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Found 26 papers, 11 papers with code
Verification of Inconsistency-Aware Knowledge and Action Bases (Extended Version)
Description Logic Knowledge and Action Bases (KABs) have been recently introduced as a mechanism that provides a semantically rich representation of the information on the domain of interest in terms of a DL KB and a set of actions to change such information over time, possibly introducing new objects.
Controlled Query Evaluation for Datalog and OWL 2 Profile Ontologies
We study confidentiality enforcement in ontologies under the Controlled Query Evaluation framework, where a policy specifies the sensitive information and a censor ensures that query answers that may compromise the policy are not returned.
Towards Analytics Aware Ontology Based Access to Static and Streaming Data (Extended Version)
Real-time analytics that requires integration and aggregation of heterogeneous and distributed streaming and static data is a typical task in many industrial scenarios such as diagnostics of turbines in Siemens.
A Framework for Evaluating Snippet Generation for Dataset Search
Reusing existing datasets is of considerable significance to researchers and developers.
Towards More Usable Dataset Search: From Query Characterization to Snippet Generation
Reusing published datasets on the Web is of great interest to researchers and developers.
On Equivalence and Cores for Incomplete Databases in Open and Closed Worlds
However, for Closed Powerset semantics we show that one can find, for any incomplete database, a unique finite set of its subinstances which are subinstances (up to renaming of nulls) of all instances semantically equivalent to the original incomplete one.
On Expansion and Contraction of DL-Lite Knowledge Bases
Moreover, we show that well-known formula-based approaches are also not appropriate for DL-Lite expansion and contraction: they either have a high complexity of computation, or they produce logical theories that cannot be expressed in DL-Lite.
Enriching Documents with Compact, Representative, Relevant Knowledge Graphs
A prominent application of knowledge graph (KG) is document enrichment.
Neural Entity Summarization with Joint Encoding and Weak Supervision
In a large-scale knowledge graph (KG), an entity is often described by a large number of triple-structured facts.
Graph Random Neural Network for Semi-Supervised Learning on Graphs
We study the problem of semi-supervised learning on graphs, for which graph neural networks (GNNs) have been extensively explored.
Graph Random Neural Networks for Semi-Supervised Learning on Graphs
We study the problem of semi-supervised learning on graphs, for which graph neural networks (GNNs) have been extensively explored.
TDGIA:Effective Injection Attacks on Graph Neural Networks
In the GIA scenario, the adversary is not able to modify the existing link structure and node attributes of the input graph, instead the attack is performed by injecting adversarial nodes into it.
A Self-supervised Method for Entity Alignment
We present SelfKG by leveraging this discovery to design a contrastive learning strategy across two KGs.
Graph Contrastive Learning for Anomaly Detection
To achieve the contrastive objective, we design a graph neural network encoder that can infer and further remove suspicious links during message passing, as well as learn the global context of the input graph.
On Event-Driven Knowledge Graph Completion in Digital Factories
Smooth operation of such factories requires that the machines and engineering personnel that conduct their monitoring and diagnostics share a detailed common industrial knowledge about the factory, e. g., in the form of knowledge graphs.
SegTime: Precise Time Series Segmentation without Sliding Window
We propose a neural networks approach SegTime that finds precise breakpoints, obviates sliding windows, handles long-term dependencies, and it is insensitive to the label changing frequency.
Adaptive Diffusion in Graph Neural Networks
Notably, message passing based GNNs, e. g., graph convolutional networks, leverage the immediate neighbors of each node during the aggregation process, and recently, graph diffusion convolution (GDC) is proposed to expand the propagation neighborhood by leveraging generalized graph diffusion.
SelfKG: Self-Supervised Entity Alignment in Knowledge Graphs
We present SelfKG with efficient strategies to optimize this objective for aligning entities without label supervision.
GRAND+: Scalable Graph Random Neural Networks
In this work, we present a scalable and high-performance GNN framework GRAND+ for semi-supervised graph learning.
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Towards Ontology Reshaping for KG Generation with User-in-the-Loop: Applied to Bosch Welding
One important approach of KG generation is to map the raw data to a given KG schema, namely a domain ontology, and construct the entities and properties according to the ontology.
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.
GraphMAE2: A Decoding-Enhanced Masked Self-Supervised Graph Learner
Graph self-supervised learning (SSL), including contrastive and generative approaches, offers great potential to address the fundamental challenge of label scarcity in real-world graph data.
ExeKGLib: Knowledge Graphs-Empowered Machine Learning Analytics
Many machine learning (ML) libraries are accessible online for ML practitioners.
Literal-Aware Knowledge Graph Embedding for Welding Quality Monitoring: A Bosch Case
Recently there has been a series of studies in knowledge graph embedding (KGE), which attempts to learn the embeddings of the entities and relations as numerical vectors and mathematical mappings via machine learning (ML).
Scaling Data Science Solutions with Semantics and Machine Learning: Bosch Case
Industry 4. 0 and Internet of Things (IoT) technologies unlock unprecedented amount of data from factory production, posing big data challenges in volume and variety.
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.
