Статті в журналах: "Graph extraction"

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Cooray, Thilini, and Ngai-Man Cheung. "Graph-Wise Common Latent Factor Extraction for Unsupervised Graph Representation Learning." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 6 (June 28, 2022): 6420–28. http://dx.doi.org/10.1609/aaai.v36i6.20593.

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Unsupervised graph-level representation learning plays a crucial role in a variety of tasks such as molecular property prediction and community analysis, especially when data annotation is expensive. Currently, most of the best-performing graph embedding methods are based on Infomax principle. The performance of these methods highly depends on the selection of negative samples and hurt the performance, if the samples were not carefully selected. Inter-graph similarity-based methods also suffer if the selected set of graphs for similarity matching is low in quality. To address this, we focus only on utilizing the current input graph for embedding learning. We are motivated by an observation from real-world graph generation processes where the graphs are formed based on one or more global factors which are common to all elements of the graph (e.g., topic of a discussion thread, solubility level of a molecule). We hypothesize extracting these common factors could be highly beneficial. Hence, this work proposes a new principle for unsupervised graph representation learning: Graph-wise Common latent Factor EXtraction (GCFX). We further propose a deep model for GCFX, deepGCFX, based on the idea of reversing the above-mentioned graph generation process which could explicitly extract common latent factors from an input graph and achieve improved results on downstream tasks to the current state-of-the-art. Through extensive experiments and analysis, we demonstrate that, while extracting common latent factors is beneficial for graph-level tasks to alleviate distractions caused by local variations of individual nodes or local neighbourhoods, it also benefits node-level tasks by enabling long-range node dependencies, especially for disassortative graphs.

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Yuan, Changsen, Heyan Huang, and Chong Feng. "Multi-Graph Cooperative Learning Towards Distant Supervised Relation Extraction." ACM Transactions on Intelligent Systems and Technology 12, no. 5 (October 31, 2021): 1–21. http://dx.doi.org/10.1145/3466560.

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The Graph Convolutional Network (GCN) is a universal relation extraction method that can predict relations of entity pairs by capturing sentences’ syntactic features. However, existing GCN methods often use dependency parsing to generate graph matrices and learn syntactic features. The quality of the dependency parsing will directly affect the accuracy of the graph matrix and change the whole GCN’s performance. Because of the influence of noisy words and sentence length in the distant supervised dataset, using dependency parsing on sentences causes errors and leads to unreliable information. Therefore, it is difficult to obtain credible graph matrices and relational features for some special sentences. In this article, we present a Multi-Graph Cooperative Learning model (MGCL), which focuses on extracting the reliable syntactic features of relations by different graphs and harnessing them to improve the representations of sentences. We conduct experiments on a widely used real-world dataset, and the experimental results show that our model achieves the state-of-the-art performance of relation extraction.

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WU, QINGHUA, and JIN-KAO HAO. "AN EXTRACTION AND EXPANSION APPROACH FOR GRAPH COLORING." Asia-Pacific Journal of Operational Research 30, no. 05 (October 2013): 1350018. http://dx.doi.org/10.1142/s0217595913500188.

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This paper presents an extraction and expansion approach for the graph coloring problem. The extraction phase transforms a large graph into a sequence of progressively smaller graphs by removing large independent sets from the graph. The expansion phase starts by generating an approximate coloring for the smallest graph in the sequence. Then it expands the smallest graph by progressively adding back the extracted independent sets and determine a coloring for each intermediate graph. To color each graph, a simple perturbation based tabu search algorithm is used. The proposed approach is evaluated on the DIMACS challenge benchmarks showing competitive results in comparison with the state-of-the-art methods.

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Rao, Bapuji, and Sarojananda Mishra. "A New Approach to Community Graph Partition Using Graph Mining Techniques." International Journal of Rough Sets and Data Analysis 4, no. 1 (January 2017): 75–94. http://dx.doi.org/10.4018/ijrsda.2017010105.

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Knowledge extraction is very much possible from the community graph using graph mining techniques. The authors have studied the related definitions of graph partition in terms of both mathematical as well as computational aspects. To derive knowledge from a particular sub-community graph of a large community graph, the authors start partitioning the large community graph into smaller sub-community graphs. Thus, the knowledge extraction from the sub-community graph becomes easier and faster. The proposed approach of partition is done by detection of edges among the community members of dissimilar community. By studying existing techniques followed by different researchers, the authors propose a new and simple algorithm for partitioning the community graph into sub-community graphs using graph mining techniques. Finally, the authors have considered a benchmark dataset as example which verifies the strength and easiness of the proposed algorithm.

