ABSTRACT Flood disasters remain one of the most recurring natural phenomena worldwide, resulting from excessive water flow submerging land for an extended period of time. The escalating occurrences of floods, particularly in urban areas, can be attributed to climate change, extreme weather patterns, uncontrolled urbanization, and complex geographical conditions. To mitigate the destructive impacts, such as loss of life and economic ramifications, automatic flood analysis and remote‐sensing imagery segmentation offer valuable decision‐making insights. However, the segmentation process for flood detection faces challenges due to the scarcity of labelled data and diverse resolutions, including medium resolution data. In response, the authors propose Flood‐MATE, a novel semi‐supervised learning approach based on the mean‐teacher model. Our approach leverages the deep learning architecture and introduces a new loss function scenario for training. The dataset utilized in this study comprises SAR images of Sentinel‐1 C‐band that have undergone thorough processing. Promisingly, the results demonstrate a 4% improvement in the IoU metric compared to the baseline method employing pseudo‐labelling.

Finding relevant tables among databases, lakes, and repositories is the first step in extracting value from data. Such a task remains difficult because assessing whether a table is relevant to a problem does not always depend only on its content but also on the context, which is usually tribal knowledge known to the individual or team. While tools like data catalogs and academic data discovery systems target this problem, they rely on keyword search or more complex interfaces, limiting non-technical users' ability to find relevant data. The advent of large language models (LLMs) offers a unique opportunity for users to ask questions directly in natural language, making dataset discovery more intuitive, accessible, and efficient. In this paper, we introduce Pneuma , a retrieval-augmented generation (RAG) system designed to efficiently and effectively discover tabular data. Pneuma leverages large language models (LLMs) for both table representation and table retrieval. For table representation, Pneuma preserves schema and row-level information to ensure comprehensive data understanding. For table retrieval, Pneuma augments LLMs with traditional information retrieval techniques, such as full-text and vector search, harnessing the strengths of both to improve retrieval performance. To evaluate Pneuma , we generate comprehensive benchmarks that simulate table discovery workload on six real-world datasets including enterprise data, scientific databases, warehousing data, and open data. Our results demonstrate that Pneuma outperforms widely used table search systems (such as full-text search and state-of-the-art RAG systems) in accuracy and resource efficiency.

Completeness is a crucial data quality aspect that deals with the question: do we have all the data we need? The lack of awareness on the completeness state of a knowledge graph (KG) may result in bias or even falsity for any decisions made based on the KG. Given a KG, one may be wondering how its completeness may vary across different topics. In this paper, we present ProWD, a framework and tool for profiling the completeness of Wikidata, a central KG on the (Semantic) Web that is open and free to use. ProWD measures the degree of completeness based on the Class-Facet-Attribute (CFA) profiles. A class denotes a collection of entities, which can be of multiple facets, allowing attribute completeness to be analyzed and compared, e.g., how does the completeness of the attribute "educated at" and "date of birth" compare between male, German computer scientists, and female, Indonesian computer scientists? ProWD generates summaries and visualizations for such analysis, giving insights into the KG completeness. ProWD is available online at~\urlhttp://prowd.id.

Academic evaluation is an important activity regularly conducted by every higher education institution to gauge the performance of education services and help the administrators of the institution improve the quality of those services. Implementation of academic evaluation almost always requires an integration of relevant data, which are often scattered in separate systems. Such an integration effort is often tedious and time consuming because the data must be gathered from different systems, manually integrated, and then presented in a format that meets some pre-determined academic evaluation criteria. One way to help reducing the amount of effort expended in the integration is to make the data available in a format that enables linking between any part of the data and allows various academic evaluation queries to be performed on them. Our work intends to realise this idea by using Semantic Web technologies, in particular ontology and linked data. In particular, our work is situated in a concrete use case based on the academic evaluation process as periodically conducted in Universitas Indonesia (UI). In this paper, we focus on the first step of this effort, namely the development of an ontology for academic evaluation with a particular emphasis on evaluation of undergraduate degree programs in UI. There has been a number of prior work focusing on the development of an ontology for a similar problem, yet none of them takes into account the fact that academic data evolve over time. Our ontology thus accounts for this aspect according to the UIs academic evaluation criteria. We also demonstrate that the ontology can be used as a basis in answering SPARQL queries that capture those criteria, which indicates the suitability and usability of the ontology.

Today's scientific investigations are producing large numbers of scholarly products. These products continue to increase in diversity and complexity as researchers recognize that scholarly achievements are not only published articles but also datasets, software, and associated supporting materials. OceanLink is an online platform that addresses scholarly discovery and collaboration in the ocean sciences. The OceanLink project leverages Semantic Web technologies, web mining, and crowdsourcing to identify links between data centers, digital repositories, and professional societies to enhance discovery, enable collaboration, and begin to assess research contribution.

We propose a description-logic style extension of OWL 2 with nominal schemas which can be used like variable nominal classes within axioms. This feature allows ontology languages to express arbitrary DL-safe rules (as expressible in SWRL or RIF) in their native syntax. We show that adding nominal schemas to OWL 2 does not increase the worst-case reasoning complexity, and we identify a novel tractable language SROELV3(∩, x) that is versatile enough to capture the lightweight languages OWL EL and OWL RL.