Multi-Hop Reasoning for Question Answering with Knowledge Graph

Zhang, Jiayuan; Cai, Yifei; Zhang, Qian; Cao, Zehao; Cheng, Zhenrong; Li, Dongmei; Meng, Xianghao

doi:10.1109/icis51600.2021.9516865

Cited by 39 publications

(59 citation statements)

References 7 publications

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“…In SimpleQuestions (Bordes et al, 2015) one needs to extract just a single fact from the KG to answer a question. MetaQA (Zhang et al, 2017) and WebQuestionsSP (Yih et al, 2015) require multi-hop reasoning, where one must traverse over multiple edges in the KG to reach the answer. ComplexWebQuestions (Talmor and Berant, 2018) contains both multi-hop and conjunction/comparison type questions.…”

Section: Temporal Qa Data Setsmentioning

confidence: 99%

“…Each of these templates has a corresponding procedure that could be executed over the temporal KG to extract all possible answers for that template. However, similar to Zhang et al (2017), we chose not to make this procedure a part of the dataset, to remove unwelcome dependence of QA systems on such formal candidate collection methods. This also allows easy augmentation of the dataset, since only question-answer pairs are needed.…”

Section: Temporal Questionsmentioning

confidence: 99%

“…This is in contrast to reading comprehension-based question answering, where typically the question is accompanied by a context (e.g., text passage) and the answer is either one of multiple choices (Rajpurkar et al, 2016) or a piece of text from the context (Yang et al, 2018). In KGQA, the answer is usually an entity (node) in the KG, and the reasoning required to answer questions is either single-fact based (Bordes et al, 2015), multi-hop (Yih et al 2015, Zhang et al 2017 or conjunction/comparison based reasoning (Talmor and Berant, 2018). Temporal KGQA takes this a step further where: Jia et al (2018a).…”

Section: Introductionmentioning

confidence: 99%

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Question Answering Over Temporal Knowledge Graphs

Saxena¹,

Chakrabarti²,

Talukdar³

2021

Preprint

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Temporal Knowledge Graphs (Temporal KGs) extend regular Knowledge Graphs by providing temporal scopes (e.g., start and end times) on each edge in the KG. While Question Answering over KG (KGQA) has received some attention from the research community, QA over Temporal KGs (Temporal KGQA) is a relatively unexplored area. Lack of broadcoverage datasets has been another factor limiting progress in this area. We address this challenge by presenting CRONQUESTIONS, the largest known Temporal KGQA dataset, clearly stratified into buckets of structural complexity. CRONQUESTIONS expands the only known previous dataset by a factor of 340×. We find that various state-of-the-art KGQA methods fall far short of the desired performance on this new dataset. In response, we also propose CRONKGQA, a transformerbased solution that exploits recent advances in Temporal KG embeddings, and achieves performance superior to all baselines, with an increase of 120% in accuracy over the next best performing method. Through extensive experiments, we give detailed insights into the workings of CRONKGQA, as well as situations where significant further improvements appear possible. In addition to the dataset, we have released our code as well.1. The underlying KG is a Temporal KG.2. The answer is either an entity or time duration. 3. Complex temporal reasoning might be needed. KG Embeddings are low-dimensional dense vector representations of entities and relations in a KG. Several methods have been proposed in the literature to embed KGs (Bordes et al. 2013, Trouillon et al. 2016, Vashishth et al. 2020). These embeddings were originally proposed for the task of KG completion i.e., predicting missing edges in the KG, since most real world KGs are incomplete. Recently, however, they have also been applied to the task of KGQA where they have been shown to increase performance the settings of both of complete and incomplete KGs (Saxena et al. 2020;).

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Section: Temporal Qa Data Setsmentioning

confidence: 99%

Section: Temporal Questionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Question Answering Over Temporal Knowledge Graphs

Saxena¹,

Chakrabarti²,

Talukdar³

2021

Preprint

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“…Semantic Parsing and Multi-task Learning Approaches Our work lies in the areas of semantic parsing and neural approaches for question answering over KGs. Works in [6,15,31,16] use neural approaches to solve the task of QA. [15] introduces an approach that splits the question into spans of tokens to match the tokens to their respective entities and predicates in the KG.…”

Section: Related Workmentioning

confidence: 99%

Context Transformer with Stacked Pointer Networks for Conversational Question Answering over Knowledge Graphs

Plepi¹,

Kacupaj²,

Singh³

et al. 2021

Preprint

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Neural semantic parsing approaches have been widely used for Question Answering (QA) systems over knowledge graphs. Such methods provide the flexibility to handle QA datasets with complex queries and a large number of entities. In this work, we propose a novel framework named CARTON (Context trAnsformeR sTacked pOinter Networks), which performs multi-task semantic parsing for handling the problem of conversational question answering over a large-scale knowledge graph. Our framework consists of a stack of pointer networks as an extension of a context transformer model for parsing the input question and the dialog history. The framework generates a sequence of actions that can be executed on the knowledge graph. We evaluate CARTON on a standard dataset for complex sequential question answering on which CARTON outperforms all baselines. Specifically, we observe performance improvements in F1-score on eight out of ten question types compared to the previous state of the art. For logical reasoning questions, an improvement of 11 absolute points is reached.

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“…Given a relational phrase and two entities, one has to find the best KG path that connects these entities. This paradigm has been extended to multi-hop QA [48,76]. While Conqer is inspired by some of these settings, the ConvQA problem is very different, with multiple entities from where agents could potentially walk and missing entities and relations in the conversational utterances.…”

Section: Related Workmentioning

confidence: 99%

Reinforcement Learning from Reformulations in Conversational Question Answering over Knowledge Graphs

Kaiser,

Roy,

Weikum

2021

Preprint

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The rise of personal assistants has made conversational question answering (ConvQA) a very popular mechanism for user-system interaction. State-of-the-art methods for ConvQA over knowledge graphs (KGs) can only learn from crisp question-answer pairs found in popular benchmarks. In reality, however, such training data is hard to come by: users would rarely mark answers explicitly as correct or wrong. In this work, we take a step towards a more natural learning paradigm -from noisy and implicit feedback via question reformulations. A reformulation is likely to be triggered by an incorrect system response, whereas a new follow-up question could be a positive signal on the previous turn's answer. We present a reinforcement learning model, termed Conqer, that can learn from a conversational stream of questions and reformulations. Conqer models the answering process as multiple agents walking in parallel on the KG, where the walks are determined by actions sampled using a policy network. This policy network takes the question along with the conversational context as inputs and is trained via noisy rewards obtained from the reformulation likelihood. To evaluate Conqer, we create and release ConvRef, a benchmark with about 11𝑘 natural conversations containing around 205𝑘 reformulations. Experiments show that Conqer successfully learns to answer conversational questions from noisy reward signals, significantly improving over a state-of-the-art baseline.

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Multi-Hop Reasoning for Question Answering with Knowledge Graph

Cited by 39 publications

References 7 publications

Question Answering Over Temporal Knowledge Graphs

Question Answering Over Temporal Knowledge Graphs

Context Transformer with Stacked Pointer Networks for Conversational Question Answering over Knowledge Graphs

Reinforcement Learning from Reformulations in Conversational Question Answering over Knowledge Graphs

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