Timely extraction of rescue-related data from social media is vital for emergency response, with event extraction and geolocation playing a key role. This paper presents a demo system that leverages Large Language Models (LLMs) and Knowledge Graphs (KGs) to identify rescue-related data from social media streams and integrate this information into a continuously updated KG, with a focus on the German city of Hamburg. Our approach utilizes an LLM to process unstructured social media text, accurately identifying events and relevant location references. LLMs in combination with in-context learning are applied for event extraction as well as geoparsing. The extracted and linked information is stored in a KG, which is both queryable for further analysis and supports downstream applications such as interactive map-based visualizations, providing real-time awareness for emergency services. Specifically, our geoparsing methods bridge the gap in the German setting, achieving state-of-the-art performance on the benchmark dataset MobIE.

Neuro-symbolic relation extraction lies at the intersection of neural networks and symbolic reasoning, presenting promising opportunities to enhance the capabilities of natural language processing (NLP) systems. Despite its potential, a comprehensive review of how these systems are developed and applied to the task of relation extraction has been lacking. This chapter addresses this gap by offering an in-depth overview of the current landscape in neuro-symbolic relation extraction, focusing on key methodologies and the datasets utilized in this field. We systematically categorize existing approaches, emphasizing how they integrate neural and symbolic components to tackle various challenges and the types of information they incorporate. Additionally, we review the datasets used to evaluate neuro-symbolic relation extraction systems, detailing their statistics, creation processes, and underlying domains. Furthermore, we discuss future research directions and challenges, such as the analysis of symbolic information and the integration of datasets with existing knowledge graphs. By synthesizing these findings, this chapter aims to provide researchers and practitioners with a clear understanding of the state of neuro-symbolic relation extraction and to inspire further innovations in this rapidly evolving field.

We present DBLP-QuAD 2.0, designed to evaluate Scholarly Knowledge Graph Question Answering (KGQA) over DBLP. Recent updates in the underlying DBLP KG, including new entities and relationships such as venues, research streams, and citation links, have necessitated a corresponding update to existing KG QA benchmarking resources. While the DBLP-QuAD dataset focused on author and publication-centered queries, DBLP-QuAD 2.0 broadens the coverage to reflect the enriched structure of the updated KG. Specifically, the questions in our dataset are formulated from SPARQL query logs that cover a wide range of entities involving authors, publications, venues, research streams, and citation relationships. DBLP-QuAD 2.0 thus provides a more comprehensive benchmark for evaluating KGQA systems with a baseline.

Despite the plethora of resources such as large-scale corpora and manually curated Knowledge Graphs (KGs), the ability to perform reasoning with natural language inputs over biomedical graphs remains challenging due to insufficient training data. We propose a novel method for automatically constructing a Biomedical Knowledge Graph Question Answering (BioKGQA) dataset sourced from PrimeKG, the largest precision medicine-oriented KG. In total, we create 85,368 question-answer pairs along with their respective SPARQL queries. Our approach generates a diverse array of contextually relevant questions covering a wide spectrum of biomedical concepts and levels of complexity. We evaluate our method based on automatic metrics alongside manual annotations. We establish novel standards tailored for KGQA systems to highlight the linguistic correctness and semantical faithfulness of the generated questions based on extracted KG facts. The compiled dataset - PrimeKGQA - serves as a valuable benchmarking resource for advancing knowledge-driven biomedical research and evaluating KGQA systems.

Geospatial knowledge has always been an essential driver for many societal aspects. This concerns in particular urban planning and urban growth management. To gain insights from geospatial data and guide decisions usually authoritative and open data sources are used, combined with user or citizen sensing data. However, we see a great potential for improving geospatial analytics by combining geospatial data with the rich terminological knowledge, e.g., provided by the Linked Open Data Cloud. Having semantically explicit, integrated geospatial and terminological knowledge, expressed by means of established vocabularies and ontologies, cross-domain spatial analytics can be performed. One analytics technique working on terminological knowledge is inductive concept learning, an approach that learns classifiers expressed as logical concept descriptions. In this paper, we extend inductive concept learning to infer and make use of the spatial context of entities in spatio-terminological data. We propose a formalism for extracting and making spatial relations explicit such that they can be exploited to learn spatial concept descriptions, enabling 'spatially aware' concept learning. We further provide an implementation of this formalism and demonstrate its capabilities in different evaluation scenarios.

We present QROWD, a project funded by the Horizon 2020 research programme, which aims at offering socio-technical solution to cross-sectorial Big Data integration in a European urban Smart Transportation context through a hybrid architecture for Big Data integration and analytics.

The following paper is an extended summary of the journal paper "DL-Learner A framework for inductive learning on the Semantic Web". In this system paper, we describe the DL-Learner framework. It is beneficial in various data and schema analytic tasks with applications in different standard machine learning scenarios, e.g. life sciences, as well as Semantic Web specific applications such as ontology learning and enrichment. Since its creation in 2007, it has become the main OWL and RDF-based software framework for supervised structured machine learning and includes several algorithm implementations, usage examples and has applications building on top of the framework.

