Reviewerly/exHarmony huggingface dataset:
A Step Towards Absolute Versions of Metamathematical Results

Abstract There is a well-known gap between metamathematical theorems and their philosophical interpretations. Take Tarski’s Theorem. According to its prevalent interpretation, the collection of all arithmetical truths is not arithmetically definable. However, the underlying metamathematical theorem merely establishes the arithmetical undefinability of a set of specific Gödel codes of certain artefactual entities, such as infix strings, which are true in the standard model. That is, as opposed to its philosophical reading, the metamathematical theorem is formulated (and proved) relative to a specific choice of the Gödel numbering and the notation system. A similar observation applies to Gödel’s and Church’s theorems, which are commonly taken to impose severe limitations on what can be proved and computed using the resources of certain formalisms. The philosophical force of these limitative results heavily relies on the belief that these theorems do not depend on contingencies regarding the underlying formalisation choices. The main aim of this paper is to provide metamathematical facts which support this belief. While employing a fixed notation system, I showed in previous work ( Review of Symbolic Logic , 2021, 14(1):51–84) how to abstract away from the choice of the Gödel numbering. In the present paper, I extend this work by establishing versions of Tarski’s, Gödel’s and Church’s theorems which are invariant regarding both the notation system and the numbering. This paper thus provides a further step towards absolute versions of metamathematical results which do not rely on contingent formalisation choices.
CloudSim express: A novel framework for rapid low code simulation of cloud computing environments

Abstract Cloud computing environment simulators enable cost‐effective experimentation of novel infrastructure designs and management approaches by avoiding significant costs incurred from repetitive deployments in real Cloud platforms. However, widely used Cloud environment simulators compromise on usability due to complexities in design and configuration, along with the added overhead of programming language expertise. Existing approaches attempting to reduce this overhead, such as script‐based simulators and graphical user interface (GUI) based simulators, often compromise on the extensibility of the simulator. Simulator extensibility allows for customization at a fine‐grained level, thus reducing it significantly affects flexibility in creating simulations. To address these challenges, we propose an architectural framework to enable human‐readable script‐based simulations in existing Cloud environment simulators while minimizing the impact on simulator extensibility. We implement the proposed framework for the widely used Cloud environment simulator, the CloudSim toolkit, and compare it against state‐of‐the‐art baselines using a practical use case. The resulting framework, called CloudSim Express , achieves extensible simulations while surpassing baselines with over a % reduction in code complexity and an 89.42% reduction in lines of code.
Focus Set Semantic Differences

Ontologies are being utilized widely as sources for formally organized information in a range of fields. The SNOMED CT ontology is a key resource in national and international health sectors for automatically linking information captured by diverse clinical information systems and research data ensuring consistent patient data capture and effective data analytics and decision support. Offering a comprehensive multilingual vocabulary for encoding clinical knowledge of multiple domains, the ontology is large and new releases are created regularly to reflect domain changes and user requirements. The main contribution of the paper is a novel automated approach for tracking semantic differences of subdomains in different versions of SNOMED CT targeted at terminologists, debuggers, ontology evaluators and developers of software using SNOMED CT. Whereas the semantic difference sets produced with existing methods are rather large and difficult to analyze, our method produces concise semantic difference sets for user-specified input focus concepts. Our method is based on subontology generation and semantic difference computation using uniform interpolation, which aids in finding inferred differences that other semantic difference tools do not reveal. The obtained semantic difference sets are related to the meaning of focus concept definitions for specific ontology subdomains, where some of these differences would not have been generated without this focused method for computing semantic differences between ontologies. A case study using SNOMED CT has shown the proposed approach is useful for domain experts.
An Evaluation of Strategies to Train More Efficient Backward-Chaining Reasoners

Knowledge bases traditionally require manual optimization to ensure reasonable performance when answering queries. We build on previous work on training a deep learning model to learn heuristics for answering queries by comparing different representations of the sentences contained in knowledge bases. We decompose the problem into issues of representation, training, and control and propose solutions for each subproblem. We evaluate different configurations on three synthetic knowledge bases. In particular we compare a novel representation approach based on learning to maximize similarity of logical atoms that unify and minimize similarity of atoms that do not unify, to two vectorization strategies taken from the automated theorem proving literature: a chain-based and a 3-term-walk strategy. We also evaluate the efficacy of pruning the search by ignoring rules with scores below a threshold.
Knowledge-enhanced Agents for Interactive Text Games

