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Found 45 papers, 21 papers with code
BERTMap: A BERT-based Ontology Alignment System
Ontology alignment (a. k. a ontology matching (OM)) plays a critical role in knowledge integration.
Contextual Semantic Embeddings for Ontology Subsumption Prediction
Automating ontology construction and curation is an important but challenging task in knowledge engineering and artificial intelligence.
Machine Learning-Friendly Biomedical Datasets for Equivalence and Subsumption Ontology Matching
Ontology Matching (OM) plays an important role in many domains such as bioinformatics and the Semantic Web, and its research is becoming increasingly popular, especially with the application of machine learning (ML) techniques.
Language Model Analysis for Ontology Subsumption Inference
Investigating whether pre-trained language models (LMs) can function as knowledge bases (KBs) has raised wide research interests recently.
DeepOnto: A Python Package for Ontology Engineering with Deep Learning
Integrating deep learning techniques, particularly language models (LMs), with knowledge representation techniques like ontologies has raised widespread attention, urging the need of a platform that supports both paradigms.
OWL2Vec*: Embedding of OWL Ontologies
Semantic embedding of knowledge graphs has been widely studied and used for prediction and statistical analysis tasks across various domains such as Natural Language Processing and the Semantic Web.
ColNet: Embedding the Semantics of Web Tables for Column Type Prediction
Automatically annotating column types with knowledge base (KB) concepts is a critical task to gain a basic understanding of web tables.
Ranked #1 on
Column Type Annotation
on T2Dv2
(F1 (%) metric)
Learning Semantic Annotations for Tabular Data
The usefulness of tabular data such as web tables critically depends on understanding their semantics.
Ranked #1 on
Column Type Annotation
on T2Dv2
Canonicalizing Knowledge Base Literals
Ontology-based knowledge bases (KBs) like DBpedia are very valuable resources, but their usefulness and usability is limited by various quality issues.
Knowledge-aware Zero-Shot Learning: Survey and Perspective
Zero-shot learning (ZSL) which aims at predicting classes that have never appeared during the training using external knowledge (a. k. a.
Correcting Knowledge Base Assertions
The usefulness and usability of knowledge bases (KBs) is often limited by quality issues.
Extending Consequence-Based Reasoning to SRIQ
Consequence-based calculi are a family of reasoning algorithms for description logics (DLs), and they combine hypertableau and resolution in a way that often achieves excellent performance in practice.
Language Models as Hierarchy Encoders
Interpreting hierarchical structures latent in language is a key limitation of current language models (LMs).
Dual Box Embeddings for the Description Logic EL++
OWL ontologies, whose formal semantics are rooted in Description Logic (DL), have been widely used for knowledge representation.
Reveal the Unknown: Out-of-Knowledge-Base Mention Discovery with Entity Linking
We propose BLINKout, a new BERT-based Entity Linking (EL) method which can identify mentions that do not have corresponding KB entities by matching them to a special NIL entity.
Ontology Enrichment from Texts: A Biomedical Dataset for Concept Discovery and Placement
Mentions of new concepts appear regularly in texts and require automated approaches to harvest and place them into Knowledge Bases (KB), e. g., ontologies and taxonomies.
Exploring Large Language Models for Ontology Alignment
This work investigates the applicability of recent generative Large Language Models (LLMs), such as the GPT series and Flan-T5, to ontology alignment for identifying concept equivalence mappings across ontologies.
Knowledge-based Transfer Learning Explanation
Machine learning explanation can significantly boost machine learning's application in decision making, but the usability of current methods is limited in human-centric explanation, especially for transfer learning, an important machine learning branch that aims at utilizing knowledge from one learning domain (i. e., a pair of dataset and prediction task) to enhance prediction model training in another learning domain.
Revisiting Inferential Benchmarks for Knowledge Graph Completion
A key feature of Machine Learning approaches for KG completion is their ability to learn inference patterns, so that the predicted facts are the results of applying these patterns to the KG.
A Language Model based Framework for New Concept Placement in Ontologies
In all steps, we propose to leverage neural methods, where we apply embedding-based methods and contrastive learning with Pre-trained Language Models (PLMs) such as BERT for edge search, and adapt a BERT fine-tuning-based multi-label Edge-Cross-encoder, and Large Language Models (LLMs) such as GPT series, FLAN-T5, and Llama 2, for edge selection.