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Huang, Xiayuan, Xiangli Nie, and Hong Qiao. "PolSAR Image Feature Extraction via Co-Regularized Graph Embedding." Remote Sensing 12, no. 11 (May 28, 2020): 1738. http://dx.doi.org/10.3390/rs12111738.

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Dimensionality reduction (DR) methods based on graph embedding are widely used for feature extraction. For these methods, the weighted graph plays a vital role in the process of DR because it can characterize the data’s structure information. Moreover, the similarity measurement is a crucial factor for constructing a weighted graph. Wishart distance of covariance matrices and Euclidean distance of polarimetric features are two important similarity measurements for polarimetric synthetic aperture radar (PolSAR) image classification. For obtaining a satisfactory PolSAR image classification performance, a co-regularized graph embedding (CRGE) method by combing the two distances is proposed for PolSAR image feature extraction in this paper. Firstly, two weighted graphs are constructed based on the two distances to represent the data’s local structure information. Specifically, the neighbouring samples are sought in a local patch to decrease computation cost and use spatial information. Next the DR model is constructed based on the two weighted graphs and co-regularization. The co-regularization aims to minimize the dissimilarity of low-dimensional features corresponding to two weighted graphs. We employ two types of co-regularization and the corresponding algorithms are proposed. Ultimately, the obtained low-dimensional features are used for PolSAR image classification. Experiments are implemented on three PolSAR datasets and results show that the co-regularized graph embedding can enhance the performance of PolSAR image classification.

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Ahmad, Jawad, Abdur Rehman, Hafiz Tayyab Rauf, Kashif Javed, Maram Abdullah Alkhayyal, and Abeer Ali Alnuaim. "Service Recommendations Using a Hybrid Approach in Knowledge Graph with Keyword Acceptance Criteria." Applied Sciences 12, no. 7 (March 31, 2022): 3544. http://dx.doi.org/10.3390/app12073544.

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Businesses are overgrowing worldwide; people struggle for their businesses and startups in almost every field of life, whether industrial or academic. The businesses or services have multiple income streams with which they generate revenue. Most companies use different marketing and advertisement strategies to engage their customers and spread their services worldwide. Service recommendation systems are gaining popularity to recommend the best services and products to customers. In recent years, the development of service-oriented computing has had a significant impact on the growth of businesses. Knowledge graphs are commonly used data structures to describe the relations among data entities in recommendation systems. Domain-oriented user and service interaction knowledge graph (DUSKG) is a framework for keyword extraction in recommendation systems. This paper proposes a novel method of chunking-based keyword extractions for hybrid recommendations to extract domain-specific keywords in DUSKG. We further show that the performance of the hybrid approach is better than other techniques. The proposed chunking method for keyword extraction outperforms the existing value feature entity extraction (VF2E) by extracting fewer keywords.

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LOURENS, TINO, and ROLF P. WÜRTZ. "EXTRACTION AND MATCHING OF SYMBOLIC CONTOUR GRAPHS." International Journal of Pattern Recognition and Artificial Intelligence 17, no. 07 (November 2003): 1279–302. http://dx.doi.org/10.1142/s0218001403002848.

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We describe an object recognition system based on symbolic contour graphs. The image to be analyzed is transformed into a graph with object corners as vertices and connecting contours as edges. Image corners are determined using a robust multiscale corner detector. Edges are constructed by line-following between corners based on evidence from the multiscale Gabor wavelet transform. Model matching is done by finding subgraph isomorphisms in the image graph. The complexity of the algorithm is reduced by labeling vertices and edges, whereby the choice of labels also makes the recognition system invariant under translation, rotation and scaling. We provide experimental evidence and theoretical arguments that the matching complexity is below O(#V3), and show that the system is competitive with other graph-based matching systems.

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Yoshikawa, Tomohiro, Yuki Uchida, Takeshi Furuhashi, Eiji Hirao, and Hiroto Iguchi. "Extraction of Evaluation Keywords for Analyzing Product Evaluation in User-Reviews Using Hierarchical Keyword Graph." Journal of Advanced Computational Intelligence and Intelligent Informatics 13, no. 4 (July 20, 2009): 457–62. http://dx.doi.org/10.20965/jaciii.2009.p0457.

