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A novel Data-Driven framework based on BIM and knowledge graph for automatic model auditing and Quantity Take-off

ADVANCED ENGINEERING INFORMATICS 2022年第0期54卷

作者： Liu, Hao Cheng, Jack C. P. Gan, Vincent J. L. Zhou, Shanjing Hong Kong Univ Sci & Technol Dept Civil & Environm Engn Hong Kong 999077 Peoples R China Natl Univ Singapore Dept Built Environm Singapore 117566 Singapore Imperial Coll London Ctr Syst Engn & Innovat Dept Civil & Environm Engn London SW7 2AZ England

Model auditing is a critical step before conducting Building Information Modeling (BIM)-based Quantity Take-off (QTO) because these models may contain various human errors and mistakes, leading to insufficient semantic information and inconsistent modeling style in BIM models. The traditional object-oriented approach has diffi-culties in representing unstructured BIM data (e.g., interrelationships), while rule-based methods involve tremendous human efforts to develop rule sets, lacking flexibility for different requirements. Therefore, this study aims to establish a novel data-driven framework based on BIM and knowledge graph (KG) to represent un-structured BIM data for automatic inferences of auditing results of BIM model mistakes. It starts by establishing a BIM-KG data model via identifying required information for auditing purposes. Subsequently, BIM data is automatically transformed into the BIM-KG representations, the embeddings of which are trained using a knowledge graph embedding model. Automatic mechanisms are then developed to utilize the computable em-beddings to effectively identify mistake BIM elements. The framework is validated using illustrative examples and the results show that 100% mistake elements can be identified successfully without human intervention.

关键词： Building information modeling Quantity take -off BIM information quality BIM model auditing knowledge graph embedding

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TeReKG: A temporal collaborative knowledge graph framework for software team recommendation

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knowledge-BASED SYSTEMS 2024年 289卷

作者： Ruenin, Pisol Choetkiertikul, Morakot Supratak, Akara Tuarob, Suppawong Mahidol Univ Fac Informat & Commun Technol Nakhon Pathom Thailand

Successful software development requires a cohesive team with the right mix of technical skills and the ability to collaborate effectively. However, forming a software team that can execute tasks with precision and efficiency requires a deep understanding of each member's competence, experience, and cooperation history. Previously, automated software team selection has evaluated technical skills, cohesion, and cooperation history. However, the previous method had some limitations. Particularly, local features directly calculated from team members were subjective to the researchers' views, and the method ignored the temporal aspect of open -source software development. To overcome these limitations, this paper proposes a knowledge-graph software team recommendation framework called TeReKG. This framework encapsulates temporal collaboration patterns and uses a temporal knowledge graph to encode software collaboration history, technical abilities, task dependencies, and project structure. TeReKG was against state -of -the -art team recommendation algorithms using three popular open -source software projects: Moodle, Apache, and Atlassian. The evaluation results show that TeReKG outperforms the state -of -the -art baselines in both single-role and team recommendation tasks. These findings demonstrate that knowledge graph embedding can be effectively utilized in automated recommendation tasks in software engineering. Additionally, this highlights the potential for knowledge graphs to capture global information that can benefit various software development applications, including impact prediction of software repositories, code clone detection, and source code retrieval.

关键词： Software team recommendation knowledge graph embedding graph representation learning Collaborative software development

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Relation-aware collaborative autoencoder for personalized multiple facet selection

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knowledge-BASED SYSTEMS 2022年 246卷 1页

作者： Chantamunee, Siripinyo Wong, Kok Wai Fung, Chun Che Murdoch Univ Discipline Informat Technol Perth Australia Walailak Univ Sch Engn & Technol Nakhon Si Thammarat Thailand Walailak Univ Informat Innovat Ctr Excellence Nakhon Si Thammarat Thailand

