Search Results for author: Jens Lehmann

Found 68 papers, 37 papers with code

Time-aware Relational Graph Attention Network for Temporal Knowledge Graph Embeddings

no code implementations29 Sep 2021 Chengjin Xu, Fenglong Su, Jens Lehmann

Embedding-based representation learning approaches for knowledge graphs (KGs) have been mostly designed for static data.

Entity Alignment Graph Attention +2

Space Efficient Context Encoding for Non-Task-Oriented Dialogue Generation with Graph Attention Transformer

1 code implementation ACL 2021 Fabian Galetzka, Jewgeni Rose, David Schlangen, Jens Lehmann

To improve the coherence and knowledge retrieval capabilities of non-task-oriented dialogue systems, recent Transformer-based models aim to integrate fixed background context.

Dialogue Generation Graph Attention +2

Improving Inductive Link Prediction Using Hyper-Relational Facts

1 code implementation10 Jul 2021 Mehdi Ali, Max Berrendorf, Mikhail Galkin, Veronika Thost, Tengfei Ma, Volker Tresp, Jens Lehmann

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.

Knowledge Graphs Link Prediction

VOGUE: Answer Verbalization through Multi-Task Learning

1 code implementation24 Jun 2021 Endri Kacupaj, Shyamnath Premnadh, Kuldeep Singh, Jens Lehmann, Maria Maleshkova

The VOGUE framework attempts to generate a verbalized answer using a hybrid approach through a multi-task learning paradigm.

Knowledge Graphs Multi-Task Learning +1

VANiLLa : Verbalized Answers in Natural Language at Large Scale

1 code implementation24 May 2021 Debanjali Biswas, Mohnish Dubey, Md Rashad Al Hasan Rony, Jens Lehmann

We also present results of training our dataset on multiple baseline models adapted from current state-of-the-art Natural Language Generation (NLG) architectures.

Knowledge Graphs Question Answering +1

Multiple Run Ensemble Learning with Low-Dimensional Knowledge Graph Embeddings

no code implementations11 Apr 2021 Chengjin Xu, Mojtaba Nayyeri, Sahar Vahdati, Jens Lehmann

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.

Ensemble Learning Knowledge Graph Completion +3

Context Transformer with Stacked Pointer Networks for Conversational Question Answering over Knowledge Graphs

1 code implementation13 Mar 2021 Joan Plepi, Endri Kacupaj, Kuldeep Singh, Harsh Thakkar, Jens Lehmann

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.

Knowledge Graphs Question Answering +1

ParaQA: A Question Answering Dataset with Paraphrase Responses for Single-Turn Conversation

1 code implementation13 Mar 2021 Endri Kacupaj, Barshana Banerjee, Kuldeep Singh, Jens Lehmann

This paper presents ParaQA, a question answering (QA) dataset with multiple paraphrased responses for single-turn conversation over knowledge graphs (KG).

Knowledge Graphs Question Answering +1

SeMantic AnsweR Type prediction task (SMART) at ISWC 2020 Semantic Web Challenge

1 code implementation1 Dec 2020 Nandana Mihindukulasooriya, Mohnish Dubey, Alfio Gliozzo, Jens Lehmann, Axel-Cyrille Ngonga Ngomo, Ricardo Usbeck

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.

Knowledge Base Question Answering Type prediction

Knowledge Graph Embeddings in Geometric Algebras

no code implementations COLING 2020 Chengjin Xu, Mojtaba Nayyeri, Yung-Yu Chen, Jens Lehmann

Knowledge graph (KG) embedding aims at embedding entities and relations in a KG into a lowdimensional latent representation space.

Knowledge Graph Embeddings Knowledge Graphs +1

TeRo: A Time-aware Knowledge Graph Embedding via Temporal Rotation

2 code implementations COLING 2020 Chengjin Xu, Mojtaba Nayyeri, Fouad Alkhoury, Hamed Shariat Yazdi, Jens Lehmann

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.

Knowledge Graph Embedding Link Prediction

Message Passing for Hyper-Relational Knowledge Graphs

1 code implementation EMNLP 2020 Mikhail Galkin, Priyansh Trivedi, Gaurav Maheshwari, Ricardo Usbeck, Jens Lehmann

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.

Knowledge Graphs Link Prediction

MLM: A Benchmark Dataset for Multitask Learning with Multiple Languages and Modalities

1 code implementation14 Aug 2020 Jason Armitage, Endri Kacupaj, Golsa Tahmasebzadeh, Swati, Maria Maleshkova, Ralph Ewerth, Jens Lehmann

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.

Representation Learning Scene Understanding

Evaluating the Impact of Knowledge Graph Context on Entity Disambiguation Models

1 code implementation12 Aug 2020 Isaiah Onando Mulang', Kuldeep Singh, Chaitali Prabhu, Abhishek Nadgeri, Johannes Hoffart, Jens Lehmann

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.