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Recently, the number of sites on the Internet which give users the opportunity to write their ideas and opinions for the public to read have been increasing. In addition, the number of people who want to know the opinions of others about interesting products has also been increasing. However, it is very difficult for people to read complete reviews on the Internet. This study tries to develop a new system for the analysis of reviews, a system which shows evaluation information about products using graphs of evaluation keywords. This paper focuses on the extraction of evaluation keywords from reviews on the Internet. This paper proposes a method for extracting evaluation keywords and displays its results as graphs. It employs HK Graph (Hierarchical Keyword Graph), which can visualize the relationship among words in a hierarchical network structure based on the co-occurrence information for the keyword graph.

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Ouadid, Youssef, Abderrahmane Elbalaoui, Mehdi Boutaounte, Mohamed Fakir, and Brahim Minaoui. "Handwritten tifinagh character recognition using simple geometric shapes and graphs." Indonesian Journal of Electrical Engineering and Computer Science 13, no. 2 (February 1, 2019): 598. http://dx.doi.org/10.11591/ijeecs.v13.i2.pp598-605.

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In this paper, a graph based handwritten Tifinagh character recognition system is presented. In preprocessing Zhang Suen algorithm is enhanced. In features extraction, a novel key point extraction algorithm is presented. Images are then represented by adjacency matrices defining graphs where nodes represent feature points extracted by a novel algorithm. These graphs are classified using a graph matching method. Experimental results are obtained using two databases to test the effectiveness. The system shows good results in terms of recognition rate.

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Qu, Jia. "A Review on the Application of Knowledge Graph Technology in the Medical Field." Scientific Programming 2022 (July 20, 2022): 1–12. http://dx.doi.org/10.1155/2022/3212370.

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With the continuous development of Internet technology, knowledge graph construction has received increasing attention. Extracting useful medical knowledge from massive data is the key to analyzing big medical data. The knowledge graph is a semantic network that reveals relationships between entities. Medicine is one of the widely used fields of knowledge graphs, and the construction of a medical knowledge graph is also a research hotspot in artificial intelligence. Knowledge graph technology has broad application prospects in the field. First, this study comprehensively analyzes the structure and construction technology of the medical knowledge graph according to the characteristics of big data in the medical field, such as strong professionalism and complex structure. Second, this study summarizes the key technologies and research progress of the four modules of the medical knowledge graph: knowledge representation, knowledge extraction, knowledge fusion, and knowledge reasoning. Finally, with the major challenges and key problems of the current medical knowledge graph construction technology, its development prospects are prospects.

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Lai, Qinghan, Zihan Zhou, and Song Liu. "Joint Entity-Relation Extraction via Improved Graph Attention Networks." Symmetry 12, no. 10 (October 21, 2020): 1746. http://dx.doi.org/10.3390/sym12101746.

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Joint named entity recognition and relation extraction is an essential natural language processing task that aims to identify entities and extract the corresponding relations in an end-to-end manner. At present, compared with the named entity recognition task, the relation extraction task performs poorly on complex text. To solve this problem, we proposed a novel joint model named extracting Entity-Relations viaImproved Graph Attention networks (ERIGAT), which enhances the ability of the relation extraction task. In our proposed model, we introduced the graph attention network to extract entities and relations after graph embedding based on constructing symmetry relations. To mitigate the over-smoothing problem of graph convolutional networks, inspired by matrix factorization, we improved the graph attention network by designing a new multi-head attention mechanism and sharing attention parameters. To enhance the model robustness, we adopted the adversarial training to generate adversarial samples for training by adding tiny perturbations. Comparing with typical baseline models, we comprehensively evaluated our model by conducting experiments on an open domain dataset (CoNLL04) and a medical domain dataset (ADE). The experimental results demonstrate the effectiveness of ERIGAT in extracting entity and relation information.

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Leri, Marina, and Yury Pavlov. "Power-Law Random Graphs’ Robustness: Link Saving and Forest Fire Model." Austrian Journal of Statistics 43, no. 4 (June 11, 2014): 229–36. http://dx.doi.org/10.17713/ajs.v43i4.34.

