Domain-specific Knowledge Graphs: A survey

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1. Apparenté Pas pertinent

   Construction and Application of Teaching System Based on Crowdsourcing Knowledge Graph

   Jinta Weng, Ying Gao, Jing Qiu et al. · 2020 · 4th China Conference on Knowledge Graph and Semantic Computing, CCKS 2019 · Résumé IA disponible

   Résumé scientifique

   Through the combination of crowdsourcing knowledge graph and teaching system, research methods to generate knowledge graph and its applications. Using two crowdsourcing approaches, crowdsourcing task distribution and reverse captcha generation, to construct knowledge graph in the field of teaching system. Generating a complete hierarchical knowledge graph of the teaching domain by nodes of school, student, teacher, course, knowledge point and exercise type. The knowledge graph constructed in a crowdsourcing manner requires many users to participate collaboratively with fully consideration of teachers' guidance and users' mobilization issues. Based on the three subgraphs of knowledge graph, p…

2. Apparenté Pas pertinent

   Neural Graph Reasoning: Complex Logical Query Answering Meets Graph Databases

   Hongyu Ren, Mikhail Galkin, Michael Cochez et al. · 2023 · Bases de données · Pas encore résumé

   Résumé scientifique

   Complex logical query answering (CLQA) is a recently emerged task of graph machine learning that goes beyond simple one-hop link prediction and solves a far more complex task of multi-hop logical reasoning over massive, potentially incomplete graphs in a latent space. The task received a significant traction in the community; numerous works expanded the field along theoretical and practical axes to tackle different types of complex queries and graph modalities with efficient systems. In this paper, we provide a holistic survey of CLQA with a detailed taxonomy studying the field from multiple angles, including graph types (modality, reasoning domain, background semantics), modeling aspects (e…

3. Apparenté Pas pertinent

   An Introduction to Knowledge Management

   Sabu M. Thampi · 2008 · Bases de données · Résumé IA disponible

   Résumé scientifique

   Knowledge has been lately recognized as one of the most important assets of organizations. Managing knowledge has grown to be imperative for the success of a company. This paper presents an overview of Knowledge Management and various aspects of secure knowledge management. A case study of knowledge management activities at Tata Steel is also discussed

4. Apparenté Pas pertinent

   Context-based Ontology Modelling for Database: Enabling ChatGPT for Semantic Database Management

   Wenjun Lin, Paul Babyn, Yan yan et al. · 2023 · Bases de données · Résumé IA disponible

   Résumé scientifique

   This research paper explores the use of ChatGPT in database management. ChatGPT, an AI-powered chatbot, has limitations in performing tasks related to database management due to the lack of standardized vocabulary and grammar for representing database semantics. To address this limitation, the paper proposes a solution that involves developing a set of syntaxes that can represent database semantics in natural language. The syntax is used to convert database schemas into natural language formats, providing a new application of ChatGPT in database management. The proposed solution is demonstrated through a case study where ChatGPT is used to perform two tasks, semantic integration, and tables …

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   Online Analytical Processsing on Graph Data

   Leticia Gómez, Bart Kuijpers, Alejandro Vaisman · 2019 · Bases de données · Pas encore résumé

   Résumé scientifique

   Online Analytical Processing (OLAP) comprises tools and algorithms that allow querying multidimensional databases. It is based on the multidimensional model, where data can be seen as a cube such that each cell contains one or more measures that can be aggregated along dimensions. In a Big Data scenario, traditional data warehousing and OLAP operations are clearly not sufficient to address current data analysis requirements, for example, social network analysis. Furthermore, OLAP operations and models can expand the possibilities of graph analysis beyond the traditional graph-based computation. Nevertheless, there is not much work on the problem of taking OLAP analysis to the graph data mode…

6. Apparenté Pas pertinent

   ProvGen: generating synthetic PROV graphs with predictable structure

   Hugo Firth, Paolo Missier · 2014 · Bases de données · Résumé IA disponible

   Résumé scientifique

   This paper introduces provGen, a generator aimed at producing large synthetic provenance graphs with predictable properties and of arbitrary size. Synthetic provenance graphs serve two main purposes. Firstly, they provide a variety of controlled workloads that can be used to test storage and query capabilities of provenance management systems at scale. Secondly, they provide challenging testbeds for experimenting with graph algorithms for provenance analytics, an area of increasing research interest. provGen produces PROV graphs and stores them in a graph DBMS (Neo4J). A key feature is to let users control the relationship makeup and topological features of the graph, by providing a seed pro…

7. Apparenté Pas pertinent

   DB-GPT: Empowering Database Interactions with Private Large Language Models

   Siqiao Xue, Caigao Jiang, Wenhui Shi et al. · 2023 · Bases de données · Résumé IA disponible

   Résumé scientifique

   The recent breakthroughs in large language models (LLMs) are positioned to transition many areas of software. Database technologies particularly have an important entanglement with LLMs as efficient and intuitive database interactions are paramount. In this paper, we present DB-GPT, a revolutionary and production-ready project that integrates LLMs with traditional database systems to enhance user experience and accessibility. DB-GPT is designed to understand natural language queries, provide context-aware responses, and generate complex SQL queries with high accuracy, making it an indispensable tool for users ranging from novice to expert. The core innovation in DB-GPT lies in its private LL…

8. Apparenté Pas pertinent

   The Complexity of Why-Provenance for Datalog Queries

   Marco Calautti, Ester Livshits, Andreas Pieris et al. · 2023 · Bases de données · Résumé IA disponible

   Résumé scientifique

   Explaining why a database query result is obtained is an essential task towards the goal of Explainable AI, especially nowadays where expressive database query languages such as Datalog play a critical role in the development of ontology-based applications. A standard way of explaining a query result is the so-called why-provenance, which essentially provides information about the witnesses to a query result in the form of subsets of the input database that are sufficient to derive that result. To our surprise, despite the fact that the notion of why-provenance for Datalog queries has been around for decades and intensively studied, its computational complexity remains unexplored. The goal o…