Consequence-based Reasoning for Description Logics with Disjunction, Inverse Roles, Number Restrictions, and Nominals
We present a consequence-based calculus for concept subsumption and classification in the description logic ALCHOIQ, which extends ALC with role hierarchies, inverse roles, number restrictions, and nominals.
Stratified Negation in Limit Datalog Programs
There has recently been an increasing interest in declarative data analysis, where analytic tasks are specified using a logical language, and their implementation and optimisation are delegated to a general-purpose query engine.
Optimised Maintenance of Datalog Materialisations
The Delete/Rederive (DRed) and the Backward/Forward (B/F) algorithms solve this problem for general datalog, but both contain steps that evaluate rules 'backwards' by matching their heads to a fact and evaluating the partially instantiated rule bodies as queries.
Foundations of Declarative Data Analysis Using Limit Datalog Programs
Motivated by applications in declarative data analysis, we study $\mathit{Datalog}_{\mathbb{Z}}$---an extension of positive Datalog with arithmetic functions over integers.
Stream Reasoning in Temporal Datalog
In recent years, there has been an increasing interest in extending traditional stream processing engines with logical, rule-based, reasoning capabilities.
The Bag Semantics of Ontology-Based Data Access
The ontology is linked to the sources using mappings, which assign views over the data to ontology predicates.
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.
Ontology Module Extraction via Datalog Reasoning
Module extraction - the task of computing a (preferably small) fragment M of an ontology T that preserves entailments over a signature S - has found many applications in recent years.
Handling owl:sameAs via Rewriting
Rewriting is widely used to optimise owl:sameAs reasoning in materialisation based OWL 2 RL systems.
Acyclicity Notions for Existential Rules and Their Application to Query Answering in Ontologies
Existential rules are closely related to the Horn fragments of the OWL 2 ontology language; furthermore, several prominent OWL 2 reasoners implement CQ answering by using the chase to materialise all relevant facts.
Completeness Guarantees for Incomplete Ontology Reasoners: Theory and Practice
Since ontologies and typical queries are often fixed at application design time, our approach allows application developers to check whether a reasoner known to be incomplete in general is actually complete for the kinds of input relevant for the application.
Hypertableau Reasoning for Description Logics
We present a novel reasoning calculus for the description logic SHOIQ^+---a knowledge representation formalism with applications in areas such as the Semantic Web.
A Description Logic Primer
This paper provides a self-contained first introduction to description logics (DLs).
Computing Datalog Rewritings beyond Horn Ontologies
Rewriting-based approaches for answering queries over an OWL 2 DL ontology have so far been developed mainly for Horn fragments of OWL 2 DL.
Introducing Nominals to the Combined Query Answering Approaches for EL
So-called combined approaches answer a conjunctive query over a description logic ontology in three steps: first, they materialise certain consequences of the ontology and the data; second, they evaluate the query over the data; and third, they filter the result of the second phase to eliminate unsound answers.
The Window Validity Problem in Rule-Based Stream Reasoning
Rule-based temporal query languages provide the expressive power and flexibility required to capture in a natural way complex analysis tasks over streaming data.
Modular Materialisation of Datalog Programs
The semina\"ive algorithm can materialise all consequences of arbitrary datalog rules, and it also forms the basis for incremental algorithms that update a materialisation as the input facts change.
Datalog Reasoning over Compressed RDF Knowledge Bases
Materialisation is often used in RDF systems as a preprocessing step to derive all facts implied by given RDF triples and rules.
Computing CQ lower-bounds over OWL 2 through approximation to RSA
We present a novel approximation of OWL 2 ontologies into RSA, and an algorithm to compute a closer (than PAGOdA) lower bound approximation using the RSA combined approach.
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.
INDIGO: GNN-Based Inductive Knowledge Graph Completion Using Pair-Wise Encoding
The aim of knowledge graph (KG) completion is to extend an incomplete KG with missing triples.
Zero-shot and Few-shot Learning with Knowledge Graphs: A Comprehensive Survey
Machine learning especially deep neural networks have achieved great success but many of them often rely on a number of labeled samples for supervision.
Cardinality-Minimal Explanations for Monotonic Neural Networks
In recent years, there has been increasing interest in explanation methods for neural model predictions that offer precise formal guarantees.
Enhancing Datalog Reasoning with Hypertree Decompositions
In this paper, we provide algorithms that exploit hypertree decompositions for the materialisation and incremental evaluation of Datalog programs.
Optimised Storage for Datalog Reasoning
Materialisation facilitates Datalog reasoning by precomputing all consequences of the facts and the rules so that queries can be directly answered over the materialised facts.