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We consider random graphs with node degrees drawn independently from a power- law distribution. By computer simulation we study two aspects of graph robustness: preserving graph connectivity and node saving in the forest fire model, considering two types of graph destruction: the removal of nodes with the highest degrees and equiprobable node extraction.

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Bahtaji, Michael Allan A. "Improving students graphing skills and conceptual understanding using explicit Graphical Physics Instructions." Cypriot Journal of Educational Sciences 15, no. 4 (August 31, 2020): 843–53. http://dx.doi.org/10.18844/cjes.v15i4.5063.

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The study presented investigates the effects of supportive graphical interventions on the graphing skills and conceptual understanding of students in physics. In this study, the first group of participants was presented with ready-made graphs during the instruction, the second group was instructed on the proper construction and extraction of graphs, while the third group was instructed to construct graphs independently. The groups were compared with respect to their scores in the graphing skills and achievement tests before and after the instructions. The group that received supportive intervention in construction and extraction of graphs attained the highest number of high-level graphs constructed and obtained the highest increase in the achievement test scores after the instruction. The results revealed that the use of the supportive graphical intervention in the construction and extraction of graphs improved the graphing skills and conceptual understanding of students, especially for those who experienced difficulties in dealing graphs. Keywords: Graphical interventions, construction of graph, interpretation of graph, graphing skills, conceptual understanding;

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赵, 海霞. "Knowledge Graph Oriented Information Extraction." Hans Journal of Data Mining 10, no. 04 (2020): 282–302. http://dx.doi.org/10.12677/hjdm.2020.104030.

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Wang, Wenguang, Yonglin Xu, Chunhui Du, Yunwen Chen, Yijie Wang, and Hui Wen. "Data Set and Evaluation of Automated Construction of Financial Knowledge Graph." Data Intelligence 3, no. 3 (2021): 418–43. http://dx.doi.org/10.1162/dint_a_00108.

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With the technological development of entity extraction, relationship extraction, knowledge reasoning, and entity linking, the research on knowledge graph has been carried out in full swing in recent years. To better promote the development of knowledge graph, especially in the Chinese language and in the financial industry, we built a high-quality data set, named financial research report knowledge graph (FR2KG), and organized the automated construction of financial knowledge graph evaluation at the 2020 China Knowledge Graph and Semantic Computing Conference (CCKS2020). FR2KG consists of 17,799 entities, 26,798 relationship triples, and 1,328 attribute triples covering 10 entity types, 19 relationship types, and 6 attributes. Participants are required to develop a constructor that will automatically construct a financial knowledge graph based on the FR2KG. In addition, we summarized the technologies for automatically constructing knowledge graphs, and introduced the methods used by the winners and the results of this evaluation.

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Zhao, Jing Jing, and Yong Ming Yang. "A Novel Algorithm for Action Landmarks Extraction." Advanced Materials Research 659 (January 2013): 97–102. http://dx.doi.org/10.4028/www.scientific.net/amr.659.97.

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In this work, we present a new algorithm for extracting action landmarks from proposition landmarks graph. Compared to published approaches, the action landmarks extraction algorithm we proposed can find more disjunctive action landmarks and single action landmarks. We illustrate our ideas with experiments among benchmark domains. The experiments show that the new algorithm is competitive with traditional algorithm for finding action landmarks from relaxed plan graph.

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Gildea, Daniel, Giorgio Satta, and Xiaochang Peng. "Ordered Tree Decomposition for HRG Rule Extraction." Computational Linguistics 45, no. 2 (June 2019): 339–79. http://dx.doi.org/10.1162/coli_a_00350.

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We present algorithms for extracting Hyperedge Replacement Grammar (HRG) rules from a graph along with a vertex order. Our algorithms are based on finding a tree decomposition of smallest width, relative to the vertex order, and then extracting one rule for each node in this structure. The assumption of a fixed order for the vertices of the input graph makes it possible to solve the problem in polynomial time, in contrast to the fact that the problem of finding optimal tree decompositions for a graph is NP-hard. We also present polynomial-time algorithms for parsing based on our HRGs, where the input is a vertex sequence and the output is a graph structure. The intended application of our algorithms is grammar extraction and parsing for semantic representation of natural language. We apply our algorithms to data annotated with Abstract Meaning Representations and report on the characteristics of the resulting grammars.

