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Daniel Hernández 0002

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

4 papers

1 author row

NeSy Conference 2025 Conference Paper

ArgRAG: Explainable Retrieval Augmented Generation using Quantitative Bipolar Argumentation

Yuqicheng Zhu
Nico Potyka
Daniel Hernández 0002
Yuan He 0008
Zifeng Ding
Bo Xiong 0001
Dongzhuoran Zhou
Evgeny Kharlamov

Retrieval-Augmented Generation (RAG) enhances large language models by incorporating external knowledge, yet suffers from critical limitations in high-stakes domains—namely, sensitivity to noisy or contradictory evidence and opaque, stochastic decision-making. We propose \textsc{ArgRAG}, an explainable, and contestable alternative that replaces black-box reasoning with structured inference using a Quantitative Bipolar Argumentation Framework (QBAF). \textsc{ArgRAG} constructs a QBAF from retrieved documents and performs deterministic reasoning under gradual semantics. This allows faithfully explanaining and contesting decisions. Evaluated on two fact verification benchmarks, PubHealth and RAGuard, \textsc{ArgRAG} achieves strong accuracy while significantly improving transparency.

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ECAI Conference 2025 Conference Paper

Full-History Graphs with Edge-Type Decoupled Networks for Temporal Reasoning

Osama Mohammed
Jiaxin Pan 0003
Mojtaba Nayyeri
Daniel Hernández 0002
Steffen Staab

Modeling evolving interactions among entities is critical in many real-world tasks. For example, predicting driver maneuvers in traffic requires tracking how neighboring vehicles accelerate, brake, and change lanes relative to one another over consecutive frames. Similarly, detecting financial fraud hinges on following the flow of funds through successive transactions as they propagate across the network. Unlike classic time-series forecasting, these settings demand reasoning over who interacts with whom and when, calling for a temporal-graph representation that makes both the relations and their evolution explicit. Existing temporal-graph methods use snapshot graphs to represent temporal evolution. In this paper, we introduce a full-history graph that instantiates one node for every entity at every timestep and separates two edge sets: (i) intra-timestep edges that capture relations within a single frame, and (ii) inter-timestep edges that connect an entity to itself at consecutive steps. To learn on this graph we design an Edge-Type Decoupled Network (ETDNet) with parallel modules: a graph-attention module aggregates information along intra-timestep edges, a multi-head temporal-attention module attends over an entity’s inter-timestep history, and a fusion module combines the two messages after every layer. When evaluated on driver-intention prediction (Waymo) and Bitcoin fraud detection (Elliptic++), ETDNet consistently surpasses strong baselines, lifting Waymo joint accuracy to 75. 6 % (vs. 74. 1 %) and raising Elliptic++ illicit-class F1 to 88. 1 % (vs. 60. 4 %). These gains demonstrate the benefit of representing structural and temporal relations as distinct edges in a single graph.

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ICML Conference 2025 Conference Paper

Is Complex Query Answering Really Complex?

Cosimo Gregucci
Bo Xiong 0001
Daniel Hernández 0002
Lorenzo Loconte
Pasquale Minervini
Steffen Staab
Antonio Vergari

Complex query answering (CQA) on knowledge graphs (KGs) is gaining momentum as a challenging reasoning task. In this paper, we show that the current benchmarks for CQA might not be as complex as we think, as the way they are built distorts our perception of progress in this field. For example, we find that in these benchmarks most queries (up to 98% for some query types) can be reduced to simpler problems, e. g. , link prediction, where only one link needs to be predicted. The performance of state-of-the-art CQA models decreses significantly when such models are evaluated on queries that cannot be reduced to easier types. Thus, we propose a set of more challenging benchmarks composed of queries that require models to reason over multiple hops and better reflect the construction of real-world KGs. In a systematic empirical investigation, the new benchmarks show that current methods leave much to be desired from current CQA methods.

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ECAI Conference 2024 Conference Paper

Generating SROI - Ontologies via Knowledge Graph Query Embedding Learning

Yunjie He
Daniel Hernández 0002
Mojtaba Nayyeri
Bo Xiong 0001
Yuqicheng Zhu
Evgeny Kharlamov
Steffen Staab

Query embedding approaches answer complex logical queries over incomplete knowledge graphs (KGs) by computing and operating on low-dimensional vector representations of entities, relations, and queries. However, current query embedding models heavily rely on excessively parameterized neural networks and cannot explain the knowledge learned from the graph. We propose a novel query embedding method, AConE, which explains the knowledge learned from the graph in the form of SROI− description logic axioms while being more parameter-efficient than most existing approaches. AConE associates queries to SROI− description logic concepts. Every SROI− concept is embedded as a cone in complex vector space, and each SROI− relation is embedded as a transformation that rotates and scales cones. We show theoretically that AConE can learn SROI− axioms, and defines an algebra whose operations correspond one-to-one to SROI− description logic concept constructs. Our empirical study on multiple query datasets shows that AConE achieves superior results over previous baselines with fewer parameters. Notably on the WN18RR dataset, AConE achieves significant improvement over baseline models. We provide comprehensive analyses showing that the capability to represent axioms positively impacts the results of query answering.

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