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Found 53 papers, 24 papers with code
LLMs4Life: Large Language Models for Ontology Learning in Life Sciences
Our work evaluates the capabilities of LLMs in ontology learning in the context of highly specialized and complex domains such as life science domains.
F -- A Model of Events based on the Foundational Ontology DOLCE+DnS Ultralite
The lack of a formal model of events hinders interoperability in distributed event-based systems.
DAGE: DAG Query Answering via Relational Combinator with Logical Constraints
We show that it is possible to implement DAGE on top of existing query embedding methods, and we empirically measure the improvement of our method over the results of vanilla methods evaluated in tree-form queries that approximate the DAG queries of our proposed benchmark.
Visual Representation Learning Guided By Multi-modal Prior Knowledge
The respective embeddings are generated from the given modalities in a common latent space, i. e., visual embeddings from original and synthetic images as well as knowledge graph embeddings (KGEs).
Is Complex Query Answering Really Complex?
The performance of state-of-the-art CQA models drops significantly when such models are evaluated on queries that cannot be reduced to easier types.
LMT-Net: Lane Model Transformer Network for Automated HD Mapping from Sparse Vehicle Observations
We evaluate the performance of LMT-Net on an internal dataset that consists of multiple vehicle observations as well as human annotations as Ground Truth (GT).
Conformalized Answer Set Prediction for Knowledge Graph Embedding
Knowledge graph embeddings (KGE) apply machine learning methods on knowledge graphs (KGs) to provide non-classical reasoning capabilities based on similarities and analogies.
Predictive Multiplicity of Knowledge Graph Embeddings in Link Prediction
Our empirical study reveals significant predictive multiplicity in link prediction, with $8\%$ to $39\%$ testing queries exhibiting conflicting predictions.
eSPARQL: Representing and Reconciling Agnostic and Atheistic Beliefs in RDF-star Knowledge Graphs
Over the past few years, we have seen the emergence of large knowledge graphs combining information from multiple sources.
Approximating Probabilistic Inference in Statistical EL with Knowledge Graph Embeddings
Statistical information is ubiquitous but drawing valid conclusions from it is prohibitively hard.
Generating $SROI^-$ Ontologies via Knowledge Graph Query Embedding Learning
AConE associates queries to a $SROI^-$ description logic concept.
TempBEV: Improving Learned BEV Encoders with Combined Image and BEV Space Temporal Aggregation
These results indicate the overall effectiveness of our approach and make a strong case for aggregating temporal information in both image and BEV latent spaces.
Hybrid Reasoning Based on Large Language Models for Autonomous Car Driving
Large Language Models (LLMs) have garnered significant attention for their ability to understand text and images, generate human-like text, and perform complex reasoning tasks.
From Shapes to Shapes: Inferring SHACL Shapes for Results of SPARQL CONSTRUCT Queries (Extended Version)
However, it becomes challenging to understand what graph data can be expected at the end of a data processing pipeline without knowing the particular input data: Shape constraints on the input graph may affect the output graph, but may no longer apply literally, and new shapes may be imposed by the query template.
Robust Knowledge Extraction from Large Language Models using Social Choice Theory
Large-language models (LLMs) can support a wide range of applications like conversational agents, creative writing or general query answering.
HGE: Embedding Temporal Knowledge Graphs in a Product Space of Heterogeneous Geometric Subspaces
Temporal knowledge graphs may exhibit static temporal patterns at distinct points in time and dynamic temporal patterns between different timestamps.
NestE: Modeling Nested Relational Structures for Knowledge Graph Reasoning
NestE represents each atomic fact as a $1\times3$ matrix, and each nested relation is modeled as a $3\times3$ matrix that rotates the $1\times3$ atomic fact matrix through matrix multiplication.
Adversarial Reweighting Guided by Wasserstein Distance for Bias Mitigation
To balance the data distribution between the majority and the minority groups, our approach deemphasizes samples from the majority group.
Semantic Map Learning of Traffic Light to Lane Assignment based on Motion Data
We show the effectiveness of basic statistical approaches for this task by implementing and evaluating a pattern-based contribution method.
Probabilistic Regular Tree Priors for Scientific Symbolic Reasoning
Symbolic Regression (SR) allows for the discovery of scientific equations from data.
Shrinking Embeddings for Hyper-Relational Knowledge Graphs
Although some recent works have proposed to embed hyper-relational KGs, these methods fail to capture essential inference patterns of hyper-relational facts such as qualifier monotonicity, qualifier implication, and qualifier mutual exclusion, limiting their generalization capability.
Geometric Relational Embeddings: A Survey
Geometric relational embeddings map relational data as geometric objects that combine vector information suitable for machine learning and structured/relational information for structured/relational reasoning, typically in low dimensions.
Hierarchical Multi-label Classification
Knowledge Graph Completion
+2
HiPrompt: Few-Shot Biomedical Knowledge Fusion via Hierarchy-Oriented Prompting
Medical decision-making processes can be enhanced by comprehensive biomedical knowledge bases, which require fusing knowledge graphs constructed from different sources via a uniform index system.
Modeling Relational Patterns for Logical Query Answering over Knowledge Graphs
However, the role of such patterns in answering FOL queries by query embedding models has not been yet studied in the literature.