Collaborative-based personalization has been one of the most successful techniques used in building personalization for recommender systems and facet selection. The technique predicts users' interests based on the preferences of similar people or items. The prediction is usually made on one single group of users or items/facets. However, multiple facet selection creates a different challenge where the prediction needs to be based on the similarity among different groups of users and facets. In conventional collaborative approach, user-facet representation is created from the concatenation of user preferences on each facet. This creates a spared representation which affects the accuracy of the personalized model. It is essential to develop a more suitable representation that effectively represents the collaborative preferences given across multiple facets and a predictive model to estimate the possible preferences across those groups. Multiple facets appear to be correlated to each other and this can be useful for associating the existing preferences. None of the previous works has addressed the issue due to the association of facet relationships. Hence, this paper aims to examine the effectiveness of a new approach that utilizes multiple-facet relationships to associate the collaborative interests across different facets. This study proposes a new collaborative-based personalization model for multiple facet selection, called Relation-aware Collaborative Autoencoder (RCAE) Model. A new embedding methodology was introduced for incorporating multiple facet relationships into user-facet interaction. Evaluations based on four real-world datasets demonstrated that the proposed model utilizing facet relationships has achieved significant improvement over the conventional collaborative approach. (C)& nbsp;2022 Elsevier B.V. All rights reserved.

关键词： Multiple facets Personalized facet selection Collaborative-based personalization Autoencoder Deep neural network knowledge graph embedding

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Negative sampling and rule mining for explainable link prediction in knowledge graphs

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knowledge-BASED SYSTEMS 2022年第0期250卷

作者： Islam, Kamrul Aridhi, Sabeur Smail-Tabbone, Malika Univ Lorraine CNRS Inria LORIA F-54000 Nancy France

Several KG embedding methods were proposed to learn low dimensional vector representations of entities and relations of a KG. Such representations facilitate the link prediction task, in the service of inference and KG completion. In this context, it is important to achieve both an efficient KG embedding and explainable predictions. During learning of efficient embeddings, sampling negative triples was highlighted as an important step as KGs only have observed positive triples. We propose an efficient simple negative sampling (SNS) method based on the assumption that the entities which are closer in the embedding space to the corrupted entity are able to provide high-quality negative triples. As for explainability, it actually constitutes a thriving research question especially when it comes to analyze KGs with their rich semantics rooted in description logics. Hence, we propose in this paper a new rule mining method on the basis of learned embeddings. We extensively evaluate our proposals through several experiments. We evaluate our SNS sampling method plugged to several KG embedding models through link prediction task performances on well-known datasets. Experimental results show that the SNS improves the prediction performance of KG embedding models, and outperforms the existing sampling methods. To assess the performance of our rule mining method with and without SNS, we mine and evaluate rules on three popular datasets. The extracted rules are evaluated as knowledge nuggets extracted from the KG and also as support for explainable link prediction. The overall results are good and open the way to many improvements and new perspectives. (c) 2022 Elsevier B.V. All rights reserved.

关键词： knowledge graph embedding Link prediction Negative sampling Rule mining Explainability

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Combining embedding-based and symbol-based methods for entity alignment

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PATTERN RECOGNITION 2022年 124卷 108433-108433页

作者： Jiang, Tingting Bu, Chenyang Zhu, Yi Wu, Xindong Hefei Univ Technol Minist Educ Key Lab Knowledge Engn Big Data Hefei Peoples R China Hefei Univ Technol Sch Comp Sci & Informat Engn Hefei Peoples R China Hefei Univ Technol Inst Big Knowledge Sci Hefei Peoples R China Yangzhou Univ Sch Informat Engn Yangzhou Jiangsu Peoples R China Mininglamp Technol Mininglamp Acad Sci Beijing Peoples R China

The objective of entity alignment is to judge whether entities refer to the same object in the real world. Methods for entity alignment can be grossly divided into two groups: conventional symbol-based entity alignment methods and embedding-based entity alignment methods. Both groups of methods have advantages and disadvantages (which are detailed in Section 1). Therefore, combining the advantages of both methods might be a promising strategy. However, to the best of our knowledge, only the RTEA algorithm that was proposed in our previous conference paper (Proceeding of Pacific Rim International Conference on Artificial Intelligence, pp. 162-175, 2019) utilizes this strategy for entity alignment. This manuscript is an extended version of that conference paper, in which an improved algorithm, namely, ESEA (combining embedding-based and symbol-based methods for entity alignment), is proposed based on the following steps. First, a novel method for combining embedding models with symbol-based models is proposed. Entities with high vector similarities are obtained through a hybrid embedding model, and the final aligned entity pairs are calculated via symbol-based methods. Second, a series of symbol based methods, instead of only the edit distance method in the original version, are combined with embedding-based methods for relation alignment. Third, we combine symbol-based and embedding based methods in a more complicated framework with the objective of better exploiting the advantages of both methods. The experimental results on real-world datasets demonstrate that the proposed method outperformed several state-of-the-art embedding-based entity alignment approaches and outperformed our previous RTEA method.(c) 2021 Elsevier Ltd. All rights reserved.