Communication via natural language is a key aspect of machine intelligence, and it requires computational models to learn and reason about world concepts, with varying levels of supervision. Significant progress has been made on fully-supervised non-interactive tasks, such as question-answering and procedural text understanding. Yet, various sequential interactive tasks, as in text-based games, have revealed limitations of existing approaches in terms of coherence, contextual awareness, and their ability to learn effectively from the environment. In this paper, we propose a knowledge-injection framework for improved functional grounding of agents in text-based games. Specifically, we consider two forms of domain knowledge that we inject into learning-based agents: memory of previous correct actions and affordances of relevant objects in the environment. Our framework supports two representative model classes: reinforcement learning agents and language model agents. Furthermore, we devise multiple injection strategies for the above domain knowledge types and agent architectures, including injection via knowledge graphs and augmentation of the existing input encoding strategies. We experiment with four models on the 10 tasks in the ScienceWorld text-based game environment, to illustrate the impact of knowledge injection on various model configurations and challenging task settings. Our findings provide crucial insights into the interplay between task properties, model architectures, and domain knowledge for interactive contexts.
Annotation and Extraction of Industrial Procedural Knowledge from Textual Documents

The ability to extract valuable information from documents and convert it into knowledge is crucial for driving technological innovation across industries. While adding metadata to manuals enhances their searchability, the real knowledge is still hidden in the procedural information they contain, which offers vital guidance for operators. Therefore, the approach of extracting and transforming unstructured human-readable information into machine-interpretable data is fundamental for establishing cutting-edge digital knowledge-based platforms. This paper presents a methodology tailored to the specific requirements of users who are seeking support in extracting and representing procedural knowledge from documents. We introduce a tool designed to support users in manually annotating procedures within PDF documents and generating a corresponding procedural knowledge graph. We assess the tool in real-world scenarios, aimed at evaluating its effectiveness in accomplishing various tasks. Finally, we generate a procedural knowledge graph that can facilitate knowledge discovery.
The Performance of Post-Quantum TLS 1.3

Quantum Computers (QCs) differ radically from traditional computers and can efficiently solve mathematical problems fundamental to our current cryptographic algorithms. Although existing QCs need to accommodate more qubits to break cryptographic algorithms, the concern of ''Store-Now-Decrypt-Later'' (i.e., adversaries store encrypted data today and decrypt them once powerful QCs become available) highlights the necessity to adopt quantum-safe approaches as soon as possible. In this work, we investigate the performance impact of Post-Quantum Cryptography (PQC) on TLS 1.3. Different signature algorithms and key agreements (as proposed by the National Institute of Standards and Technology (NIST)) are examined through black- and white-box measurements to get precise handshake latencies and computational costs per participating library. We emulated loss, bandwidth, and delay to analyze constrained environments. Our results reveal that HQC and Kyber are on par with our current state-of-the-art, while Dilithium and Falcon are even faster. We observed no performance drawback from using hybrid algorithms; moreover, on higher NIST security levels, PQC outperformed any algorithm in use today. Hence, we conclude that post-quantum TLS is suitable for adoption in today's systems.
Lingua Franca – Entity-Aware Machine Translation Approach for Question Answering over Knowledge Graphs

This research paper proposes an approach called Lingua Franca that improves machine translation quality by utilizing information from a knowledge graph to translate named entities accurately. The accurate entity translation is crucial when applied to entity-oriented search including Knowledge Graph Question Answering systems. In a nutshell, the approach preserves recognized named entities with an entity-replacement technique during the translation process. It replaces the entities back with their labels found in a knowledge graph for the target language to ensure that questions are translated correctly before answering them using a Knowledge Graph Question Answering system. The paper also introduces an open-source modular framework that enables researchers to design their own named entity-aware machine translation pipelines. The presented experimental results demonstrate the effectiveness of the Lingua Franca approach in comparison to baseline Machine Translation models. The approach shows a statistically significant improvement in the quality provided by several Knowledge Graph Question Answering systems using Lingua Franca on different datasets.
Tunable Query Optimizer for Web APIs and User Preferences

To answer queries many SPARQL query processors use different sources, e.g., various knowledge bases (KBs) or end points. RESTful Web APIs are rarely the focus of those systems as they come with many limitations, like not being able to process SPARQL queries. Moreover, most existing approaches optimize their query plans only for performance, even though users often have additional preferences, e.g., coverage, reliability, or currency. Additionally, data is often provided with different levels of quality so that not all sources should be trusted equally. In this paper, we therefore present TunA, a query engine that is able to combine RESTful Web APIs and local RDF KBs in the form of triple stores while tuning its (query) plans towards user preferences. Erroneous information from Web APIs is detected using hierarchical agglomerative clustering. Our evaluation shows that TunA outperforms current state-of-the-art systems and is less vulnerable to erroneous information, even in settings where only unreliable sources are available.
Knowledge Engineering for Hybrid Intelligence

