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Found 83 papers, 46 papers with code
Bringing Light Into the Dark: A Large-scale Evaluation of Knowledge Graph Embedding Models Under a Unified Framework
The heterogeneity in recently published knowledge graph embedding models' implementations, training, and evaluation has made fair and thorough comparisons difficult.
PyKEEN 1.0: A Python Library for Training and Evaluating Knowledge Graph Embeddings
Recently, knowledge graph embeddings (KGEs) received significant attention, and several software libraries have been developed for training and evaluating KGEs.
Ranked #1 on
Link Prediction
on WN18
(training time (s) metric)
EARL: Joint Entity and Relation Linking for Question Answering over Knowledge Graphs
Many question answering systems over knowledge graphs rely on entity and relation linking components in order to connect the natural language input to the underlying knowledge graph.
Using Multi-Label Classification for Improved Question Answering
In particular, we develop a multi-label classification-based metasystem for question answering over 6 existing systems using an innovative set of 14 question features.
No One is Perfect: Analysing the Performance of Question Answering Components over the DBpedia Knowledge Graph
Question answering (QA) over knowledge graphs has gained significant momentum over the past five years due to the increasing availability of large knowledge graphs and the rising importance of question answering for user interaction.
Message Passing for Hyper-Relational Knowledge Graphs
We also demonstrate that existing benchmarks for evaluating link prediction (LP) performance on hyper-relational KGs suffer from fundamental flaws and thus develop a new Wikidata-based dataset - WD50K.
Ranked #2 on
Link Prediction
on JF17K
Temporal Knowledge Graph Embedding Model based on Additive Time Series Decomposition
Moreover, considering the temporal uncertainty during the evolution of entity/relation representations over time, we map the representations of temporal KGs into the space of multi-dimensional Gaussian distributions.
TeRo: A Time-aware Knowledge Graph Embedding via Temporal Rotation
We show our proposed model overcomes the limitations of the existing KG embedding models and TKG embedding models and has the ability of learning and inferringvarious relation patterns over time.
Improving Response Selection in Multi-Turn Dialogue Systems by Incorporating Domain Knowledge
Building systems that can communicate with humans is a core problem in Artificial Intelligence.
How Complex is your classification problem? A survey on measuring classification complexity
This paper surveys and analyzes measures which can be extracted from the training datasets in order to characterize the complexity of the respective classification problems.
Learning to Rank Query Graphs for Complex Question Answering over Knowledge Graphs
In this paper, we conduct an empirical investigation of neural query graph ranking approaches for the task of complex question answering over knowledge graphs.
Incorporating Literals into Knowledge Graph Embeddings
Most of the existing work on embedding (or latent feature) based knowledge graph analysis focuses mainly on the relations between entities.
CHOLAN: A Modular Approach for Neural Entity Linking on Wikipedia and Wikidata
Our empirical study was conducted on two well-known knowledge bases (i. e., Wikidata and Wikipedia).
Ranked #1 on
Entity Linking
on MSNBC
Conversational Question Answering over Knowledge Graphs with Transformer and Graph Attention Networks
For this task, we propose LASAGNE (muLti-task semAntic parSing with trAnsformer and Graph atteNtion nEtworks).
Old is Gold: Linguistic Driven Approach for Entity and Relation Linking of Short Text
Short texts challenge NLP tasks such as named entity recognition, disambiguation, linking and relation inference because they do not provide sufficient context or are partially malformed (e. g. wrt.
Evaluating the Impact of Knowledge Graph Context on Entity Disambiguation Models
We further hypothesize that our proposed KG context can be standardized for Wikipedia, and we evaluate the impact of KG context on state-of-the-art NED model for the Wikipedia knowledge base.
Ranked #2 on
Entity Disambiguation
on AIDA-CoNLL
Temporal Knowledge Graph Completion using a Linear Temporal Regularizer and Multivector Embeddings
Representation learning approaches for knowledge graphs have been mostly designed for static data.
Using a KG-Copy Network for Non-Goal Oriented Dialogues
Non-goal oriented, generative dialogue systems lack the ability to generate answers with grounded facts.
