Search Results for author:
Found 25 papers, 8 papers with code
Leveraging Abstract Meaning Representation for Knowledge Base Question Answering
Knowledge base question answering (KBQA)is an important task in Natural Language Processing.
Leveraging Semantic Parsing for Relation Linking over Knowledge Bases
Knowledgebase question answering systems are heavily dependent on relation extraction and linking modules.
Ranked #1 on
Relation Linking
on QALD-7
Open Knowledge Graphs Canonicalization using Variational Autoencoders
In this work, we propose Canonicalizing Using Variational Autoencoders (CUVA), a joint model to learn both embeddings and cluster assignments in an end-to-end approach, which leads to a better vector representation for the noun and relation phrases.
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.
Scaling Knowledge Graphs for Automating AI of Digital Twins
Digital Twins are digital representations of systems in the Internet of Things (IoT) that are often based on AI models that are trained on data from those systems.
Finspector: A Human-Centered Visual Inspection Tool for Exploring and Comparing Biases among Foundation Models
Pre-trained transformer-based language models are becoming increasingly popular due to their exceptional performance on various benchmarks.
Hypernym Detection Using Strict Partial Order Networks
This paper introduces Strict Partial Order Networks (SPON), a novel neural network architecture designed to enforce asymmetry and transitive properties as soft constraints.
Type Prediction Systems
Inferring semantic types for entity mentions within text documents is an important asset for many downstream NLP tasks, such as Semantic Role Labelling, Entity Disambiguation, Knowledge Base Question Answering, etc.
Generative Relation Linking for Question Answering over Knowledge Bases
Relation linking is essential to enable question answering over knowledge bases.
Ranked #1 on
Relation Linking
on QALD-9
A Semantics-aware Transformer Model of Relation Linking for Knowledge Base Question Answering
Relation linking is a crucial component of Knowledge Base Question Answering systems.
SYGMA: System for Generalizable Modular Question Answering OverKnowledge Bases
In addition, to demonstrate extensi-bility to additional reasoning types we evaluate on multi-hopreasoning datasets and a new Temporal KBQA benchmarkdataset on Wikidata, namedTempQA-WD1, introduced in thispaper.
Dynamic Facet Selection by Maximizing Graded Relevance
Dynamic faceted search (DFS), an interactive query refinement technique, is a form of Human–computer information retrieval (HCIR) approach.
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.
Learning to Transpile AMR into SPARQL
We propose a transition-based system to transpile Abstract Meaning Representation (AMR) into SPARQL for Knowledge Base Question Answering (KBQA).
Applying a Generic Sequence-to-Sequence Model for Simple and Effective Keyphrase Generation
In recent years, a number of keyphrase generation (KPG) approaches were proposed consisting of complex model architectures, dedicated training paradigms and decoding strategies.
A Benchmark for Generalizable and Interpretable Temporal Question Answering over Knowledge Bases
Specifically, our benchmark is a temporal question answering dataset with the following advantages: (a) it is based on Wikidata, which is the most frequently curated, openly available knowledge base, (b) it includes intermediate sparql queries to facilitate the evaluation of semantic parsing based approaches for KBQA, and (c) it generalizes to multiple knowledge bases: Freebase and Wikidata.
KGI: An Integrated Framework for Knowledge Intensive Language Tasks
In this paper, we present a system to showcase the capabilities of the latest state-of-the-art retrieval augmented generation models trained on knowledge-intensive language tasks, such as slot filling, open domain question answering, dialogue, and fact-checking.
CBR-iKB: A Case-Based Reasoning Approach for Question Answering over Incomplete Knowledge Bases
Yet, in many question answering applications coupled with knowledge bases, the sparse nature of KBs is often overlooked.
An Empirical Study on Pseudo-log-likelihood Bias Measures for Masked Language Models Using Paraphrased Sentences
In this paper, we conduct an empirical study on a bias measure, log-likelihood Masked Language Model (MLM) scoring, on a benchmark dataset.
Permutation Invariant Strategy Using Transformer Encoders for Table Understanding
Existing methods that leverage pretrained Transformer encoders range from a simple construction of pseudo-sentences by concatenating text across rows or columns to complex parameter-intensive models that encode table structure and require additional pretraining.
Knowledge Graph Induction enabling Recommending and Trend Analysis: A Corporate Research Community Use Case
A research division plays an important role of driving innovation in an organization.
KnowGL: Knowledge Generation and Linking from Text
We propose KnowGL, a tool that allows converting text into structured relational data represented as a set of ABox assertions compliant with the TBox of a given Knowledge Graph (KG), such as Wikidata.
Exploring In-Context Learning Capabilities of Foundation Models for Generating Knowledge Graphs from Text
Knowledge graphs can represent information about the real-world using entities and their relations in a structured and semantically rich manner and they enable a variety of downstream applications such as question-answering, recommendation systems, semantic search, and advanced analytics.
Text2KGBench: A Benchmark for Ontology-Driven Knowledge Graph Generation from Text
In this paper, we present Text2KGBench, a benchmark to evaluate the capabilities of language models to generate KGs from natural language text guided by an ontology.
Matching Table Metadata with Business Glossaries Using Large Language Models
The resulting matching enables the use of an available or curated business glossary for retrieval and analysis without or before requesting access to the data contents.