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Zamini, Mohamad, Hassan Reza, and Minou Rabiei. "A Review of Knowledge Graph Completion." Information 13, no. 8 (August 21, 2022): 396. http://dx.doi.org/10.3390/info13080396.

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Information extraction methods proved to be effective at triple extraction from structured or unstructured data. The organization of such triples in the form of (head entity, relation, tail entity) is called the construction of Knowledge Graphs (KGs). Most of the current knowledge graphs are incomplete. In order to use KGs in downstream tasks, it is desirable to predict missing links in KGs. Different approaches have been recently proposed for representation learning of KGs by embedding both entities and relations into a low-dimensional vector space aiming to predict unknown triples based on previously visited triples. According to how the triples will be treated independently or dependently, we divided the task of knowledge graph completion into conventional and graph neural network representation learning and we discuss them in more detail. In conventional approaches, each triple will be processed independently and in GNN-based approaches, triples also consider their local neighborhood.

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Niu, L., and Y. Q. Song. "A FASTER R-CNN APPROACH FOR EXTRACTING INDOOR NAVIGATION GRAPH FROM BUILDING DESIGNS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W13 (June 5, 2019): 865–72. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w13-865-2019.

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Abstract. The indoor navigation graph is crucial for emergency evacuation and route guidance. However, most of existing solutions are limited to the tedious manual solutions and inefficient automatic solutions of the indoor building designs. In this paper, we strive to combine the cutting-edge faster R-CNN deep learning models with spatial connection rules to provide fine quality indoor navigation graphs. The extraction experiment result is convincing for general navigation purpose. But there exist several shortages for faster R-CNN models to overcome, such as optimizations of the complex object detections and ability of handling irregular shape regions for indoor navigation graph extractions.

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Grabowecky, Marcia, Stacey Parrott, Emmanuel Guzman-Martinez, Laura Ortega, and Satoru Suzuki. "Auditory–visual, positional, and semantic effects in visual extraction of slope." Seeing and Perceiving 25 (2012): 196. http://dx.doi.org/10.1163/187847612x648251.

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Extracting slopes from arrays of visual features is crucial for interpreting graphs. To understand broader influences on slope perception, we investigated the effects of concurrent sounds, of relative graph location, and of semantic priming on a visual search task in which observers searched for a graph with a positive or negative slope. Four bar graphs or scatter plots were simultaneously presented in separate quadrants of a visual display. Participants pressed a key as quickly as possible if one of the graphs displayed the target slope and otherwise refrained from response. A concurrently presented ascending pitch slowed responses to negative-slope targets, and concurrently presented descending pitch slowed responses to positive-slope targets, indicating crossmodal interference. This interference was eliminated when the sounds were presented 200 ms before the graphs, consistent with crossmodal interaction rather than response bias. Positive slopes were detected slowest in the upper-left quadrant whereas negative slopes were detected slowest in the upper-right quadrant, suggesting that slope detection is impeded when a graph is placed inconsistently with a mental number-line representation (negative values on the left and positive values on the right). Finally, positive slopes were detected faster when the search display was immediately preceded by a briefly flashed word ‘uphill’ compared to the word ‘downhill’ (and the converse for negative slopes), indicating a semantic priming effect, but this was observed only for scatter plots (the stimulus specificity precluding response bias). In summary, perception of visual slope is systematically influenced by auditory signals, by location of graphs, and by semantic priming.

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Peng, Nanyun, Hoifung Poon, Chris Quirk, Kristina Toutanova, and Wen-tau Yih. "Cross-Sentence N-ary Relation Extraction with Graph LSTMs." Transactions of the Association for Computational Linguistics 5 (December 2017): 101–15. http://dx.doi.org/10.1162/tacl_a_00049.

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Past work in relation extraction has focused on binary relations in single sentences. Recent NLP inroads in high-value domains have sparked interest in the more general setting of extracting n-ary relations that span multiple sentences. In this paper, we explore a general relation extraction framework based on graph long short-term memory networks (graph LSTMs) that can be easily extended to cross-sentence n-ary relation extraction. The graph formulation provides a unified way of exploring different LSTM approaches and incorporating various intra-sentential and inter-sentential dependencies, such as sequential, syntactic, and discourse relations. A robust contextual representation is learned for the entities, which serves as input to the relation classifier. This simplifies handling of relations with arbitrary arity, and enables multi-task learning with related relations. We evaluate this framework in two important precision medicine settings, demonstrating its effectiveness with both conventional supervised learning and distant supervision. Cross-sentence extraction produced larger knowledge bases. and multi-task learning significantly improved extraction accuracy. A thorough analysis of various LSTM approaches yielded useful insight the impact of linguistic analysis on extraction accuracy.