Entity Disambiguation

PyKEEN 1.0: A Python Library for Training and Evaluating Knowledge Graph Embeddings

2 code implementations28 Jul 2020 Mehdi Ali, Max Berrendorf, Charles Tapley Hoyt, Laurent Vermue, Sahand Sharifzadeh, Volker Tresp, Jens Lehmann

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)

Knowledge Graph Embedding Knowledge Graph Embeddings +1

Improving the Long-Range Performance of Gated Graph Neural Networks

no code implementations19 Jul 2020 Denis Lukovnikov, Jens Lehmann, Asja Fischer

Many popular variants of graph neural networks (GNNs) that are capable of handling multi-relational graphs may suffer from vanishing gradients.

Bringing Light Into the Dark: A Large-scale Evaluation of Knowledge Graph Embedding Models Under a Unified Framework

2 code implementations23 Jun 2020 Mehdi Ali, Max Berrendorf, Charles Tapley Hoyt, Laurent Vermue, Mikhail Galkin, Sahand Sharifzadeh, Asja Fischer, Volker Tresp, Jens Lehmann

The heterogeneity in recently published knowledge graph embedding models' implementations, training, and evaluation has made fair and thorough comparisons difficult.

Knowledge Graph Embedding

IQA: Interactive Query Construction in Semantic Question Answering Systems

no code implementations20 Jun 2020 Hamid Zafar, Mohnish Dubey, Jens Lehmann, Elena Demidova

Semantic Question Answering (SQA) systems automatically interpret user questions expressed in a natural language in terms of semantic queries.

Question Answering

5* Knowledge Graph Embeddings with Projective Transformations

no code implementations8 Jun 2020 Mojtaba Nayyeri, Sahar Vahdati, Can Aykul, Jens Lehmann

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.

Knowledge Graph Completion Knowledge Graph Embedding +3

End-to-End Entity Linking and Disambiguation leveraging Word and Knowledge Graph Embeddings

no code implementations25 Feb 2020 Rostislav Nedelchev, Debanjan Chaudhuri, Jens Lehmann, Asja Fischer

Entity linking - connecting entity mentions in a natural language utterance to knowledge graph (KG) entities is a crucial step for question answering over KGs.

Entity Disambiguation Entity Linking +4

Incorporating Joint Embeddings into Goal-Oriented Dialogues with Multi-Task Learning

1 code implementation28 Jan 2020 Firas Kassawat, Debanjan Chaudhuri, Jens Lehmann

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.

Goal-Oriented Dialog Goal-Oriented Dialogue Systems +2

Open-domain Event Extraction and Embedding for Natural Gas Market Prediction

1 code implementation8 Dec 2019 Minh Triet Chau, Diego Esteves, Jens Lehmann

We propose an approach to predict the natural gas price in several days using historical price data and events extracted from news headlines.

Event Extraction Sentence Embedding +1

Temporal Knowledge Graph Embedding Model based on Additive Time Series Decomposition

1 code implementation18 Nov 2019 Chengjin Xu, Mojtaba Nayyeri, Fouad Alkhoury, Hamed Shariat Yazdi, Jens Lehmann

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.

Knowledge Graph Completion Knowledge Graph Embedding +2

Using a KG-Copy Network for Non-Goal Oriented Dialogues

1 code implementation17 Oct 2019 Debanjan Chaudhuri, Md Rashad Al Hasan Rony, Simon Jordan, Jens Lehmann

Non-goal oriented, generative dialogue systems lack the ability to generate answers with grounded facts.

Knowledge Graphs

The Query Translation Landscape: a Survey

no code implementations7 Oct 2019 Mohamed Nadjib Mami, Damien Graux, Harsh Thakkar, Simon Scerri, Sören Auer, Jens Lehmann

In particular, we study which query language is a most suitable candidate for that 'universal' query language.

Translation

Method for the semantic indexing of concept hierarchies, uniform representation, use of relational database systems and generic and case-based reasoning

no code implementations3 Oct 2019 Uwe Petersohn, Sandra Zimmer, Jens Lehmann

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.

Distantly Supervised Question Parsing

1 code implementation27 Sep 2019 Hamid Zafar, Maryam Tavakol, Jens Lehmann

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.

Knowledge Graphs Question Answering +1

Towards Optimisation of Collaborative Question Answering over Knowledge Graphs

no code implementations14 Aug 2019 Kuldeep Singh, Mohamad Yaser Jaradeh, Saeedeh Shekarpour, Akash Kulkarni, Arun Sethupat Radhakrishna, Ioanna Lytra, Maria-Esther Vidal, Jens Lehmann

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).

Feature Selection Knowledge Graphs +1

Linking Physicians to Medical Research Results via Knowledge Graph Embeddings and Twitter

no code implementations24 Jul 2019 Afshin Sadeghi, Jens Lehmann

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.