DialoKG: Knowledge-Structure Aware Task-Oriented Dialogue Generation
Task-oriented dialogue generation is challenging since the underlying knowledge is often dynamic and effectively incorporating knowledge into the learning process is hard.
Open-domain Event Extraction and Embedding for Natural Gas Market Prediction
We propose an approach to predict the natural gas price in several days using historical price data and events extracted from news headlines.
Belittling the Source: Trustworthiness Indicators to Obfuscate Fake News on the Web
To this aim, an important step to detect fake-news is to have access to a credibility score for a given information source.
SeMantic AnsweR Type prediction task (SMART) at ISWC 2020 Semantic Web Challenge
Each year the International Semantic Web Conference accepts a set of Semantic Web Challenges to establish competitions that will advance the state of the art solutions in any given problem domain.
Context Transformer with Stacked Pointer Networks for Conversational Question Answering over Knowledge Graphs
In this work, we propose a novel framework named CARTON, which performs multi-task semantic parsing for handling the problem of conversational question answering over a large-scale knowledge graph.
Grounding Dialogue Systems via Knowledge Graph Aware Decoding with Pre-trained Transformers
Generating knowledge grounded responses in both goal and non-goal oriented dialogue systems is an important research challenge.
GeoWINE: Geolocation based Wiki, Image,News and Event Retrieval
The first module is a state-of-the-art model for geolocation estimation of images.
Improving Inductive Link Prediction Using Hyper-Relational Facts
In this work, we classify different inductive settings and study the benefits of employing hyper-relational KGs on a wide range of semi- and fully inductive link prediction tasks powered by recent advancements in graph neural networks.
Time-aware Graph Neural Network for Entity Alignment between Temporal Knowledge Graphs
Entity alignment aims to identify equivalent entity pairs between different knowledge graphs (KGs).
Ranked #2 on
Entity Alignment
on DICEWS-1K
Time-aware Graph Neural Networks for Entity Alignment between Temporal Knowledge Graphs
Entity alignment aims to identify equivalent entity pairs between different knowledge graphs (KGs).
ParaQA: A Question Answering Dataset with Paraphrase Responses for Single-Turn Conversation
This paper presents ParaQA, a question answering (QA) dataset with multiple paraphrased responses for single-turn conversation over knowledge graphs (KG).
VANiLLa : Verbalized Answers in Natural Language at Large Scale
We also present results of training our dataset on multiple baseline models adapted from current state-of-the-art Natural Language Generation (NLG) architectures.
RoMe: A Robust Metric for Evaluating Natural Language Generation
Secondly, it should consider the grammatical quality of the generated sentence.
Embedding Knowledge Graphs Attentive to Positional and Centrality Qualities
Capturing not only local graph structure but global features of entities are crucial for prediction tasks on Knowledge Graphs.
Ranked #2 on
Link Property Prediction
on ogbl-biokg
Space Efficient Context Encoding for Non-Task-Oriented Dialogue Generation with Graph Attention Transformer
To improve the coherence and knowledge retrieval capabilities of non-task-oriented dialogue systems, recent Transformer-based models aim to integrate fixed background context.
Contrastive Representation Learning for Conversational Question Answering over Knowledge Graphs
The majority of existing ConvQA methods rely on full supervision signals with a strict assumption of the availability of gold logical forms of queries to extract answers from the KG.
Distantly Supervised Question Parsing
The emergence of structured databases for Question Answering (QA) systems has led to developing methods, in which the problem of learning the correct answer efficiently is based on a linking task between the constituents of the question and the corresponding entries in the database.
MDE: Multiple Distance Embeddings for Link Prediction in Knowledge Graphs
We propose the Multiple Distance Embedding model (MDE) that addresses these limitations and a framework to collaboratively combine variant latent distance-based terms.
Ranked #7 on
Link Prediction
on FB15k
Incorporating Joint Embeddings into Goal-Oriented Dialogues with Multi-Task Learning
Since such models can greatly benefit from user intent and knowledge graph integration, in this paper we propose an RNN-based end-to-end encoder-decoder architecture which is trained with joint embeddings of the knowledge graph and the corpus as input.
