UNIQORN: Unified Question Answering over RDF Knowledge Graphs and Natural Language Text

• URL: http://arxiv.org/abs/2108.08614v9
• Date: Thu, 25 Jul 2024 18:51:23 GMT
• Title: UNIQORN: Unified Question Answering over RDF Knowledge Graphs and Natural Language Text
• Authors: Soumajit Pramanik, Jesujoba Alabi, Rishiraj Saha Roy, Gerhard Weikum,
• Abstract summary: Question answering over RDF data like knowledge graphs has been greatly advanced. IR and NLP communities have addressed QA over text, but such systems barely utilize semantic data and knowledge. This paper presents a method for complex questions that can seamlessly operate over a mixture of RDF datasets and text corpora.
• Score: 20.1784368017206
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Question answering over RDF data like knowledge graphs has been greatly advanced, with a number of good systems providing crisp answers for natural language questions or telegraphic queries. Some of these systems incorporate textual sources as additional evidence for the answering process, but cannot compute answers that are present in text alone. Conversely, the IR and NLP communities have addressed QA over text, but such systems barely utilize semantic data and knowledge. This paper presents a method for complex questions that can seamlessly operate over a mixture of RDF datasets and text corpora, or individual sources, in a unified framework. Our method, called UNIQORN, builds a context graph on-the-fly, by retrieving question-relevant evidences from the RDF data and/or a text corpus, using fine-tuned BERT models. The resulting graph typically contains all question-relevant evidences but also a lot of noise. UNIQORN copes with this input by a graph algorithm for Group Steiner Trees, that identifies the best answer candidates in the context graph. Experimental results on several benchmarks of complex questions with multiple entities and relations, show that UNIQORN significantly outperforms state-of-the-art methods for heterogeneous QA -- in a full training mode, as well as in zero-shot settings. The graph-based methodology provides user-interpretable evidence for the complete answering process.

Related papers

• Exploring Hint Generation Approaches in Open-Domain Question Answering [16.434748534272014]
  We introduce a novel context preparation approach called HINTQA. Unlike traditional methods, HINTQA prompts LLMs to produce hints about potential answers for the question. We demonstrate that hints enhance the accuracy of answers more than retrieved and generated contexts.
  arXiv  Detail & Related papers  (2024-09-24T13:50:32Z)
• Integrating Large Language Models with Graph-based Reasoning for Conversational Question Answering [58.17090503446995]
  We focus on a conversational question answering task which combines the challenges of understanding questions in context and reasoning over evidence gathered from heterogeneous sources like text, knowledge graphs, tables, and infoboxes. Our method utilizes a graph structured representation to aggregate information about a question and its context.
  arXiv  Detail & Related papers  (2024-06-14T13:28:03Z)
• From Local to Global: A Graph RAG Approach to Query-Focused Summarization [3.9676927113698626]
  We propose a Graph RAG approach to question answering over private text corpora. Our approach uses an entity knowledge graph from the source documents, then to pregenerate community summaries for all groups of closely-related entities. For a class of global sensemaking questions over datasets in the 1 million token range, we show that Graph RAG leads to substantial improvements over a na"ive RAG baseline.
  arXiv  Detail & Related papers  (2024-04-24T18:38:11Z)
• Open-Set Knowledge-Based Visual Question Answering with Inference Paths [79.55742631375063]
  The purpose of Knowledge-Based Visual Question Answering (KB-VQA) is to provide a correct answer to the question with the aid of external knowledge bases. We propose a new retriever-ranker paradigm of KB-VQA, Graph pATH rankER (GATHER for brevity) Specifically, it contains graph constructing, pruning, and path-level ranking, which not only retrieves accurate answers but also provides inference paths that explain the reasoning process.
  arXiv  Detail & Related papers  (2023-10-12T09:12:50Z)
• BigText-QA: Question Answering over a Large-Scale Hybrid Knowledge Graph [23.739432128095107]
  BigText-QA is able to answer questions based on a structured knowledge graph. Our results demonstrate that BigText-QA outperforms DrQA, a neural-network-based QA system, and achieves competitive results to QUEST, a graph-based unsupervised QA system.
  arXiv  Detail & Related papers  (2022-12-12T09:49:02Z)
• Open-domain Question Answering via Chain of Reasoning over Heterogeneous Knowledge [82.5582220249183]
  We propose a novel open-domain question answering (ODQA) framework for answering single/multi-hop questions across heterogeneous knowledge sources. Unlike previous methods that solely rely on the retriever for gathering all evidence in isolation, our intermediary performs a chain of reasoning over the retrieved set. Our system achieves competitive performance on two ODQA datasets, OTT-QA and NQ, against tables and passages from Wikipedia.
  arXiv  Detail & Related papers  (2022-10-22T03:21:32Z)
• FeTaQA: Free-form Table Question Answering [33.018256483762386]
  We introduce FeTaQA, a new dataset with 10K Wikipedia-based table, question, free-form answer, supporting table cells pairs. FeTaQA yields a more challenging table question answering setting because it requires generating free-form text answers after retrieval, inference, and integration of multiple discontinuous facts from a structured knowledge source.
  arXiv  Detail & Related papers  (2021-04-01T09:59:40Z)
• ComQA:Compositional Question Answering via Hierarchical Graph Neural Networks [47.12013005600986]
  We present a large-scale compositional question answering dataset containing more than 120k human-labeled questions. To tackle the ComQA problem, we proposed a hierarchical graph neural networks, which represents the document from the low-level word to the high-level sentence. Our proposed model achieves a significant improvement over previous machine reading comprehension methods and pre-training methods.
  arXiv  Detail & Related papers  (2021-01-16T08:23:27Z)
• Open Question Answering over Tables and Text [55.8412170633547]
  In open question answering (QA), the answer to a question is produced by retrieving and then analyzing documents that might contain answers to the question. Most open QA systems have considered only retrieving information from unstructured text. We present a new large-scale dataset Open Table-and-Text Question Answering (OTT-QA) to evaluate performance on this task.
  arXiv  Detail & Related papers  (2020-10-20T16:48:14Z)
• Semantic Graphs for Generating Deep Questions [98.5161888878238]
  We propose a novel framework which first constructs a semantic-level graph for the input document and then encodes the semantic graph by introducing an attention-based GGNN (Att-GGNN) On the HotpotQA deep-question centric dataset, our model greatly improves performance over questions requiring reasoning over multiple facts, leading to state-of-the-art performance.
  arXiv  Detail & Related papers  (2020-04-27T10:52:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.