Hybrid Intelligence (HI) is a rapidly growing field aiming at creating collaborative systems where humans and intelligent machines cooperate in mixed teams towards shared goals. A clear characterization of the tasks and knowledge exchanged by the agents in HI applications is still missing, hampering both standardization and reuse when designing new HI systems. Knowledge Engineering (KE) methods have been used to solve such issue through the formalization of tasks and roles in knowledge-intensive processes. We investigate whether KE methods can be applied to HI scenarios, and specifically whether common, reusable elements such as knowledge roles, tasks and subtasks can be identified in contexts where symbolic, subsymbolic and human-in-the-loop components are involved. We first adapt the well-known CommonKADS methodology to HI, and then use it to analyze several HI projects and identify common tasks. The results are (i) a high-level ontology of HI knowledge roles, (ii) a set of novel, HI-specific tasks and (iii) an open repository to store scenarios1 – allowing reuse, validation and design of existing and new HI applications.
Unflattening Knowledge Graphs

Large general-purpose knowledge graphs (KGs) are a critical component for knowledge-driven applications. However, most KGs represent only a limited view of the entities and concepts they describe. The concept coffee can, for example, refer to the plant that yields coffee seeds, the beverage ‘coffee’, and the activity of drinking the beverage. Moreover, it has a long history that is deeply connected to colonialism and status. All of these notions are an intricate part of national identities, have changed dramatically over time, and connect to many different narratives with different opinions on them. This complexity is not captured in current KGs. In this vision paper, I present the three crucial challenges for unflattening knowledge graphs and directions for future work.
NLFOA: Natural Language Focused Ontology Alignment

For Ontology Alignment (OA), the task is to align semantically equivalent concepts and relations from different ontologies. This task plays a crucial role in many downstream tasks and applications in academia and industry. Since manually aligning ontologies is inefficient and costly, numerous approaches exist to do this automatically. However, most approaches are tailored to specific domains, are rule-based systems or based on feature engineering, and require external knowledge. The most recent advances in the field of OA rely on the widely proven effectiveness of pre-trained language models to represent the human-generated language that describes the entities in an ontology. However, these approaches additionally require sophisticated algorithms or Graph Neural Networks to exploit an ontology’s graphical structure to achieve state-of-the-art performance. In this work, we present NLFOA, or Natural Language Focused Ontology Alignment, which purely focuses on the natural language contained in ontologies to process the ontology’s semantics as well as graphical structure. An evaluation of our approach on common OA datasets shows superior results when finetuning with only a small number of training samples. Additionally, it demonstrates strong results in a zero-shot setting which could be employed in an active learning setup to reduce human labor when manually aligning ontologies significantly.
XSD2SHACL: Capturing RDF Constraints from XML Schema

SHACL shapes describe the constraints of RDF subgraphs which are constructed from heterogeneous data, such as RDBs, JSONs, XMLs, etc. These heterogeneous data often already have constraints defined in their schemas, e.g., JSON Schema for JSON or XSD for XML, but this information is ignored when the RDF graph is constructed, as there are currently not many works that translate such schemas into SHACL. In this paper, we focus on the incorporation of XSD constraints for XML data sources in SHACL shapes. We define a translation from XSD to SHACL, and provide a corresponding system. We compare our solution with XMLSchema2ShEx which translates XSD constraints to ShEx and validate our solution against two use cases. Our solution provides the desired SHACL shapes in a reasonable time. This allows us to automatically derive SHACL shapes for some original raw data without any manual effort.
Representation Learning for Person or Entity-Centric Knowledge Graphs: An Application in Healthcare

Knowledge graphs (KGs) are a popular way to organise information based on ontologies or schemas. Despite advances in KGs, representing knowledge remains a non-trivial task across industries and it is especially challenging in the biomedical and healthcare domains due to complex interdependent relations between entities, heterogeneity, lack of standardization, and sparseness of data. KGs are used to discover diagnoses or prioritize genes relevant to disease, but they often rely on schemas that are not centred around a node or entity of interest, such as a person. Entity-centric KGs are relatively unexplored but hold promise in representing important facets connected to a central node and unlocking downstream tasks beyond graph traversal and reasoning, such as training graph neural networks (GNNs) for a wide range of predictive tasks. This paper presents an end-to-end representation learning framework to extract entity-centric KGs from structured and unstructured data. We introduce a star-shaped ontology to represent the multiple facets of a person and use it to guide KG creation. Compact representations of the graphs are created leveraging GNNs and experiments are conducted using different levels of heterogeneity or explicitness. A readmission prediction task is used to evaluate the results of the proposed framework, showing a stable system, robust to missing data, that outperforms a range of baseline machine learning classifiers. We highlight that this approach has several potential applications across domains and is open-sourced.
Dual-Stage OOD Detection Learning with an Unsupervised Start