Ranked #1 on
Goal-Oriented Dialog
on Kvret
VOGUE: Answer Verbalization through Multi-Task Learning
The VOGUE framework attempts to generate a verbalized answer using a hybrid approach through a multi-task learning paradigm.
Proxy Indicators for the Quality of Open-domain Dialogues
The automatic evaluation of open-domain dialogues remains a largely unsolved challenge.
Ranked #1 on
Dialogue Evaluation
on USR-PersonaChat
Survey on English Entity Linking on Wikidata
(3) How do current Entity Linking approaches exploit the specific characteristics of Wikidata?
Transformer with Tree-order Encoding for Neural Program Generation
While a considerable amount of semantic parsing approaches have employed RNN architectures for code generation tasks, there have been only few attempts to investigate the applicability of Transformers for this task.
Unveiling Relations in the Industry 4.0 Standards Landscape based on Knowledge Graph Embeddings
We use knowledge graph embeddings to automatically create these communities exploiting the meaning of the existing relationships.
Language Model Transformers as Evaluators for Open-domain Dialogues
Computer-based systems for communication with humans are a cornerstone of AI research since the 1950s.
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.
Formal Ontology Learning from English IS-A Sentences
Ontology learning (OL) is the process of automatically generating an ontological knowledge base from a plain text document.
SimDoc: Topic Sequence Alignment based Document Similarity Framework
Document similarity is the problem of estimating the degree to which a given pair of documents has similar semantic content.
Named Entity Recognition in Twitter using Images and Text
Named Entity Recognition (NER) is an important subtask of information extraction that seeks to locate and recognise named entities.
Translating Natural Language to SQL using Pointer-Generator Networks and How Decoding Order Matters
Translating natural language to SQL queries for table-based question answering is a challenging problem and has received significant attention from the research community.
NIF4OGGD - NLP Interchange Format for Open German Governmental Data
In the last couple of years the amount of structured open government data has increased significantly.
Soft Marginal TransE for Scholarly Knowledge Graph Completion
In this work, the TransE embedding model is reconciled for a specific link prediction task on scholarly metadata.
Adaptive Margin Ranking Loss for Knowledge Graph Embeddings via a Correntropy Objective Function
To tackle this problem, several loss functions have been proposed recently by adding upper bounds and lower bounds to the scores of positive and negative samples.
Introduction to Neural Network based Approaches for Question Answering over Knowledge Graphs
Question answering has emerged as an intuitive way of querying structured data sources, and has attracted significant advancements over the years.
Linking Physicians to Medical Research Results via Knowledge Graph Embeddings and Twitter
Informing professionals about the latest research results in their field is a particularly important task in the field of health care, since any development in this field directly improves the health status of the patients.
Towards Optimisation of Collaborative Question Answering over Knowledge Graphs
Collaborative Question Answering (CQA) frameworks for knowledge graphs aim at integrating existing question answering (QA) components for implementing sequences of QA tasks (i. e. QA pipelines).
LogicENN: A Neural Based Knowledge Graphs Embedding Model with Logical Rules
We prove that LogicENN can learn every ground truth of encoded rules in a knowledge graph.
Ranked #17 on
Link Prediction
on FB15k
Toward Understanding The Effect Of Loss function On Then Performance Of Knowledge Graph Embedding
We show that by a proper selection of the loss function for training the TransE model, the main limitations of the model are mitigated.
Method for the semantic indexing of concept hierarchies, uniform representation, use of relational database systems and generic and case-based reasoning
With the indexing algorithm, keys are computed such that concepts are partially unifiable with all more specific concepts and only semantically correct concepts are allowed to be added.
The Query Translation Landscape: a Survey
In particular, we study which query language is a most suitable candidate for that 'universal' query language.
Encoding Knowledge Graph Entity Aliases in Attentive Neural Network for Wikidata Entity Linking
In this paper, we examine the role of knowledge graph context on an attentive neural network approach for entity linking on Wikidata.
End-to-End Entity Linking and Disambiguation leveraging Word and Knowledge Graph Embeddings
Entity linking - connecting entity mentions in a natural language utterance to knowledge graph (KG) entities is a crucial step for question answering over KGs.
Treating Dialogue Quality Evaluation as an Anomaly Detection Problem
Dialogue systems for interaction with humans have been enjoying increased popularity in the research and industry fields.
