Neural architecture for question answering using a knowledge graph and web corpus

Sawant, Uma; Garg, Saurabh; Chakrabarti, Soumen; Ramakrishnan, Ganesh

doi:10.1007/s10791-018-9348-8

Cited by 40 publications

(20 citation statements)

References 51 publications

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“…Starting with early approaches in 2012-'13 [5,36,38], based on parsing questions via handcoded templates and grammars, KG-QA already has a rich body of literature. While templates continued to be a strong line of work due to its focus on interpretability and generalizability [1,2,4,7,35], a parallel thread has focused on neural methods driven by performance gains [15,20,31]. Newer trends include shifts towards more complex questions [19,21,34], and fusion of knowledge graphs and text [31,33].…”

Section: Related Workmentioning

confidence: 99%

Look before you Hop: Conversational Question Answering over Knowledge Graphs Using Judicious Context Expansion

Christmann

Roy

Abujabal

et al. 2019

Proceedings of the 28th ACM International Conference on Information and Knowledge Management

118

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Fact-centric information needs are rarely one-shot; users typically ask follow-up questions to explore a topic. In such a conversational setting, the user's inputs are often incomplete, with entities or predicates left out, and ungrammatical phrases. This poses a huge challenge to question answering (QA) systems that typically rely on cues in full-fledged interrogative sentences. As a solution, we develop Convex: an unsupervised method that can answer incomplete questions over a knowledge graph (KG) by maintaining conversation context using entities and predicates seen so far and automatically inferring missing or ambiguous pieces for follow-up questions. The core of our method is a graph exploration algorithm that judiciously expands a frontier to find candidate answers for the current question. To evaluate Convex, we release ConvQuestions, a crowdsourced benchmark with 11, 200 distinct conversations from five different domains. We show that Convex: (i) adds conversational support to any stand-alone QA system, and (ii) outperforms state-of-the-art baselines and question completion strategies.

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Section: Related Workmentioning

confidence: 99%

Look before you Hop: Conversational Question Answering over Knowledge Graphs Using Judicious Context Expansion

Christmann

Roy

Abujabal

et al. 2019

Proceedings of the 28th ACM International Conference on Information and Knowledge Management

118

View full text Add to dashboard Cite

show abstract

“…These disadvantages may indicate which QA system is more suitable in a particular case (Gupta and Gupta 2012). Recent efforts aim to create hybrid QA systems that combine elements of both (Mitra et al 2019;Sawant et al 2019). In case of GIS, such hybrid systems may be most suitable.…”

Section: State Of the Artmentioning

confidence: 99%

Geo-analytical question-answering with GIS

Scheider

Nyamsuren

Kruiger

et al. 2020

International Journal of Digital Earth

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Question Answering (QA), the process of computing valid answers to questions formulated in natural language, has recently gained attention in both industry and academia. Translating this idea to the realm of geographic information systems (GIS) may open new opportunities for data scientists. In theory, analysts may simply ask spatial questions to exploit diverse geographic information resources, without a need to know how GIS tools and geodata sets interoperate. In this outlook article, we investigate the scientific challenges of geo-analytical question answering, introducing the problems of unknown answers and indirect QA. Furthermore, we argue why core concepts of spatial information play an important role in addressing this challenge, enabling us to describe analytic potentials, and to compose spatial questions and workflows for generating answers. ARTICLE HISTORY

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“…Microsoft's Satori and Google's Freebase are examples of this type. The KG is suitable for question answering and information search tasks [105]. An example of the knowledge graph is shown in figure 8.…”

Section: Graph-based Representation In Natural Language Processingmentioning

confidence: 99%

Graph-Based Text Representation and Matching: A Review of the State of the Art and Future Challenges

Osman

Barukub

2020

IEEE Access

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Graph-based text representation is one of the important preprocessing steps in data and text mining, Natural Language Processing (NLP), and information retrieval approaches. The graph-based methods focus on how to represent text documents in the shape of a graph to exploit the best features of their characteristics. This study reviews and lists the advantages and disadvantages of such methods employed or developed in graph-based text representations. The literature shows that some of the proposed graph-based methods suffer from a lack of representing texts in certain situations. Currently, several techniques are commonly used in graph-based text representation. However, there are still some weaknesses and shortages in these techniques and tools that significantly affect the success of graph representation and graph matching. In this review, we conduct an inclusive survey of the state of the art in graph-based text representation and learning. We provide a formal description of the problem of graph-based text representation and introduce some basic concepts. More significantly, this study proposes a new taxonomy of graph-based text representation, categorizing the existing studies based on representation characteristics and scheme techniques. In terms of the representation scheme taxonomy, we introduce four main types of conceptual graph schemes and summarize the challenges faced in each scheme. The main issues of graph representation, such as research topics and the sub-taxonomy of graph models for web documents, are introduced and categorized. This research also covers some tasks of understanding natural language processing (NLP) that depend on different types of graph structures. In addition, the graph matching taxonomy implements three main categories based on the matching approach, including structural-, semantic-, and similarity-based approaches. Moreover, a deep comparison of these approaches is discussed and reported in terms of methods and tools, the concepts of matching and locality, and the application domains that use these tools. Finally, the paper recommends seven promising future study directions in the graph-based text representation field. These recommendation points are summarized and highlighted as open problems and challenges of graphbased text representation and learning to facilitate and fill the research gaps for scientific researchers in this field.

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Neural architecture for question answering using a knowledge graph and web corpus

Cited by 40 publications

References 51 publications

Look before you Hop: Conversational Question Answering over Knowledge Graphs Using Judicious Context Expansion

Look before you Hop: Conversational Question Answering over Knowledge Graphs Using Judicious Context Expansion

Geo-analytical question-answering with GIS

Graph-Based Text Representation and Matching: A Review of the State of the Art and Future Challenges

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