research-article Share on Dual-Stage OOD Detection Learning with an Unsupervised Start Authors: Jaturong Kongmanee University of Toronto, Canada University of Toronto, Canada 0009-0003-6153-7960View Profile , Thanyathorn Thanapattheerakul Innovative Cognitive Computing (IC2) Research Center School of Information Technology King Mongkut?s University of Technology Thonburi, Thailand Innovative Cognitive Computing (IC2) Research Center School of Information Technology King Mongkut?s University of Technology Thonburi, Thailand 0009-0004-6954-2243View Profile , Mark Chignell University of Toronto, Canada University of Toronto, Canada 0000-0001-8120-6905View Profile Authors Info & Claims IAIT '23: Proceedings of the 13th International Conference on Advances in Information TechnologyDecember 2023Article No.: 10Pages 1–7https://doi.org/10.1145/3628454.3628497Published:06 December 2023Publication History 0citation9DownloadsMetricsTotal Citations0Total Downloads9Last 12 Months9Last 6 weeks5 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
A Modified Snake Optimizer Algorithm with Otsu-based Method for Satellite Image Segmentation

Image segmentation is an important step in image analysis that aims to segment regions of interest in an image by assigning a label to individual pixels sharing certain characteristics. Otsu-based method is a well-known thresholding technique that selects a threshold to segment regions by maximizing the variance between classes. Despite its advantages of considerable effectiveness and stability, its major drawback is high computational cost. This paper proposes a Modified Snake Optimizer algorithm (MSO), which can dynamically and efficiently tune Snake Optimizer (SO) parameters. To address the aforementioned drawback, MSO is applied with the Otsu threshold method (MSO-Otsu) in segmenting satellite images which helps analyze the snow-covered areas of mountain ranges in China. The experimental results show that the proposed MSO, in general, outperformed the traditional SO when applying to benchmark functions, and the proposed MSO-Otsu outperforms the traditional Otsu-based method in segmentation results and convergence time.
O2WB: A tool enabling ontology reuse in Wikibase

The Semantic Web initiative has established standards and practices for publishing interconnected knowledge, where RDF Schema and OWL shall enable the reuse of ontologies as one of these established practices. However, Wikibase, the software behind Wikidata, which is increasingly gaining popularity among data publishers, lacks the functionality to import and reuse existing RDF Schema and OWL ontologies. To facilitate ontology reuse, FAIR data publishing and encourage a tighter connection of existing Linked Data resources with Wikibase instances, we align the Wikibase data model with RDF and present O2WB, a tool for ontology import and export within Wikibase.
A Neuro-Symbolic Approach for Anomaly Detection and Complex Fault Diagnosis Exemplified in the Automotive Domain

This paper presents an iterative, hybrid neuro-symbolic approach for anomaly detection and complex fault diagnosis, enabling knowledge-based (symbolic) methods to complement (neural) machine learning methods and vice versa. We demonstrate an instantiation of this novel diagnosis system with applicability in a practically relevant real-world context, specifically the automotive domain. Explainability is indispensable for diagnosis and arises naturally in the system through the specific interplay of neural and symbolic methods. The presented architecture can be considered as a blueprint which is generally transferable to various diagnostic problems and domains.
Procedural Text Mining with Large Language Models

Recent advancements in the field of Natural Language Processing, particularly the development of large-scale language models that are pretrained on vast amounts of knowledge, are creating novel opportunities within the realm of Knowledge Engineering. In this paper, we investigate the usage of large language models (LLMs) in both zero-shot and in-context learning settings to tackle the problem of extracting procedures from unstructured PDF text in an incremental question-answering fashion. In particular, we leverage the current state-of-the-art GPT-4 (Generative Pre-trained Transformer 4) model, accompanied by two variations of in-context learning that involve an ontology with definitions of procedures and steps and a limited number of samples of few-shot learning. The findings highlight both the promise of this approach and the value of the in-context learning customisations. These modifications have the potential to significantly address the challenge of obtaining sufficient training data, a hurdle often encountered in deep learning-based Natural Language Processing techniques for procedure extraction.