The availability of structured data has increased significantly over the past decade and several approaches to learn from structured data have been proposed. These logic-based, inductive learning methods are often conceptually similar, which would allow a comparison among them even if they stem from different research communities. However, so far no efforts were made to define an environment for running learning tasks on a variety of tools, covering multiple knowledge representation languages. With SML-Bench, we propose a benchmarking framework to run inductive learning tools from the ILP and semantic web communities on a selection of learning problems. In this paper, we present the foundations of SML-Bench, discuss the systematic selection of benchmarking datasets and learning problems, and showcase an actual benchmark run on the currently supported tools.

The European General Data Protection Regulation (GDPR) sets new precedents for the processing of personal data. In this paper, we propose an architecture that provides an automated means to enable transparency with respect to personal data processing and sharing transactions and compliance checking with respect to data subject usage policies and GDPR legislative obligations.

Exploration and analysis of large amounts of machine generated data requires innovative approaches. We propose a combination of Semantic Web and Machine Learning to facilitate the analysis. First, data is collected and converted to RDF according to a schema in the Web Ontology Language OWL. Several components can continue working with the data, to interlink, label, augment, or classify. The size of the data poses new challenges to existing solutions, which we solve in this contribution by transitioning from in-memory to database.

We demonstrate the open-source Semantic Analytics Stack (SANSA), which can perform scalable analysis of large-scale knowledge graphs to facilitate applications such as link prediction, knowledge base completion and reasoning. The motivation behind this work lies in the lack of scalable methods for analytics which exploit expressive structures underlying semantically structured knowledge bases. The demonstration is based on the BigDataEurope technical platform, which utilizes Docker technology. We present various examples of using SANSA in the form of interactive Spark notebooks, which are executed with Apache Zeppelin. The technical platform and the notebooks are available on SANSA Github and can be deployed on any Docker-enabled host, locally or in a Docker Swarm cluster.

A major research challenge is to perform scalable analysis of large-scale knowledge graphs to facilitate applications like link prediction, knowledge base completion and reasoning. Analytics methods which exploit expressive structures usually do not scale well to very large knowledge bases, and most analytics approaches which do scale horizontally (i.e., can be executed in a distributed environment) work on simple feature-vector-based input. This software framework paper describes the ongoing Semantic Analytics Stack (SANSA) project, which supports expressive and scalable semantic analytics by providing functionality for distributed computing on RDF data.

In this system paper, we describe the DL-Learner framework, which supports supervised machine learning using OWL and RDF for background knowledge representation. It can be beneficial in various data and schema analysis tasks with applications in different standard machine learning scenarios, e.g. in the life sciences, as well as Semantic Web specific applications such as ontology learning and enrichment. Since its creation in 2007, it has become the main OWL and RDF-based software framework for supervised structured machine learning and includes several algorithm implementations, usage examples and has applications building on top of the framework. The article gives an overview of the framework with a focus on algorithms and use cases.

The world is losing some of its 7,000 languages. Hypothesizing that language attrition might subside if all languages were intertranslatable, the PanLex project supports panlingual lexical translation by integrating all known lexical translations. Semantic Web technologies can flexibly represent and reason with the content of its database and interlink it with linguistic and other resources and annotations. Conversely, PanLex, with its collection of translation links between more than a billion pairs of lexemes from more than 9,000 language varieties, can improve the coverage of the Linguistic Web of Data. We detail how we transformed the content of the PanLex database to RDF, established conformance with the lemon and GOLD data models, interlinked it with Lexvo and DBpedia, and published it as Linked Data and via SPARQL.

The combination of the advantages of widely used relational databases and semantic technologies has attracted signicant research over the past decade. In particular, mapping languages for the conversion of databases to RDF knowledge bases have been developed and standardized in the form of R2RML. In this article, we rst review those mapping languages and then devise work towards a unied formal model for them. Based on this, we present the Sparqlication Mapping Language (SML), which provides an intuitive way to declare mappings based on SQL VIEWS and SPARQL construct queries. We show that SML has the same expressivity as R2RML by enumerating the language features and show the correspondences, and we outline how one syntax can be converted into the other. A conducted user study for this paper juxtaposing SML and R2RML provides evidence that SML is a more compact syntax which is easier to understand and read and thus lowers the barrier to oer SPARQL access to relational databases.

Within the GeoKnow project, various tools are developed and integrated which aim to simplify managing geospatial Linked Data on the web. In this article, we summarise the state of the art and describe the status of open geospatial data on the web. We continue by presenting the Linked Data Stack as technical underpinning of GeoKnow and give a first presentation of the platform providing a light-weight integration of those tools.

. Within the GeoKnow project, various tools are developed and integrated which aim to simplify managing geospatial Linked Data on the web. In this article, we summarize the state of the art and describe the status of open geospatial data on the web. We continue by presenting the Linked Data Stack as technical underpinning of GeoKnow and give a first presentation of the platform providing a light-weight integration of those tools.