KGVQL: A knowledge graph visual query language with bidirectional transformations

Liu, Pengkai; Wang, Xin; Qiang, Fu; Yang, Yajun; Li, Yuan Fang; Zhang, Qingpeng

doi:10.1016/j.knosys.2022.108870

Cited by 16 publications

(11 citation statements)

References 12 publications

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“…While visual interfaces for API querying were a part of databases, efforts are also on to design a visual query language for graphs. In a recently published work (Liu et al, 2022), an interactive visual query language called KGVQL has been proposed, that supports multiple operators and is independent of a low‐level graph query language. It can provide intermediate results and trajectories of query results to explore knowledge graphs and construct queries incrementally.…”

Section: Graph Databases For Storing Knowledge Graphsmentioning

confidence: 99%

Knowledge graph‐driven data processing for business intelligence

Dey

2024

WIREs Data Min & Knowl

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With proliferation of Big Data, organizational decision making has also become more complex. Business Intelligence (BI) is no longer restricted to querying about marketing and sales data only. It is more about linking data from disparate applications and also churning through large volumes of unstructured data like emails, call logs, social media, News, and so on in an attempt to derive insights that can also provide actionable intelligence and better inputs for future strategy making. Semantic technologies like knowledge graphs have proved to be useful tools that help in linking disparate data sources intelligently and also enable reasoning through complex networks that are created as a result of this linking. Over the last decade the process of creation, storage, and maintenance of knowledge graphs have sufficiently matured, and they are now making inroads into business decision making also. Very recently, these graphs are also seen as a potential way to reduce hallucinations of large language models, by including these during pre‐training as well as generation of output. There are a number of challenges also. These include building and maintaining the graphs, reasoning with missing links, and so on. While these remain as open research problems, we present in this article a survey of how knowledge graphs are currently used for deriving business intelligence with use‐cases from various domains.This article is categorized under: Algorithmic Development > Text Mining Application Areas > Business and Industry

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Section: Graph Databases For Storing Knowledge Graphsmentioning

confidence: 99%

Knowledge graph‐driven data processing for business intelligence

Dey

2024

WIREs Data Min & Knowl

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“…e.g. Deepmind RETRO [164], Facebook DrQA [165], FiDO [166], Atlas [167], training RL agents to query external knowledge [168], Toolformer [169].…”

Section: Strengthsmentioning

confidence: 99%

“…optimizing discrete text prompts [326], improving prompt in-context policy iteration [327]. (4) Information retrieval in reinforced browser-assisted QA with human feedback [56], knowledge-grounded QA [37], retrieval augmented process [255], answering with verified quotes [183], querying external knowledge [168], reasoning and acting in multiple search [157]. (5) Answer generation reinforcement mainly with instructions/expectation alignment (quality, safety, ambiguity...) in training a helpful and harmless assistant from human feedback [55], improving it with AI feedback [36], aligning with natural language goals [256], benchmarking with preference [328].…”

Section: Improvement Loop and Knowledge Capitalizationmentioning

confidence: 99%

Complex QA and language models hybrid architectures, Survey

Daull¹,

Bellot²,

Bruno³

et al. 2023

Preprint

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This paper provides a survey of the state of the art of hybrid language models architectures and strategies for "complex" question-answering (QA, CQA, CPS). Very large language models are good at leveraging public data on standard problems but once you want to tackle more specific complex questions or problems you may need specific architecture, knowledge, skills, tasks, methods, sensitive data, performance, human approval and versatile feedback... This survey extends findings from the robust community edited research papers BIG, BLOOM and HELM which open source, benchmark and analyze limits and challenges of large language models in terms of tasks complexity and strict evaluation on accuracy (e.g. fairness, robustness, toxicity, ...). It identifies the key elements used with Large Language Models (LLM) to solve complex questions or problems. Recent projects like ChatGPT and GALACTICA have allowed non-specialists to grasp the great potential as well as the equally strong limitations of language models in complex QA. Hybridizing these models with different components could allow to overcome these different limits and go much further. We discuss some challenges associated with complex QA, including domain adaptation, decomposition and efficient multi-step QA, long form QA, non-factoid QA, safety and multi-sensitivity data protection, multimodal search, hallucinations, QA explainability and truthfulness, time dimension. Therefore we review current solutions and promising strategies, using elements such as hybrid LLM architectures, human-in-the-loop reinforcement learning, prompting adaptation, neuro-symbolic and structured knowledge grounding, program synthesis, and others. We analyze existing solutions and provide an overview of the current research and trends in the area of complex QA.

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“…Zhang et al [42] propose the TransRHS approach, using relational structures to build a more complete knowledge graph. Liu et al [43] propose a Knowledge Graph Interactive Visual Query Language KGVQL to improve the understanding of knowledge graphs by end users. Knowledge Tracing (KT) can trace the state of evolutionary mastery for particular knowledge or concept, which can also construct a graph structure.…”

Section: Graph Neural Networkmentioning

confidence: 99%

EAGS: An extracting auxiliary knowledge graph model in multi-turn dialogue generation

et al. 2022

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Multi-turn dialogue generation is an essential and challenging subtask of text generation in the question answering system. Existing methods focused on extracting latent topic-level relevance or utilizing relevant external background knowledge. However, they are prone to ignore the fact that relying too much on latent aspects will lose subjective key information. Furthermore, there is not so much relevant external knowledge that can be used for referencing or a graph that has complete entity links. Dependency tree is a special structure that can be extracted from sentences, it covers the explicit key information of sentences. Therefore, in this paper, we proposed the EAGS model, which combines the subjective pivotal information from the explicit dependency tree with sentence implicit semantic information. The EAGS model is a knowledge graph enabled multi-turn dialogue generation model, and it doesn't need extra external knowledge, it can not only extract and build a dependency knowledge graph from existing sentences, but also prompt the node representation, which is shared with Bi-GRU each time step word embedding in node semantic level. We store the specific domain subgraphs built by the EAGS, which can be retrieved as external knowledge graph in the future multi-turn dialogue generation task. We design a multi-task training approach to enhance semantics and structure local feature extraction, and balance with the global features. Finally, we conduct experiments on Ubuntu large-scale English multi-turn dialogue community dataset and English Daily dialogue dataset. Experiment results show that our EAGS model performs well on both automatic evaluation and human evaluation compared with the existing baseline models.

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KGVQL: A knowledge graph visual query language with bidirectional transformations

Cited by 16 publications

References 12 publications

Knowledge graph‐driven data processing for business intelligence

Knowledge graph‐driven data processing for business intelligence

Complex QA and language models hybrid architectures, Survey

EAGS: An extracting auxiliary knowledge graph model in multi-turn dialogue generation

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