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نتیجه جستجو - Knowledge graphs

تعداد مقالات یافته شده: 6
ردیف عنوان نوع
1 Image2Triplets: A computer vision-based explicit relationship extraction framework for updating construction activity knowledge graphs
Image2Triplets: چارچوب استخراج رابطه صریح مبتنی بر بینایی ماشین برای به روز رسانی نمودارهای دانش فعالیت های ساخت-2022
Knowledge graph (KG) is an effective tool for knowledge management, particularly in the architecture, engineering and construction (AEC) industry, where knowledge is fragmented and complicated. However, research on KG updates in the industry is scarce, with most current research focusing on text-based KG updates. Considering the superiority of visual data over textual data in terms of accuracy and timeliness, the potential of computer vision technology for explicit relationship extraction in KG updates is yet to be ex- plored. This paper combines zero-shot human-object interaction detection techniques with general KGs to propose a novel framework called Image2Triplets that can extract explicit visual relationships from images to update the construction activity KG. Comprehensive experiments on the images of architectural dec- oration processes have been performed to validate the proposed framework. The results and insights will contribute new knowledge and evidence to human-object interaction detection, KG update and construc- tion informatics from the theoretical perspective. © 2022 Elsevier B.V. All rights reserved.
keywords: یادگیری شات صفر | تشخیص تعامل انسان و شی | بینایی ماشین| استخراج رابطه صریح | نمودار دانش | Zero-shot learning | Human-object interaction detection | Computer vision | Explicit relationship extraction | Knowledge graph
مقاله انگلیسی
2 Exploiting knowledge graphs in industrial products and services: A survey of key aspects, challenges, and future perspectives
بهره برداری از نمودارهای دانش در محصولات صنعتی و خدمات: بررسی جنبه های کلیدی، چالش ها و دیدگاه های آینده-2021
The rapid development of information and communication technologies has enabled a value co-creation paradigm for developing industrial products and services, where massive heterogeneous data and multidisciplinary knowledge are generated and leveraged. In this context, Knowledge Graph (KG) emerges as a promising tool to elicit, fuse, process, and utilize numerous entities and relationships embedded in products and services, as well as their stakeholders. Nevertheless, to the best of the authors’ knowledge, there is scarcely any comprehensive and thorough discussion about making full use of KG’s potentials to solve pain points of product development and service innovation in the industry. Aiming to fill this gap, this paper conducted a systematic survey of KG exploitations in industrial products and services and the customizations towards higher adaptability to practices. The authors selected 119 representative papers (up to 10/03/2021) together with other 29 supplementary works to summarize the technical and practical efforts and discuss the current challenges of exploiting KG in industrial products and services. Meantime, this work also highlights enhancing KG’s availability and boosting its productivity in industrial products and services development as the core future perspectives to explore. It is hoped that this work can provide a basis for the explorations and implementations of KG-supported industrial product and services development, and attract more open discussions to the exploitation of KG-enabled industrial information systems.
keywords: گراف دانش | توسعه محصول | نوآوری خدمات | مدیریت دانش | سیستم های خدمات محصول | مرور | Knowledge graph | Product development | Service innovation | Knowledge management | Product-service systems | Review
مقاله انگلیسی
3 Data science with Vadalog: Knowledge Graphs with machine learning and reasoning in practice
علم داده با Vadalog: نمودارهای دانش با یادگیری ماشینی و استدلال در عمل-2021
Following the recent successful examples of large technology companies, many modern enterprises seek to build Knowledge Graphs to provide a unified view of corporate knowledge, and to draw deep insights using machine learning and logical reasoning. There is currently a perceived disconnect between the traditional approaches for data science, typically based on machine learning and statistical modeling, and systems for reasoning with domain knowledge. In this paper, we demonstrate how to perform a broad spectrum of data science tasks in a unified Knowledge Graph environment. This includes data wrangling, complex logical and probabilistic reasoning, and machine learning. We base our work on the state-of-the-art Knowledge Graph Management System Vadalog, which delivers highly expressive and efficient logical reasoning and provides seamless integration with modern data science toolkits such as the Jupyter platform. We argue that this is a significant step forward towards practical, holistic data science workflows that combine machine learning and reasoning in data science.
