Answering Complex Questions by Joining Multi-Document Evidence with Quasi Knowledge Graphs

Lu, Xiaolu; Pramanik, Soumajit; Roy, Rishiraj Saha; Abujabal, Abdalghani; Wang, Yafang; Weikum, Gerhard

doi:10.1145/3331184.3331252

Cited by 58 publications

(49 citation statements)

References 57 publications

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“…This method has been inspired from the technique used in ‘QUEST’ ( [11] ). We define the entity nodes of the queries as cornerstones .…”

Section: Methodsmentioning

confidence: 99%

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Graph NLU enabled question answering system

Varma¹,

Shivam²,

Biswas³

et al. 2021

Heliyon

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With a huge amount of information being stored as structured data, there is an increasing need for retrieving exact answers to questions from tables. Answering natural language questions on structured data usually involves semantic parsing of query to a machine understandable format which is then used to retrieve information from the database. Training semantic parsers for domain specific tasks is a tedious job and does not guarantee accurate results. In this paper, we used conversational analytics tool to create the user interface and to get the required entities and intents from the query thus avoiding the traditional semantic parsing approach. We then make use of Knowledge Graph for querying in structured data domain. Knowledge graphs can be easily leveraged for question answering systems, to use them as the database. We extract appropriate answers for different types of queries which have been illustrated in the Results section.

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“…This method has been inspired from the technique used in ‘QUEST’ ( [11] ). We define the entity nodes of the queries as cornerstones .…”

Section: Methodsmentioning

confidence: 99%

“…Lu et al [11] have proposed a model ‘QUEST’ which attempts at answering complex queries from multiple unstructured documents with the use of Quasi Knowledge Graph. QUEST dynamically retrieves documents related to the query and generates subject-predicate-object triples.…”

Section: Related Workmentioning

confidence: 99%

Graph NLU enabled question answering system

Varma¹,

Shivam²,

Biswas³

et al. 2021

Heliyon

View full text Add to dashboard Cite

show abstract

“…The Quasi Knowledge Graphs System, proposed by Lu et al (2019), is designed to answer complex questions. It is a novel method that computes answers by dynamically building up a knowledge graph that fits the query.…”

Section: Retrieval Of Similar Instances From a Large Corpusmentioning

confidence: 99%

Entity Enhancement for Implicit Discourse Relation Classification in the Biomedical Domain

Wei¹,

Demberg²

2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

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Implicit discourse relation classification is a challenging task, in particular when the text domain is different from the standard Penn Discourse Treebank (PDTB;Prasad et al., 2008) training corpus domain (Wall Street Journal in 1990s). We here tackle the task of implicit discourse relation classification on the biomedical domain, for which the Biomedical Discourse Relation Bank (BioDRB; Prasad et al., 2011) is available. We show that entity information can be used to improve discourse relational argument representation. In a first step, we show that explicitly marked instances that are content-wise similar to the target relations can be used to achieve good performance in the cross-domain setting using a simple unsupervised voting pipeline. As a further step, we show that with the linked entity information from the first step, a transformer which is augmented with entity-related information (K-BERT; Liu et al., 2020) sets the new state of the art performance on the dataset, outperforming the large pre-trained BioBERT (Lee et al., 2020) model by 2% points.

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“…Like delft, qest [33] seeks to address this by building a noisy quasi-kg with nodes and edges, consisting of dynamically retrieved entity names and relational phrases from raw text. Unlike delft, this graph is built using existing Open Information Extraction (ie).…”

Section: Knowledge Graph Question Answeringmentioning

confidence: 99%

Complex Factoid Question Answering with a Free-Text Knowledge Graph

Zhao

Xiong

Qian

et al. 2020

Proceedings of the Web Conference 2020

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We introduce delft, a factoid question answering system which combines the nuance and depth of knowledge graph question answering approaches with the broader coverage of free-text. delft builds a free-text knowledge graph from Wikipedia, with entities as nodes and sentences in which entities co-occur as edges. For each question, delft finds the subgraph linking question entity nodes to candidates using text sentences as edges, creating a dense and high coverage semantic graph. A novel graph neural network reasons over the free-text graph-combining evidence on the nodes via information along edge sentences-to select a final answer. Experiments on three question answering datasets show delft can answer entity-rich questions better than machine reading based models, bert-based answer ranking and memory networks. delft's advantage comes from both the high coverage of its free-text knowledge graph-more than double that of dbpedia relations-and the novel graph neural network which reasons on the rich but noisy free-text evidence.

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Answering Complex Questions by Joining Multi-Document Evidence with Quasi Knowledge Graphs

Cited by 58 publications

References 57 publications

Graph NLU enabled question answering system

Graph NLU enabled question answering system

Entity Enhancement for Implicit Discourse Relation Classification in the Biomedical Domain

Complex Factoid Question Answering with a Free-Text Knowledge Graph

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