关键词： Entity alignment knowledge graph embedding String Similarity

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A Multi-View Filter for Relation-Free knowledge graph Completion

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BIG DATA RESEARCH 2023年 33卷

作者： Li, Juan Zhang, Wen Yu, Hongtao Zhejiang Univ Coll Comp Sci & Technol 38 Zheda Rd Hangzhou 310007 Peoples R China Zhejiang Univ Sch Software Technol 1689 Jiangnan Rd Ningbo 315048 Peoples R China

As knowledge graphs are often incomplete, knowledge graph completion methods have been widely proposed to infer missing facts by predicting the missing element of a triple given the other two elements. However, the assumption that the two elements have to be correlated is strong. Thus in this paper, we investigate relation-free knowledge graph completion to predict relation-tail(r-t) pairs given a head entity. Considering the large scale of candidate relation-tail pairs, previous work proposed to filter r-t pairs before ranking them relying on entity types, which fails when entity types are missing or insufficient. To tackle the limitation, we propose a relation-free knowledge graph completion method that can cope with knowledge graphs without additional ontological information, such as entity types. Specifically, we propose a multi-view filter, including two intra-view modules and an inter-view module, to filter r-t pairs. For the intra-view modules, we construct head-relation and tail-relation graphs based on triples. Two graph neural networks are respectively trained on these two graphs to capture the correlations between the head entities and the relations, as well as the tail entities and the relations. The inter-view module is learned to bridge the embeddings of entities that appeared in the two graphs. In terms of ranking, existing knowledge graph embedding models are applied to score and rank the filtered candidate r-t pairs. Experimental results show the efficiency of our method in preserving higher-quality candidate r-t pairs for knowledge graphs and resulting in better relation-free knowledge graph completion.& COPY;2023 Elsevier Inc. All rights reserved.

关键词： knowledge graph knowledge graph embedding Relation-free knowledge graph completion graph neural networks

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embedding-Based Entity Alignment of Cross-Lingual Temporal knowledge graphs

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NEURAL NETWORKS 2024年 172卷 106143页

作者： Bai, Luyi Li, Nan Li, Guishun Zhang, Ziyi Zhu, Lin Northeastern Univ Qinhuangdao Sch Comp & Commun Engn Qinhuangdao 066004 Peoples R China

Entity alignment aims to construct a complete knowledge graph (KG) by matching the same entities in multi-source KGs. Existing researches on entity alignment mainly focuses on static multi-relational data in knowledge graphs. However, the relationships or attributes between entities often possess temporal characteristics as well. Neglecting these temporal characteristics can frequently lead to alignment errors. Compared to studying entity alignment in temporal knowledge graphs, there are relatively few efforts on entity alignment in cross-lingual temporal knowledge graphs. Therefore, in this paper, we put forward an entity alignment method for cross-lingual temporal knowledge graphs, namely CTEA. Based on GCN and TransE, CTEA combines entity embeddings, relation embeddings and attribute embeddings to design a joint embedding model, which is more conducive to generating transferable entity embedding. In the meantime, the distance calculation between elements and the similarity calculation of entity pairs are combined to enhance the reliability of cross-lingual entity alignment. Experiments shows that the proposed CTEA model improves Hits@m and MRR by about 0.8 similar to 2.4 percentage points compared with the latest methods.

