Knowledge graph matching is a critical task in data integration that consists of ontology matching and instance matching, i.e., finding correspondences between classes, properties, and instances from different knowledge graphs. Existing approaches for this task often rely on lexical similarities between instance attributes and separate ontology matching from instance matching, leading to suboptimal results. To overcome these limitations, we propose TIM - a tiered iterative knowledge graph matching architecture that leverages the inherent connectedness of knowledge graphs to iteratively find new correspondences. The tiered iterative structure enables the preferred use of high-precision matchers, with recall-oriented matchers running only after the more precise matchers cannot find any new correspondences. We evaluate our approach against several state-of-the-art approaches on multiple real-world datasets. We achieve the best accuracy for class and property matching by incorporating instance information into their matching process and an above-average accuracy for instance matching. Additionally, due to the efficient architecture of our approach, we achieve the fastest runtime for non-trivial baseline systems.
Artificial Intelligence (AI) methods based on Knowledge Graphs (KGs) have recently been proposed to generate explanations for scientific discovery tasks. Although these approaches offer a promising foundation for accelerating research and development, their practical adoption depends on a clearer understanding of which factors influence the usefulness and trustworthiness of the explanations they generate. We conducted an in-depth user study with eleven biomedical researchers on KG-based explanations for two key scientific discovery tasks in drug development: drug repurposing and drug–target interaction. We evaluated the relevance, completeness, and validity of path-based explanations extracted from KGs by state-of-the-art methods, and compared them along two dimensions: whether they produce single vs. multiple explanatory paths, and whether they incorporate ontological information. The evaluation combined participants’ ratings with a qualitative analysis of feedback to understand how experts interpret these explanations. Our results revealed that biomedical researchers prefer explanations that integrate ontological information and present diverse, biologically plausible mechanisms over those based solely on structural connectivity. These findings shed light on key design choices for KG-based AI systems that aim to support informed candidate selection in drug development and, more broadly, improve their impact in scientific applications.
Knowledge graphs (KGs) represent heterogeneous data as triples, including relational triples between entities and attribute triples linking entities to literal values like labels or numbers. Integrating multiple KGs is an essential task for maximizing data utility in downstream tasks. However, the integration process is challenging due to common issues such as noise, incompleteness, and varying coverages, which complicate accurate entity linking and can lead to misclassifications. As the size and the number of available datasets continues to grow, automated approaches to KG integration are indispensable for scalability. Existing methods often rely on KG embedding techniques and struggle when confronted with noisy or contradictory literal data. In this paper, we present Conel, a novel approach for unsupervised link discovery that leverages literal similarities and employs contrastive and entropy based scoring for robust entity matching. Our experiments show that Conel outperforms all baselines on 5 out of 7 datasets exhibiting robustness to the adverse conditions typical in real-world KG linking.
Question and feedback generation from ontologies has appeal for the high-quality content it can provide, and to alleviate the test setting and marking burden of teachers and quiz question setters. Existing approaches lack a formal and expressive technique, however, which limits their ability to represent and process complex and semantically rich questions, answers, and explanatory feedback patterns. They typically require post-processing to eliminate irrelevant outputs, rely on entity labels rather than represented semantics, and they often lack modularity, reducing scalability and reusability. We solve this by focusing on the content determination stage in the natural language generation pipeline, and any realiser of choice to be used afterwards. We propose a framework consisting of an OWL 2 DL-based model combined with an optimised determination algorithm to efficiently fetch relevant content from the ontology. The framework was implemented and evaluated on content selection performance with several ontologies.
Knowledge Graphs (KGs) are essential for organizing and retrieving knowledge from large text collections, yet constructing high-quality KGs from text remains a difficult task. An important challenge in constructing KGs is evaluation: how can we measure the quality of the graph under construction? Traditional evaluation relies on expensive, manually curated gold-standard graphs whose creation demands significant expert effort and is rarely available for every domain. To alleviate this cost, we need unsupervised metrics that compare generated graphs directly with the source texts. Unfortunately, most proposals in this direction require training or fine-tuning machine learning models, introducing additional costs that limit their applicability. To address this gap, we develop a fully unsupervised metric that compares graphs directly with the source texts. Our proposal, called Bidirectional Graph Similarity (BIGS), transforms each triple into natural language sentences and measures semantic alignment with original document sentences using embedding similarity. We evaluate BIGS on several document collections and standard benchmarks for supervised evaluation, finding strong correlations with supervised metrics, and that it remains robust across different text segmentation and verbalization strategies. Our results show that BIGS effectively captures the coverage of generated graphs without any manual annotations or additional training, offering a practical tool for KG evaluation.
