QuerioDALI: Question Answering Over Dynamic and Linked Knowledge Graphs

López, Vanessa; Tommasi, Pierpaolo; Kotoulas, Spyros; Wu, Jiang

doi:10.1007/978-3-319-46547-0_32

Cited by 17 publications

(11 citation statements)

References 14 publications

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“…Finally a poorly addressed point is the interaction of QA systems with users. We are not aware of works that study the usability of open domain KB-based QA systems and there are a few works applying QA over KBs in real scenarios (Song et al 2015, Lopez et al 2016. The interaction with the user can also be a good opportunity to improve QA systems over time and to collect training data which is becoming more and more important to improve the performance and understanding of these systems.…”

Section: Discussionmentioning

confidence: 99%

Core techniques of question answering systems over knowledge bases: a survey

et al. 2017

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Abstract. The Semantic Web contains an enormous amount of information in the form of knowledge bases (KB). To make this information available, many question answering (QA) systems over KBs were created in the last years. Building a QA system over KBs is difficult because there are many different challenges to be solved. In order to address these challenges, QA systems generally combine techniques from natural language processing, information retrieval, machine learning and Semantic Web. The aim of this survey is to give an overview of the techniques used in current QA systems over KBs. We present the techniques used by the QA systems which were evaluated on a popular series of benchmarks: Question Answering over Linked Data (QALD). Techniques that solve the same task are first grouped together and then described. The advantages and disadvantages are discussed for each technique. This allows a direct comparison of similar techniques. Additionally, we point to techniques that are used over WebQuestions and SimpleQuestions, which are two other popular benchmarks for QA systems.

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Section: Discussionmentioning

confidence: 99%

Core techniques of question answering systems over knowledge bases: a survey

et al. 2017

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“…In the first step the authors have translated the natural language questions into SPARQL and then in the next step all the translated SPARQL's are evaluated by the system, which provides the answer of the question. IBM based researches proposed Question Answering system [26] to sequentially perform linguistic analysis of query, do named entity extraction, entity / graph search, fusion and ranking of possible answers. Our research is also following the similar approach.…”

Section: Question Answeringmentioning

confidence: 99%

“…The multi-labeling and Edge Property is utilized in the above queries. In this work, similar approach is followed as followed in [26]. Presently, we have developed the interface in Visual Studio 2012 for querying Financial knowledge graph (KG) as displayed in Fig.…”

Section: Querying Over Knowledge Graphmentioning

confidence: 99%

Financial Knowledge Graph Based Financial Report Query System

et al. 2021

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Annual Financial Reports are the core in the Banking Sector to publish its financial statistics. Extracting useful information from these complex and lengthy reports involves manual process to resolve the financial queries, resulting in delays and ambiguity in investment decisions. One of the major reasons is the lack of any standardization in the format and vocabulary used in the reports. An automated system for resolution of intelligent financial queries is therefore difficult to design.Several works have been proposed to overcome these problems using Information Extraction; however, they do not address the semantic interoperability of the reports across different institutions. This work proposed an automated querying engine to answer the financial queries using Ontology based Information Extraction. For Semantic modeling of financial reports, a Financial Knowledge Graph, assisted by Financial Ontology, has been proposed. The nodes are populated with entities, while links are populated with relationships using Information Extraction applied on annual reports. Two benefits have been provided by this system to stakeholders through automation: decision making through queries and generation of custom financial stories. The work can further be extended to other domains including healthcare and academia where physical reports are used for communication.

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“…Reddy et al (2016) have employed the Freebase online API that was since deprecated. A number of question answering systems have relied on DBPedia Spotlight to extract entities (Lopez et al, 2016;Chen et al, 2016). DB-Pedia Spotlight (Mendes et al, 2011) uses document similarity vectors, word embeddings and manually defined features such as entity frequency.…”

Section: Motivation and Related Workmentioning

confidence: 99%

Mixing Context Granularities for Improved Entity Linking on Question Answering Data across Entity Categories

Sorokin¹,

Gurevych²

2018

Proceedings of the Seventh Joint Conference on Lexical And Computational Semantics

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The first stage of every knowledge base question answering approach is to link entities in the input question. We investigate entity linking in the context of a question answering task and present a jointly optimized neural architecture for entity mention detection and entity disambiguation that models the surrounding context on different levels of granularity.We use the Wikidata knowledge base and available question answering datasets to create benchmarks for entity linking on question answering data. Our approach outperforms the previous state-of-the-art system on this data, resulting in an average 8% improvement of the final score. We further demonstrate that our model delivers a strong performance across different entity categories.

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QuerioDALI: Question Answering Over Dynamic and Linked Knowledge Graphs

Cited by 17 publications

References 14 publications

Core techniques of question answering systems over knowledge bases: a survey

Core techniques of question answering systems over knowledge bases: a survey

Financial Knowledge Graph Based Financial Report Query System

Mixing Context Granularities for Improved Entity Linking on Question Answering Data across Entity Categories

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