关键词： Cross -lingual Entity alignment graph Neural Networks knowledge graph embedding Temporal knowledge graph

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Is diversity optimization always suitable? Toward a better understanding of diversity within recommendation approaches

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INFORMATION PROCESSING & MANAGEMENT 2021年第6期58卷 102721-102721页

作者： Du, Yu Ranwez, Sylvie Sutton-Charani, Nicolas Ranwez, Vincent Univ Montpellier EuroMov Digital Hlth Mot IMT Mines Ales Ales France Univ Montpellier UMR AGAP Inst Inst Agro CIRADINRAE F-34398 Montpellier France

The diversity of the item list suggested by recommender systems has been proven to impact user satisfaction significantly. Most of the existing diversity optimization approaches re-rank the list of candidate items during a post-processing step. However, the diversity level of the candidate list strongly depends on the recommender system used. Hence, applying the same post-processing diversification strategy may not be as effective for different recommendation approaches. Moreover, individual users' diversity needs are usually ignored in the diversification post-processing. This article aims at providing an in-depth analysis of the diversity performances of different recommender systems. To the best of our knowledge, it is the first study to systematically compare diversity performances of the main types of recommendation models using benchmark datasets in different domains (movie, anime and book). Semantics related to items may be considered a key factor in measuring diversity within recommender systems. In this study, we leverage support from the knowledge engineering domain and take advantage of resources such as Linked Data and knowledge graphs, to assert the diversity of recommendations. We also propose a variant of the classic diversification post-processing objective that allows to take into account specific users' diversity needs. We measure the adequacy between the diversity levels a recommender system suggests to its users and those of users' profiles with the R-2 coefficient of determination. Our study indicates that (1) none of the tested recommender systems, even the most recent ones, provides items with levels of diversity that suit user profiles (R 2 < 0.2);(2) the classic post-processing diversification approach may lead to over-diversification compared to users' diversity needs and (3) the diversity adjustment that accounts for user profiles has more benefits (greater R-2 and smaller accuracy loss). All the source code and datasets used in our study

关键词： Recommender system Diversity Greedy optimization DBpedia knowledge graph embedding Deep learning

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PRGNN: Modeling high-order proximity with relational graph neural network for knowledge graph completion

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NEUROCOMPUTING 2024年 594卷

作者： Zhu, Danhao Jiangsu Police Inst Criminal Sci & Technol 48 Shifo SAN Gong Nanjing 210031 Jiangsu Peoples R China

Relational graph Neural Networks (RGNNs) are designed to extract structural information from relational graphs and have garnered attention in the domain of knowledge graph Completion (KGC). However, recent empirical investigations have indicated that some prominent RGNN-based methodologies have not significantly enhanced precision, prompting questions regarding the efficacy of RGNNs in KGC applications. In this paper, we introduce a novel RGNN-based KGC approach, the Proximity Relational graph Neural Network (PRGNN), which excels at modeling high -order proximities among entities. PRGNN is founded on a notably straightforward yet effective RGNN framework that discards unnecessary components commonly incorporated in previous approaches, such as attention layers, and linear and non-linear mappings. We demonstrate that PRGNN empowers traditional KGC techniques to apprehend high -order proximities among entities more effectively. Through extensive experimentation on benchmark datasets, we establish that PRGNN consistently outperforms conventional KGC methods and achieves state-of-the-art results. Furthermore, we show that PRGNN necessitates considerably less training time (ranging from one-third to one -fifth) and fewer parameters (ranging from half to two-thirds), rendering it an exceptionally efficient approach. All data and code have been made available at 1 .

关键词： knowledge graph completion knowledge graph embedding graph neural network

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A knowledge graphs representation method based on IsA relation modeling

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 254卷

作者： Zhang, Pengfei Chen, Dong Fang, Yang Xiao, Weidong Zhao, Xiang Tan, Zhen Wang, Rui Natl Univ Def Technol Changsha 410073 Peoples R China

knowledge graph representation learning, which is able to support further knowledge computation and reasoning, has drawn much research attention in the field of artificial intelligence in recent years. Due to data sparsity, traditional knowledge graph methods focusing on the observed facts have poor performance. Therefore, researchers introduce auxiliary information to tackle this issue. As a kind of prior knowledge containing rich semantics, IsA relation could be used to boost model performance. We propose a knowledge representation learning method combined with isA relation modeling. Specifically, we model two features of isA relation-transitivity and antisymmetry. The transitivity is modeled by the projection invariance of vectors, and antisymmetry is modeled by partial order constraints. We use RotatE as our base model which could also be replaced by other knowledge embedding model with higher dimension. Finally, experimental results of link prediction and triple classification task on two datasets verify the proposed model's effectiveness outperforming baseline models.

关键词： knowledge graph embedding IsA relation Partial order relation

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