5* Knowledge Graph Embeddings with Projective Transformations
Most of the embedding models designed in Euclidean geometry usually support a single transformation type - often translation or rotation, which is suitable for learning on graphs with small differences in neighboring subgraphs.
IQA: Interactive Query Construction in Semantic Question Answering Systems
Semantic Question Answering (SQA) systems automatically interpret user questions expressed in a natural language in terms of semantic queries.
Improving the Long-Range Performance of Gated Graph Neural Networks
Many popular variants of graph neural networks (GNNs) that are capable of handling multi-relational graphs may suffer from vanishing gradients.
MLM: A Benchmark Dataset for Multitask Learning with Multiple Languages and Modalities
We demonstrate the value of the resource in developing novel applications in the digital humanities with a motivating use case and specify a benchmark set of tasks to retrieve modalities and locate entities in the dataset.
Training Multimodal Systems for Classification with Multiple Objectives
We learn about the world from a diverse range of sensory information.
PNEL: Pointer Network based End-To-End Entity Linking over Knowledge Graphs
In such a pipeline, Entity Linking (EL) is often the first step.
Knowledge Graph Embeddings in Geometric Algebras
Knowledge graph (KG) embedding aims at embedding entities and relations in a KG into a lowdimensional latent representation space.
Motif Learning in Knowledge Graphs Using Trajectories Of Differential Equations
To this end, we represent each relation (edge) in a KG as a vector field on a smooth Riemannian manifold.
Multiple Run Ensemble Learning with Low-Dimensional Knowledge Graph Embeddings
For example, instead of training a model one time with a large embedding size of 1200, we repeat the training of the model 6 times in parallel with an embedding size of 200 and then combine the 6 separate models for testing while the overall numbers of adjustable parameters are same (6*200=1200) and the total memory footprint remains the same.
Trans4E: Link Prediction on Scholarly Knowledge Graphs
This is typical for KGs that categorize a large number of entities (e. g., research articles, patents, persons) according to a relatively small set of categories.
Time-aware Relational Graph Attention Network for Temporal Knowledge Graph Embeddings
Embedding-based representation learning approaches for knowledge graphs (KGs) have been mostly designed for static data.
Knowledge Graph Representation Learning using Ordinary Differential Equations
Many KGEs use the Euclidean geometry which renders them incapable of preserving complex structures and consequently causes wrong inferences by the models.
Toward Understanding The Effect of Loss Function on The Performance of Knowledge Graph Embedding
We show that by a proper selection of the loss function for training the TransE model, the main limitations of the model are mitigated.
Semantic Answer Type and Relation Prediction Task (SMART 2021)
The Semantic Answer Type and Relation Prediction Task (SMART) task is one of the ISWC 2021 Semantic Web challenges.
Geometric Algebra based Embeddings for Static and Temporal Knowledge Graph Completion
In this work, we strive to move beyond the complex or hypercomplex space for KGE and propose a novel geometric algebra based embedding approach, GeomE, which uses multivector representations and the geometric product to model entities and relations.
LEMON: LanguagE ModeL for Negative Sampling of Knowledge Graph Embeddings
The learning process of such models can be performed by contrasting positive and negative triples.
An Answer Verbalization Dataset for Conversational Question Answerings over Knowledge Graphs
We introduce a new dataset for conversational question answering over Knowledge Graphs (KGs) with verbalized answers.
Direct Fact Retrieval from Knowledge Graphs without Entity Linking
There has been a surge of interest in utilizing Knowledge Graphs (KGs) for various natural language processing/understanding tasks.
Investigating Multilingual Instruction-Tuning: Do Polyglot Models Demand for Multilingual Instructions?
The adaption of multilingual pre-trained Large Language Models (LLMs) into eloquent and helpful assistants is essential to facilitate their use across different language regions.
REXEL: An End-to-end Model for Document-Level Relation Extraction and Entity Linking
Extracting structured information from unstructured text is critical for many downstream NLP applications and is traditionally achieved by closed information extraction (cIE).
Ranked #1 on
Coreference Resolution
on DWIE
Coreference Resolution
Document-level Closed Information Extraction
+9