Knowledge Graph Embeddings

Introduction to Neural Network based Approaches for Question Answering over Knowledge Graphs

no code implementations22 Jul 2019 Nilesh Chakraborty, Denis Lukovnikov, Gaurav Maheshwari, Priyansh Trivedi, Jens Lehmann, Asja Fischer

Question answering has emerged as an intuitive way of querying structured data sources, and has attracted significant advancements over the years.

Knowledge Graphs Question Answering

Adaptive Margin Ranking Loss for Knowledge Graph Embeddings via a Correntropy Objective Function

no code implementations9 Jul 2019 Mojtaba Nayyeri, Xiaotian Zhou, Sahar Vahdati, Hamed Shariat Yazdi, Jens Lehmann

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.

Knowledge Graph Embeddings Knowledge Graphs +2

Old is Gold: Linguistic Driven Approach for Entity and Relation Linking of Short Text

1 code implementation NAACL 2019 Ahmad Sakor, on, Isaiah o Mulang{'}, Kuldeep Singh, Saeedeh Shekarpour, Maria Esther Vidal, Jens Lehmann, S{\"o}ren Auer

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.

Entity Linking Named Entity Recognition +1

MDE: Multiple Distance Embeddings for Link Prediction in Knowledge Graphs

2 code implementations25 May 2019 Afshin Sadeghi, Damien Graux, Hamed Shariat Yazdi, Jens Lehmann

We propose the Multiple Distance Embedding model (MDE) that addresses these limitations and a framework to collaboratively combine variant latent distance-based terms.

Link Prediction Relational Pattern Learning +1

Soft Marginal TransE for Scholarly Knowledge Graph Completion

no code implementations27 Apr 2019 Mojtaba Nayyeri, Sahar Vahdati, Jens Lehmann, Hamed Shariat Yazdi

In this work, the TransE embedding model is reconciled for a specific link prediction task on scholarly metadata.

Knowledge Graph Completion Link Prediction +1

Translating Natural Language to SQL using Pointer-Generator Networks and How Decoding Order Matters

no code implementations13 Nov 2018 Denis Lukovnikov, Nilesh Chakraborty, Jens Lehmann, Asja Fischer

Translating natural language to SQL queries for table-based question answering is a challenging problem and has received significant attention from the research community.

Question Answering Semantic Parsing

Learning to Rank Query Graphs for Complex Question Answering over Knowledge Graphs

1 code implementation2 Nov 2018 Gaurav Maheshwari, Priyansh Trivedi, Denis Lukovnikov, Nilesh Chakraborty, Asja Fischer, Jens Lehmann

In this paper, we conduct an empirical investigation of neural query graph ranking approaches for the task of complex question answering over knowledge graphs.

Graph Ranking Knowledge Graphs +3

No One is Perfect: Analysing the Performance of Question Answering Components over the DBpedia Knowledge Graph

2 code implementations26 Sep 2018 Kuldeep Singh, Ioanna Lytra, Arun Sethupat Radhakrishna, Saeedeh Shekarpour, Maria-Esther Vidal, Jens Lehmann

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.

Knowledge Graphs Question Answering

How Complex is your classification problem? A survey on measuring classification complexity

2 code implementations10 Aug 2018 Ana C. Lorena, Luís P. F. Garcia, Jens Lehmann, Marcilio C. P. Souto, Tin K. Ho

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.

Classification General Classification

Formal Ontology Learning from English IS-A Sentences

no code implementations11 Feb 2018 Sourish Dasgupta, Ankur Padia, Gaurav Maheshwari, Priyansh Trivedi, Jens Lehmann

Ontology learning (OL) is the process of automatically generating an ontological knowledge base from a plain text document.

Incorporating Literals into Knowledge Graph Embeddings

1 code implementation3 Feb 2018 Agustinus Kristiadi, Mohammad Asif Khan, Denis Lukovnikov, Jens Lehmann, Asja Fischer

Most of the existing work on embedding (or latent feature) based knowledge graph analysis focuses mainly on the relations between entities.

Entity Embeddings Knowledge Graph Embeddings +2

EARL: Joint Entity and Relation Linking for Question Answering over Knowledge Graphs

1 code implementation11 Jan 2018 Mohnish Dubey, Debayan Banerjee, Debanjan Chaudhuri, Jens Lehmann

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.

Entity Linking Knowledge Graphs +3

Named Entity Recognition in Twitter using Images and Text

no code implementations30 Oct 2017 Diego Esteves, Rafael Peres, Jens Lehmann, Giulio Napolitano

Named Entity Recognition (NER) is an important subtask of information extraction that seeks to locate and recognise named entities.

Named Entity Recognition NER

Using Multi-Label Classification for Improved Question Answering

1 code implementation24 Oct 2017 Ricardo Usbeck, Michael Hoffmann, Michael Röder, Jens Lehmann, Axel-Cyrille Ngonga Ngomo

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.

Classification General Classification +2

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