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Wang, Erniu, Fan Wang, Zhihao Yang, Lei Wang, Yin Zhang, Hongfei Lin, and Jian Wang. "A Graph Convolutional Network–Based Method for Chemical-Protein Interaction Extraction: Algorithm Development." JMIR Medical Informatics 8, no. 5 (May 19, 2020): e17643. http://dx.doi.org/10.2196/17643.

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Background Extracting the interactions between chemicals and proteins from the biomedical literature is important for many biomedical tasks such as drug discovery, medicine precision, and knowledge graph construction. Several computational methods have been proposed for automatic chemical-protein interaction (CPI) extraction. However, the majority of these proposed models cannot effectively learn semantic and syntactic information from complex sentences in biomedical texts. Objective To relieve this problem, we propose a method to effectively encode syntactic information from long text for CPI extraction. Methods Since syntactic information can be captured from dependency graphs, graph convolutional networks (GCNs) have recently drawn increasing attention in natural language processing. To investigate the performance of a GCN on CPI extraction, this paper proposes a novel GCN-based model. The model can effectively capture sequential information and long-range syntactic relations between words by using the dependency structure of input sentences. Results We evaluated our model on the ChemProt corpus released by BioCreative VI; it achieved an F-score of 65.17%, which is 1.07% higher than that of the state-of-the-art system proposed by Peng et al. As indicated by the significance test (P<.001), the improvement is significant. It indicates that our model is effective in extracting CPIs. The GCN-based model can better capture the semantic and syntactic information of the sentence compared to other models, therefore alleviating the problems associated with the complexity of biomedical literature. Conclusions Our model can obtain more information from the dependency graph than previously proposed models. Experimental results suggest that it is competitive to state-of-the-art methods and significantly outperforms other methods on the ChemProt corpus, which is the benchmark data set for CPI extraction.

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Barai, Mohit Kumar, and Subhasis Sanyal. "DOMAIN SPECIFIC KEY FEATURE EXTRACTION USING KNOWLEDGE GRAPH MINING." Multiple Criteria Decision Making 15 (2020): 1–22. http://dx.doi.org/10.22367/mcdm.2020.15.01.

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In the field of text mining, many novel feature extraction approaches have been propounded. The following research paper is based on a novel feature extraction algorithm. In this paper, to formulate this approach, a weighted graph mining has been used to ensure the effectiveness of the feature extraction and computational efficiency; only the most effective graphs representing the maximum number of triangles based on a predefined relational criterion have been considered. The proposed novel technique is an amalgamation of the relation between words surrounding an aspect of the product and the lexicon-based connection among those words, which creates a relational triangle. A maximum number of a triangle covering an element has been accounted as a prime feature. The proposed algorithm performs more than three times better than TF-IDF within a limited set of data in analysis based on domain-specific data. Keywords: feature extraction, natural language processing, product review, text processing, knowledge graph.

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Akmal, R. Munir, and J. Santoso. "Graph Extraction of Batik Image Using Region Adjacency Graph Representation." IOP Conference Series: Materials Science and Engineering 1077, no. 1 (February 1, 2021): 012006. http://dx.doi.org/10.1088/1757-899x/1077/1/012006.

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VISHVESHWARA, SARASWATHI, K. V. BRINDA, and N. KANNAN. "PROTEIN STRUCTURE: INSIGHTS FROM GRAPH THEORY." Journal of Theoretical and Computational Chemistry 01, no. 01 (July 2002): 187–211. http://dx.doi.org/10.1142/s0219633602000117.

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The sequence and structure of a large body of proteins are becoming increasingly available. It is desirable to explore mathematical tools for efficient extraction of information from such sources. The principles of graph theory, which was earlier applied in fields such as electrical engineering and computer networks are now being adopted to investigate protein structure, folding, stability, function and dynamics. This review deals with a brief account of relevant graphs and graph theoretic concepts. The concepts of protein graph construction are discussed. The manner in which graphs are analyzed and parameters relevant to protein structure are extracted, are explained. The structural and biological information derived from protein structures using these methods is presented.