Beyond Demographic Parity: Redefining Equal Treatment
Liberalism-oriented political philosophy reasons that all individuals should be treated equally independently of their protected characteristics.
Explanation Shift: How Did the Distribution Shift Impact the Model?
Then, state-of-the-art techniques model input data distributions or model prediction distributions and try to understand issues regarding the interactions between learned models and shifting distributions.
Link Prediction with Attention Applied on Multiple Knowledge Graph Embedding Models
As a result, the combined model can learn relational and structural patterns.
SCENE: Reasoning about Traffic Scenes using Heterogeneous Graph Neural Networks
In this work we propose SCENE, a methodology to encode diverse traffic scenes in heterogeneous graphs and to reason about these graphs using a heterogeneous Graph Neural Network encoder and task-specific decoders.
Ranked #1 on
Node Classification
on BGS
Robust Recurrent Neural Network to Identify Ship Motion in Open Water with Performance Guarantees -- Technical Report
In this work, we represent a recurrent neural network as a linear time-invariant system with nonlinear disturbances.
Predicting Eye Gaze Location on Websites
This is done by the curation of a unified dataset that consists of website screenshots, eye-gaze heatmap and website's layout information in the form of image and text masks.
Explanation Shift: Detecting distribution shifts on tabular data via the explanation space
We provide a mathematical analysis of different types of distribution shifts as well as synthetic experimental examples.
Integrating Knowledge Graph embedding and pretrained Language Models in Hypercomplex Spaces
In our approach, we build on existing strong representations of single modalities and we use hypercomplex algebra to represent both, (i), single-modality embedding as well as, (ii), the interaction between different modalities and their complementary means of knowledge representation.
Ultrahyperbolic Knowledge Graph Embeddings
Recent knowledge graph (KG) embeddings have been advanced by hyperbolic geometry due to its superior capability for representing hierarchies.
Detecting Anomalies within Time Series using Local Neural Transformations
We develop a new method to detect anomalies within time series, which is essential in many application domains, reaching from self-driving cars, finance, and marketing to medical diagnosis and epidemiology.
Fairness Implications of Encoding Protected Categorical Attributes
We investigate the interaction between categorical encodings and target encoding regularization methods that reduce unfairness.
Faithiful Embeddings for EL++ Knowledge Bases
Namely, the learned model of BoxEL embedding with loss 0 is a (logical) model of the KB.
Wikidated 1.0: An Evolving Knowledge Graph Dataset of Wikidata's Revision History
Wikidata is the largest general-interest knowledge base that is openly available.
ProGS: Property Graph Shapes Language (Extended Version)
In RDF representations, this error can be addressed by shape languages such as SHACL or ShEx, which allow for checking whether graphs are valid with respect to a set of domain constraints.
Learning Gradual Argumentation Frameworks using Genetic Algorithms
As a first proof of concept, we propose a genetic algorithm to simultaneously learn the structure of argumentative classification models.
Pseudo-Riemannian Graph Convolutional Networks
Empirical results demonstrate that our method outperforms Riemannian counterparts when embedding graphs of complex topologies.
Hybrid Physics and Deep Learning Model for Interpretable Vehicle State Prediction
Physical motion models offer interpretable predictions for the motion of vehicles.
LaHAR: Latent Human Activity Recognition using LDA
Processing sequential multi-sensor data becomes important in many tasks due to the dramatic increase in the availability of sensors that can acquire sequential data over time.
MOFA: Modular Factorial Design for Hyperparameter Optimization
MOFA pursues several rounds of HPO, where each round alternates between exploration of hyperparameter space by factorial design and exploitation of evaluation results by factorial analysis.
GeoSPARQL+: Syntax, Semantics and System for Integrated Querying of Graph, Raster and Vector Data -- Technical Report
We introduce an approach to semantically represent and query raster data in a Semantic Web graph.
Is Language Modeling Enough? Evaluating Effective Embedding Combinations
We present PubMedSection, a novel topic classification dataset focussed on the biomedical domain.
Knowledge Graphs
In this paper we provide a comprehensive introduction to knowledge graphs, which have recently garnered significant attention from both industry and academia in scenarios that require exploiting diverse, dynamic, large-scale collections of data.
Understanding Social Networks using Transfer Learning
A detailed understanding of users contributes to the understanding of the Web's evolution, and to the development of Web applications.
CLEARumor at SemEval-2019 Task 7: ConvoLving ELMo Against Rumors
The goal of subtask B is to predict the veracity of a given rumor.
Learning Taxonomies of Concepts and not Words using Contextualized Word Representations: A Position Paper
Taxonomies are semantic hierarchies of concepts.
Predicting User Roles in Social Networks using Transfer Learning with Feature Transformation
How can we recognise social roles of people, given a completely unlabelled social network?
Observing and Recommending from a Social Web with Biases
The research question this report addresses is: how, and to what extent, those directly involved with the design, development and employment of a specific black box algorithm can be certain that it is not unlawfully discriminating (directly and/or indirectly) against particular persons with protected characteristics (e. g. gender, race and ethnicity)?
A Generalized Language Model as the Combination of Skipped n-grams and Modified Kneser-Ney Smoothing
In an extensive empirical experiment over English text corpora we demonstrate that our generalized language models lead to a substantial reduction of perplexity between 3. 1% and 12. 7% in comparison to traditional language models using modified Kneser-Ney smoothing.
