Since static methods to learn the graph spectrum would not consider the history of the evolution of the spectrum as the graph evolves with time, we propose a novel approach to learn the graph wavelets to capture this evolving spectra.
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.
We introduce a new dataset for conversational question answering over Knowledge Graphs (KGs) with verbalized answers.
We further provide a theoretical analysis and prove that the spatial attention mechanism in the transformer cannot effectively capture the desired frequency response, thus, inherently limiting its expressiveness in spectral space.
Scholarly Knowledge Graphs (KGs) provide a rich source of structured information representing knowledge encoded in scientific publications.
Transformers have recently been applied in the more generic domain of graphs.
A few KGE techniques address interpretability, i. e., mapping the connectivity patterns of the relations (i. e., symmetric/asymmetric, inverse, and composition) to a geometric interpretation such as rotations.
The VOGUE framework attempts to generate a verbalized answer using a hybrid approach through a multi-task learning paradigm.
We present a novel method for relation extraction (RE) from a single sentence, mapping the sentence and two given entities to a canonical fact in a knowledge graph (KG).
For this task, we propose LASAGNE (muLti-task semAntic parSing with trAnsformer and Graph atteNtion nEtworks).
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.
This paper presents ParaQA, a question answering (QA) dataset with multiple paraphrased responses for single-turn conversation over knowledge graphs (KG).
In the last decade, a large number of Knowledge Graph (KG) information extraction approaches were proposed.
Our empirical study was conducted on two well-known knowledge bases (i. e., Wikidata and Wikipedia).
Ranked #1 on Entity Linking on MSNBC
In the era of Big Knowledge Graphs, Question Answering (QA) systems have reached a milestone in their performance and feasibility.
In this paper, we present a novel method named RECON, that automatically identifies relations in a sentence (sentential relation extraction) and aligns to a knowledge graph (KG).
We extract entities and relationships related to COVID-19 from a corpus of articles related to Corona virus by employing a novel entities and relationship model.
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
no code implementations • 14 Jul 2020 • Dharmen Punjani, Markos Iliakis, Theodoros Stefou, Kuldeep Singh, Andreas Both, Manolis Koubarakis, Iosif Angelidis, Konstantina Bereta, Themis Beris, Dimitris Bilidas, Theofilos Ioannidis, Nikolaos Karalis, Christoph Lange, Despina-Athanasia Pantazi, Christos Papaloukas, Georgios Stamoulis
We give a detailed description of the system's architecture, its underlying algorithms, and its evaluation using a set of 201 natural language questions.
The Natural Language Processing (NLP) community has significantly contributed to the solutions for entity and relation recognition from the text, and possibly linking them to proper matches in Knowledge Graphs (KGs).
In this paper, we examine the role of knowledge graph context on an attentive neural network approach for entity linking on Wikidata.
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).
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.
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.
Particle Filter(PF) is used extensively for estimation of target Non-linear and Non-gaussian state.