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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 |
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CACDA: A knowledge graph for a context-aware cognitive design assistant
CACDA: یک گراف دانش برای دستیار طراحی شناختی زمینه آگاه-2021 The design of complex engineered systems highly relies on a laborious zigzagging between computeraided design (CAD) software and design rules prescribed by design manuals. Despite the emergence of
knowledge management techniques (ontology, expert system, text mining, etc.), companies continue to
store design rules in large and unstructured documents. To facilitate the integration of design rules and
CAD software, we propose a knowledge graph that structures a large set of design rules in a computable
format. The knowledge graph organises entities of design rules (nodes), relationships among design rules
(edges), as well as contextual information. The categorisation of entities and relationships in four subcontexts: semantic, social, engineering, and IT – facilitates the development of the data model, especially
the definition of the “design context” concept. The knowledge graph paves the way to a context-aware
cognitive design assistant. Indeed, connected to or embedded in a CAD software, a context-aware cognitive design assistant will capture the design context in near real time and run reasoning operations
on the knowledge graph to extend traditional CAD capabilities, such as the recommendation of design
rules, the verification of design solutions, or the automation of design routines. Our validation experiment shows that the current version of the context-aware cognitive design assistant is more efficient
than the traditional document-based design. On average, participants using an unstructured design rules
document have a precision of 0.36 whereas participants using our demonstrator obtain a 0.61 precision
score. Finally, designers supported by the design assistant spend more time designing than searching for
applicable design rules compared to the traditional design approach.
keywords: قانون طراحی | نمودار دانش | مدیریت دانش | آگاهی متقابل | دستیار شناختی | Design rule | Knowledge graph | Knowledge management | Context-awareness | Cognitive assistant |
مقاله انگلیسی |
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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 |
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A knowledge graph method for hazardous chemical management: Ontology design and entity identification
یک روش نمودار دانش برای مدیریت مواد شیمیایی خطرناک: طراحی هستی شناسی و شناسایی موجودیت-2021 Hazardous chemicals are widely used in the production activities of the chemical industry. The risk management of hazardous chemicals is critical to the safety of life and property. Hence, the effective risk management of hazardous chemicals has always been important to the chemical industry. Since a large
quantity of knowledge and information of hazardous chemicals is stored in isolated databases, it is challenging to manage hazardous chemicals in an information-rich manner. Herein, we prompt a knowledge
graph to overcome the information gap between decentralized databases, which would improve the hazardous chemical management. In the implementation of the knowledge graph, we design an ontology
schema of hazardous chemicals management. To facilitate enterprises to master the knowledge in the full
lifecycle of hazardous chemicals, including production, transportation, storage, etc., we jointly use data
from companies and open data from the public domain of hazardous chemicals to construct the knowledge graph. The named entity recognition task is one of the key tasks in the implementation of the knowledge graph, which is of great significance for extracting entity information from unstructured data,
namely the hazardous chemical accidents records. To extract useful information from multi-source data,
we adopt the pre-trained BERT-CRF model to conduct named entity recognition for incidents records. The
model achieves good results, exhibiting the effectiveness in the task of named entity recognition in the
chemical industry.
keywords: نمودار دانش | هستی شناسی | مدیریت مواد شیمیایی خطرناک | به رسمیت شناختن نهادها | Knowledge graph | Ontology | Hazardous chemicals management | Named entity recognition |
مقاله انگلیسی |
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MemoryPath: A Deep Reinforcement Learning Framework for Incorporating Memory Component into Knowledge Graph Reasoning
MemoryPath: یک چارچوب یادگیری تقویتی عمیق برای ترکیب مولفه حافظه در استدلال نمودار دانش-2020 Knowledge Graph (KG) is identified as a major area in artificial intelligence, which is used for many
real-world applications. The task of knowledge graph reasoning has been widely used and proven
to be effective, which aims to find these reasonable paths for various relations to solve the issue of
incompleteness in KGs. However, many previous works on KG reasoning, such as path-based or rein-
forcement learning-based methods, are too reliant on the pre-training, where the paths from the head
entity and the target entity must be given to pre-train the model, which would easily lead the model to
overfit on the given paths seen in the pre-training. To address this issue, we propose a novel reasoning
model named MemoryPath with a deep reinforcement learning framework, which incorporates Long
Short Term Memory(LSTM) and graph attention mechanism to form the memory component. The
well-designed memory component can get rid of the pre-training so that the model doesn’t depend on
the given target entity for training. A tailored mechanism of reinforcement learning is presented in
this proposed deep reinforcement framework to optimize the training procedure, where two metrics,
Mean Selection Rate (MSR) and Mean Alternative Rate (MAR), are defined to quantitatively mea-
sure the complexities of the query relations. Meanwhile, three different training mechanisms, Action
Dropout, Reward Shaping and Force Forward, are proposed to optimize the training process of the
proposed MemoryPath. The proposed MemoryPath is validated on two datasets from FB15K-237
and NELL-995 on different tasks including fact prediction, link prediction and success rate in finding
paths. The experimental results demonstrate that the tailored mechanism of reinforcement learning
make the MemoryPath achieves state-of-the-art performance comparing with the other models. Also,
the qualitative analysis indicates that the MemoryPath can store the learning process and automati-
cally find the promising paths for a reasoning task during the training, and shows the effectiveness of
the memory component. Keywords: Knowledge Graph Reasoning | Memory Component | Deep Reinforcement Learning | Link Prediction | Attentional Path | Force Forward |
مقاله انگلیسی |
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ADRL: An attention-based deep reinforcement learning framework for knowledge graph reasoning
ADRL: یک چارچوب یادگیری تقویتی عمیق مبتنی بر توجه برای استدلال نمودار دانش-2020 Knowledge graph reasoning is one of the key technologies for knowledge graph construction, which
plays an important part in application scenarios such as vertical search and intelligent question
answering. It is intended to infer the desired entity from the entities and relations that already exist in
the knowledge graph. Most current methods for reasoning, such as embedding-based methods, globally
embed all entities and relations, and then use the similarity of vectors to infer relations between
entities or whether given triples are true. However, in real application scenarios, we require a clear and
interpretable target entity as the output answer. In this paper, we propose a novel attention-based deep
reinforcement learning framework (ADRL) for learning multi-hop relational paths, which improves
the efficiency, generalization capacity, and interpretability of conventional approaches through the
structured perception of deep learning and relational reasoning of reinforcement learning. We define
the entire process of reasoning as a Markov decision process. First, we employ CNN to map the
knowledge graph to a low-dimensional space, and a message-passing mechanism to sense neighbor
entities at each level, and then employ LSTM to memorize and generate a sequence of historical
trajectories to form a policy and value functions. We design a relational module that includes a selfattention
mechanism that can infer and share the weights of neighborhood entity vectors and relation
vectors. Finally, we employ the actor–critic algorithm to optimize the entire framework. Experiments
confirm the effectiveness and efficiency of our method on several benchmark data sets. Keywords: Knowledge graph | Knowledge reasoning | Reinforcement learning | Deep learning | Attention |
مقاله انگلیسی |
7 |
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 |
8 |
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 |