keywords: گراف دانش | علم داده | یادگیری ماشین | استدلال | استدلال احتمالی | Knowledge Graphs | Data science | Machine learning | Reasoning | Probabilistic reasoning
مقاله انگلیسی
4 Transformation of semantic knowledge into simulation-based decision support
تحول دانش معنایی به پشتیبانی تصمیم گیری مبتنی بر شبیه سازی-2021
Simulation is capable to cope with the uncertain and dynamic nature of industrial value chains. However, indepth system expertise is inevitable for mapping objects and constraints from the real world to a virtual model. This knowledge-intensity leads to long development times of respective projects, which contradicts the need for timely decision support. Since more and more companies use industrial knowledge graphs and ontologies to foster their knowledge management, this paper proposes a framework on how to efficiently derive a simulation model from such semantic knowledge bases. As part of the approach, a novel Simulation Ontology provides a standardized meta-model for hybrid simulations. Its instantiation enables the user to come up with a fully parameterized formal simulation model. Newly developed Mapping Rules facilitate this process by providing guidance on how to turn knowledge from existing ontologies, which describe the system to be simulated, into instances of the Simulation Ontology. The framework is completed by a parsing procedure for an automated transformation of this conceptual model into an executable one. This novel modeling approach makes model development more efficient by reducing its complexity. It is validated in a use case implementation from semiconductor manufacturing, where cross-domain knowledge was required in order to model and simulate the impacts of the COVID-19 pandemic on a global supply chain network.
keywords: تحول دانش | پشتیبانی تصمیم | هستی شناسی | مدل سازی ترکیبی | شبیه سازی همه گیر | شبیه سازی زنجیره تامین | Knowledge Transformation | Decision Support | Ontologies | Hybrid modeling | Pandemic Simulation | Supply chain simulation
مقاله انگلیسی
5 Geo-semantic-parsing: AI-powered geoparsing by traversing semantic knowledge graphs
تجزیه جغرافیایی-معنایی: تجزیه و تحلیل ژئوپارسی با هوش مصنوعی با عبور از نمودارهای دانش معنایی-2020
Online social networks convey rich information about geospatial facets of reality. However in most cases, geographic information is not explicit and structured, thus preventing its exploitation in real-time applications. We address this limitation by introducing a novel geoparsing and geotagging technique called Geo-Semantic- Parsing (GSP). GSP identifies location references in free text and extracts the corresponding geographic coordinates. To reach this goal, we employ a semantic annotator to identify relevant portions of the input text and to link them to the corresponding entity in a knowledge graph. Then, we devise and experiment with several efficient strategies for traversing the knowledge graph, thus expanding the available set of information for the geoparsing task. Finally, we exploit all available information for learning a regression model that selects the best entity with which to geotag the input text. We evaluate GSP on a well-known reference dataset including almost 10 k event-related tweets, achieving F1=0.66. We extensively compare our results with those of 2 baselines and 3 state-of-the-art geoparsing techniques, achieving the best performance. On the same dataset, competitors obtain F1 ≤ 0.55. We conclude by providing in-depth analyses of our results, showing that the overall superior performance of GSP is mainly due to a large improvement in recall, with respect to existing techniques.
Keywords: Geoparsing | Geotagging | Artificial intelligence | Knowledge graphs | Twitter
مقاله انگلیسی
6 Bridging the gap between linked open data-based recommender systems and distributed representations
ایجاد شکاف بین سیستمهای پیشنهادی مبتنی بر داده باز و پیوندهای داده شده توزیع شده-2019
Recently, several methods have been proposed for introducing Linked Open Data (LOD) into recommender systems. LOD can be used to enrich the representation of items by leveraging RDF statements and adopting graph-based methods to implement effective recommender systems. However, most of those methods do not exploit embeddings of entities and relations built on knowledge graphs, such as datasets coming from the LOD. In this paper, we propose a novel recommender system based on holographic embeddings of knowledge graphs built from Wikidata, a free and open knowledge base that can be read and edited by both humans and machines. The evaluation performed on three standard datasets such as Movielens 1M, Last.fm and LibraryThing shows promising results, which confirm the effectiveness of the proposed method.
Keywords: Recommender systems | Knowledge graph embedding | Linked data
مقاله انگلیسی
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