| Challenge: | Existing datasets for question answering over knowledge graphs lack answer triples from Freebase . a defunct knowledge graph makes it difficult to build "real-world" question answering systems . |
| Approach: | They propose a benchmark dataset for simple question answering over knowledge graphs that maps SimpleQuestions entities and predicates from Freebase to DBpedia. |
| Outcome: | The proposed dataset provides simple yet strong baselines with and without neural networks. |
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Strong Baselines for Simple Question Answering over Knowledge Graphs with and without Neural Networks (N18-2)
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| Challenge: | Existing work on simple question answering over knowledge graphs involves increasingly complex NN architectures. |
| Approach: | They propose to decompose the problem into entity detection, entity linking, relation prediction, evidence combination and heuristics. |
| Outcome: | The proposed approach outperforms existing models and benchmarks on a simple QA task. |
An empirical analysis of existing systems and datasets toward general simple question answering (2020.coling-main)
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| Challenge: | evaluators of simple factoid question answering using different datasets are not able to solve SimpleQuestions. |
| Approach: | They evaluate the progress of the field toward solving simple factoid questions over a knowledge base. |
| Outcome: | The proposed model is nearly solved on the most popular dataset, but not on the robustness of existing systems. |
A System for Answering Simple Questions in Multiple Languages (2023.acl-demo)
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| Challenge: | Existing knowledge graph question answering systems are limited to simple questions, but they can be used to answer complex questions. |
| Approach: | They propose a multilingual Knowledge Graph Question Answering technique that orders potential responses based on the distance between the question’s text embeddings and the answer’s graph embedds. |
| Outcome: | The proposed method consistently outperforms baseline systems, including seq2seq QA models and complex rule-based pipelines. |
FreebaseQA: A New Factoid QA Data Set Matching Trivia-Style Question-Answer Pairs with Freebase (N19-1)
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| Challenge: | Using FreebaseQA, we can generate over 54K matches from about 28K unique questions with minimal cost. |
| Approach: | They propose a data set for open-domain factoid question answering tasks over structured knowledge bases, like Freebase, using a combination of trivia-type question-answer pairs and subject-predicate-object triples. |
| Outcome: | The proposed data set generates 54K matches from 28K unique questions with minimal cost. |
A guide to the dataset explosion in QA, NLI, and commonsense reasoning (2020.coling-tutorials)
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| Challenge: | a tutorial aims to provide an up-to-date guide to the recent datasets . the target audience is the NLP practitioners who are lost in dozens of the recent data sets. |
| Approach: | This tutorial provides an up-to-date guide to the recent datasets . it surveys old and new methodological issues with dataset construction . |
| Outcome: | This tutorial aims to provide an up-to-date guide to the recent datasets . it surveys the old and new methodological issues with dataset construction . |
Graph Reasoning for Question Answering with Triplet Retrieval (2023.findings-acl)
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| Challenge: | Existing methods to answer complex questions require reasoning over knowledge graphs (KGs) state-of-the-art methods constrain retrieved knowledge in local subgraphs and discard more diverse triplets that are disconnected but useful for question answering. |
| Approach: | They propose a method to retrieve the most relevant triplets from KGs and then rerank them, which are then concatenated with questions to be fed into language models. |
| Outcome: | The proposed method outperforms state-of-the-art methods on commonsenseQA and OpenbookQA datasets with 4.6% absolute accuracy. |
Simple Question Answering with Subgraph Ranking and Joint-Scoring (N19-1)
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| Challenge: | Knowledge graph based simple question answering is a major area of research in question answering. |
| Approach: | They propose a framework to describe and analyze existing knowledge graph based simple question answering approaches. |
| Outcome: | The proposed model achieves a state-of-the-art (85.44% accuracy) on the SimpleQuestions dataset. |
Improving Query Graph Generation for Complex Question Answering over Knowledge Base (2021.emnlp-main)
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| Challenge: | Existing Knowledge-based Question Answering methods use a query graph to find the answer to a question. |
| Approach: | They propose a method that starts with the entire knowledge base and gradually shrinks it to the desired query graph. |
| Outcome: | Experimental results show that the proposed method achieves state-of-the-art performance on ComplexWebQuestion dataset. |
Enhancing Complex Reasoning in Knowledge Graph Question Answering through Query Graph Approximation (2025.findings-acl)
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| Challenge: | Existing knowledge-grounded question answering frameworks lack essential triplets related to the questions . Existing approaches to knowledge-based QA are incomplete in the context of KGs . |
| Approach: | They propose a framework to provide answers to structured queries by leveraging Knowledge Graphs. |
| Outcome: | The proposed framework outperforms existing methods on QA tasks where KGs are incomplete . the framework is based on a set of data from a dataset of QA questions . |
DBQR-QA: A Question Answering Dataset on a Hybrid of Database Querying and Reasoning (2024.findings-acl)
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| Challenge: | Question answering (QA) is a fundamental task in the field of Natural Language Processing (NLP). |
| Approach: | They propose a database querying and reasoning dataset for question answering that is designed to accommodate sequential questions and multi-hop queries. |
| Outcome: | The proposed dataset better mirrors the dynamics of real-world information retrieval and analysis with a particular focus on the financial reports of US companies. |