learning (MMUGL) for learning meaningful representations of medical concepts using graph neural networks over knowledge graphs based on the unified medical language system. These representations are aggregated to represent entire patient visits and then fed into a sequence model to perform predictions at the granularity of multiple hospital visits of a patient. We improve performance by incorporating prior medical knowledge and considering multiple modalities. We compare our method to existing architectures proposed to learn representations at different granularities on the MIMIC-III dataset and show that our approach outperforms these methods. The results demonstrate the significance of multi-modal medical concept representations based on prior medical knowledge.

Competency Questions (CQs) are a form of ontology functional requirements expressed as natural language questions. Inspecting CQs together with the axioms in an ontology provides critical insights into the intended scope and applicability of the ontology. CQs also underpin a number of tasks in the development of ontologies e.g. ontology reuse, ontology testing, requirement specification, and the definition of patterns that implement such requirements. Although CQs are integral to the majority of ontology engineering methodologies, the practice of publishing CQs alongside the ontological artefacts is not widely observed by the community. In this context, we present an experiment in retrofitting CQs from existing ontologies. We propose RETROFIT-CQs, a method to extract candidate CQs directly from ontologies using Generative AI. In the paper we present the pipeline that facilitates the extraction of CQs by leveraging Large Language Models (LLMs) and we discuss its application to a number of existing ontologies.

This paper discusses the different roles that explicit knowledge, in particular ontologies, can play in Explainable AI and in the development of human-centric explainable systems and intelligible explanations. We consider three main perspectives in which ontologies can contribute significantly, namely reference modelling, common-sense reasoning, and knowledge refinement and complexity management. We overview some of the existing approaches in the literature, and we position them according to these three proposed perspectives. The paper concludes by discussing what challenges still need to be addressed to enable ontology-based approaches to explanation and to evaluate their human-understandability and effectiveness.

Multi-modal Large Language Models (MLLMs) have demonstrated impressive instruction abilities across various open-ended tasks. However, previous methods primarily focus on enhancing multi-modal capabilities. In this work, we introduce a versatile multi-modal large language model, mPLUG-Owl2, which effectively leverages modality collaboration to improve performance in both text and multi-modal tasks. mPLUG-Owl2 utilizes a modularized network design, with the language decoder acting as a universal interface for managing different modalities. Specifically, mPLUG-Owl2 incorporates shared functional modules to facilitate modality collaboration and introduces a modality-adaptive module that preserves modality-specific features. Extensive experiments reveal that mPLUG-Owl2 is capable of generalizing both text tasks and multi-modal tasks and achieving state-of-the-art performances with a single generic model. Notably, mPLUG-Owl2 is the first MLLM model that demonstrates the modality collaboration phenomenon in both pure-text and multi-modal scenarios, setting a pioneering path in the development of future multi-modal foundation models.

Modern agriculture faces grand challenges to meet increased demands for food, fuel, feed, and fiber with population growth under the constraints of climate change and dwindling natural resources. Data innovation is urgently required to secure and improve the productivity, sustainability, and resilience of our agroecosystems. As various sensors and Internet of Things (IoT) instrumentation become more available, affordable, reliable, and stable, it has become possible to conduct data collection, integration, and analysis at multiple temporal and spatial scales, in real-time, and with high resolutions. At the same time, the sheer amount of data poses a great challenge to data storage and analysis, and the \textit{de facto} data management and analysis practices adopted by scientists have become increasingly inefficient. Additionally, the data generated from different disciplines, such as genomics, phenomics, environment, agronomy, and socioeconomic, can be highly heterogeneous. That is, datasets across disciplines often do not share the same ontology, modality, or format. All of the above make it necessary to design a new data management infrastructure that implements the principles of Findable, Accessible, Interoperable, and Reusable (FAIR). In this paper, we propose Agriculture Data Management and Analytics (ADMA), which satisfies the FAIR principles. Our new data management infrastructure is intelligent by supporting semantic data management across disciplines, interactive by providing various data management/analysis portals such as web GUI, command line, and API, scalable by utilizing the power of high-performance computing (HPC), extensible by allowing users to load their own data analysis tools, trackable by keeping track of different operations on each file, and open by using a rich set of mature open source technologies.

The semantic description of music metadata is a key requirement for the creation of music datasets that can be aligned, integrated, and accessed for information retrieval and knowledge discovery. It is nonetheless an open challenge due to the complexity of musical concepts arising from different genres, styles, and periods -- standing to benefit from a lingua franca to accommodate various stakeholders (musicologists, librarians, data engineers, etc.). To initiate this transition, we introduce the Music Meta ontology, a rich and flexible semantic model to describe music metadata related to artists, compositions, performances, recordings, and links. We follow eXtreme Design methodologies and best practices for data engineering, to reflect the perspectives and the requirements of various stakeholders into the design of the model, while leveraging ontology design patterns and accounting for provenance at different levels (claims, links). After presenting the main features of Music Meta, we provide a first evaluation of the model, alignments to other schema (Music Ontology, DOREMUS, Wikidata), and support for data transformation.

Ontology (and more generally: Knowledge Graph) Matching is a challenging task where information in natural language is one of the most important signals to process. With the rise of Large Language Models, it is possible to incorporate this knowledge in a better way into the matching pipeline. A number of decisions still need to be taken, e.g., how to generate a prompt that is useful to the model, how information in the KG can be formulated in prompts, which Large Language Model to choose, how to provide existing correspondences to the model, how to generate candidates, etc. In this paper, we present a prototype that explores these questions by applying zero-shot and few-shot prompting with multiple open Large Language Models to different tasks of the Ontology Alignment Evaluation Initiative (OAEI). We show that with only a handful of examples and a well-designed prompt, it is possible to achieve results that are en par with supervised matching systems which use a much larger portion of the ground truth.