The global halal market is growing, driven by rising stakeholder populations and increasing consumer interest in ethical and sustainable food choices. This surge in demand necessitates robust halal compliance throughout complex supply chains. However, there are several challenges, including fragmented information, increased understanding of halal requirements among stakeholders, and difficulties in tracing product provenance. This paper proposes a holistic framework for halal certification and compliance, addressing these challenges through the integration of artificial intelligence (AI) and blockchain technologies. AI can automate halal compliance checks, identify potential irregularities in sourcing and composition, and facilitate risk management. The blockchain offers an ideal platform for tracking product provenance throughout the halal supply chain. This ensures trust and confidence among consumers by providing verifiable information on ingredient origin and production processes. This paper further strengthens the potential of this framework by presenting an illustrative example that utilises knowledge graphs, machine learning, and smart contracts. This exemplifies the potential application of the proposed framework in the context of halal pre-certification processes. By fostering transparency and streamlining compliance procedures, the proposed holistic framework, empowered by AI and the blockchain, can significantly enhance trust and confidence among stakeholders within the halal food industry.

Around the globe, Geographic Information Systems (GISs) are well established in the daily workflow of authorities, businesses and non-profit organisations. GIS can effectively handle spatial entities and offer sophisticated analysis and modelling functions to deal with space. Only a small fraction of the literature in Geographic Information Science—or GIScience in short—has advanced the development of place, addressing entities with an ambiguous boundary and relying more on the human or social attributes of a location rather than on crisp geographic boundaries. While the GIScience developments support the establishment of the digital humanities, GISs were never designed to handle subjective or vague data. We, an international group of authors, juxtapose place and space in English language and in several other languages and discuss potential consequences for Geoinformatics and GIScience. In particular, we address the question of whether linguistic and cultural settings play a role in the perception of place. We report on some facts revealed by this multi-language and multi-cultural dialogue, and what particular aspects of place we were able to discern regarding the few languages addressed.

A long-standing question in GIScience is whether geographic information systems (GIS) facilitates an adequate quantifiable representation of the concept of place. Considering the difficulties of quantifying elusive concepts related to place, several researchers focus on more tangible dimensions of the human understanding of place. The most common approaches are semantic enrichment of spatial information and holistic conceptualization of the notion of place. However, these approaches give emphasis on either space or human meaning, or they mainly exist as concepts without practically proven usable artifacts. A partial answer to this problem was proposed by the function-based model that treats place as functional space. This paper focuses primarily on the level of composition, describing and formalizing it as a rule-based framework with the following objectives: (a) contribute to the formalization efforts of the notion of place and its integration within GIS and (b) maintain tangible properties intertwined with the human understanding of place. The operationalization potential of the proposed framework is illustrated with an example of identifying the shopping areas in an urban region. The results show that the proposed model is able to capture all shopping malls as well as other areas that are not explicitly labeled as such but still function similarly to a shopping mall.

The problem of discovering regions that support particular functionalities in an urban setting has been approached in literature using two general methodologies: top-down, encoding expert knowledge on urban planning and design and discovering regions that conform to that knowledge; and bottom-up, using data to train machine learning models, which can discover similar regions. Both methodologies face limitations, with knowledge-based approaches being criticized for scalability and transferability issues and data-driven approaches for lacking interpretability and depending heavily on data quality. To mitigate these disadvantages, we propose a novel framework that fuses a knowledge-based approach using design patterns and a data-driven approach using latent Dirichlet allocation (LDA) topic modeling in three different ways: Functional regions discovered using either approach are evaluated against each other to identify cases of significant agreement or disagreement; knowledge from patterns is used to adjust topic probabilities in the learning model; and topic probabilities are used to adjust pattern-based results. The proposed methodologies are demonstrated through the use case of identifying shopping-related regions in the Los Angeles metropolitan area. Results show that the combination of pattern-based discovery and topic modeling extraction helps uncover discrepancies between the two approaches and smooth inaccuracies caused by the limitations of each approach.

Purpose: Attention Deficit Hyperactivity Disorder (ADHD) is a widespread condition that affects human behaviour and can interfere with daily activities and relationships. Medication or medical information about ADHD can be found in several data sources on the Web. Such distribution of knowledge raises notable obstacles since researchers and clinicians must manually combine various sources to deeply explore aspects of ADHD. Knowledge graphs have been widely used in medical applications due to their data integration capabilities, offering rich data stores of information built from heterogeneous sources; however, general purpose knowledge graphs cannot represent knowledge in sufficient detail, thus there is an increasing interest in domain-specific knowledge graphs. Methods: In this work we propose a Knowledge Graph of ADHD. In particular, we introduce an automated procedure enabling the construction of a knowledge graph that covers knowledge from a wide range of data sources primarily focusing on adult ADHD. These include relevant literature and clinical trials, prescribed medication and their known side-effects. Data integration between these data sources is accomplished by employing a suite of information linking procedures, which aim to connect resources by relating them to common concepts found in medical thesauri. Results: The usability and appropriateness of the developed knowledge graph is evaluated through a series of use cases that illustrate its ability to enhance and accelerate information retrieval. Conclusion: The Knowledge Graph of ADHD can provide valuable assistance to researchers and clinicians in the research, training, diagnostic and treatment processes for ADHD.

Searching for places rather than traditional keyword-based search represents significant challenges. The most prevalent method of addressing place-related queries is based on place names but has limited potential due to the vagueness of natural language and its tendency to lead to ambiguous interpretations. In previous work we proposed a system-oriented logic-based formalization of place that goes beyond place names by introducing composition patterns of place which enable function-based search of space. In this study, we introduce flexibility into these patterns in terms of what is included when describing the spatial composition of a place using two distinct approaches, based on modal and probabilistic logic. Additionally, we propose a novel automated process of extracting these patterns relying on both theoretical and empirical knowledge, using statistical and spatial analysis and statistical relational learning. The proposed methodology is exemplified through the use case of locating all areas within London that support shopping-related functionality. Results show that the newly introduced patterns can identify more relevant areas, additionally offering a more fine-grained representation of the level of support of the required functionality.