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Liu, Wei. "Automatically refining synonym extraction results: Cleaning and ranking." Journal of Information Science 45, no. 4 (September 14, 2018): 460–72. http://dx.doi.org/10.1177/0165551518799640.

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Synonyms are crucial resources for many semantic applications, and the issue of synonym extraction has been studied extensively. However, extraction accuracy still cannot meet the practical demands. In addition, manually refining extraction results is time consuming. This article focuses on refining synonym extraction results by cleaning and ranking. A new graph model, the synonym graph, is proposed for the purpose of transforming the synonym extraction result of each word into a directed graph. Following this, two approaches for refining synonym extraction results are proposed based on the synonym graph. The first approach divides each extraction result into two parts – synonyms and noise – and detects noise by analysing the connectivity of the synonym graph. The second approach ranks the words in each extraction result by computing their semantic distance in the synonym graph. This approach was found to be more flexible than the first. The results of the experiments conducted in this study indicate that the performance of both of our proposed approaches is effective. In particular, they were found to perform well with datasets containing large synonym extraction results, which is important to reducing the cost of refining.

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Usery, E. Lynn. "GeoAI for Topographic Mapping Feature Extraction to Knowledge Graph." Abstracts of the ICA 2 (October 9, 2020): 1. http://dx.doi.org/10.5194/ica-abs-2-39-2020.

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Abstract. The U.S Geological Survey is exploring the use of machine learning and geospatial artificial intelligence (GeoAI) for topographic mapping tasks. These automated tasks include extracting topographic features such as hydrography, transportation, vegetation canopy, urban 3D structures, and others from raw data including lidar point clouds, color and near infrared images, historic topographic maps, and Web sources of existing geospatial resources. Current (2020) work includes extracting hydrography from elevation data, and geomorphic features with geographic names from historical topographical maps using Deep Learning. Extracted features are included in a geographic information system (GIS), supporting topographic mapping and modeling activities, and as semantic entities in a graph data model, building a knowledge graph for topographic data. These GIS datasets and topographic knowledge graphs can be used in automated topographic mapping processes and artificial intelligence routines that develop data for hydrologic, biologic, and geologic models that form part of the USGS EarthMap vision.

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CHEN, Jin-Xiu, and Dong-Hong JI. "Graph-Based Semi-Supervised Relation Extraction." Journal of Software 19, no. 11 (April 7, 2009): 2843–52. http://dx.doi.org/10.3724/sp.j.1001.2008.02843.

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Burdescu, Dumitru Dan, Liana Stanescu, Marius Brezovan, Cosmin Stoica Spahiu, and Daniel Costin Ebanca. "Graph Extraction Algorithm for Volumetric Segmentation." Advanced Science Letters 21, no. 10 (October 1, 2015): 3123–27. http://dx.doi.org/10.1166/asl.2015.6443.

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Sekkal, Houda, Naïla Amrous, and Samir Bennani. "Knowledge graph-based method for solutions detection and evaluation in an online problem-solving community." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 6 (December 1, 2022): 6350. http://dx.doi.org/10.11591/ijece.v12i6.pp6350-6362.

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Online communities are a real medium for human experiences sharing. They contain rich knowledge of lived situations and experiences that can be used to support decision-making process and problem-solving. This work presents an approach for extracting, representing, and evaluating components of problem-solving knowledge shared in online communities. Few studies have tackled the issue of knowledge extraction and its usefulness evaluation in online communities. In this study, we propose a new approach to detect and evaluate best solutions to problems discussed by members of online communities. Our approach is based on knowledge graph technology and graphs theory enabling the representation of knowledge shared by the community and facilitating its reuse. Our process of problem-solving knowledge extraction in online communities (PSKEOC) consists of three phases: problems and solutions detection and classification, knowledge graph constitution and finally best solutions evaluation. The experimental results are compared to the World Health Organization (WHO) model chapter about Infant and young child feeding and show that our approach succeed to extract and reveal important problem-solving knowledge contained in online community’s conversations. Our proposed approach leads to the construction of an experiential knowledge graph as a representation of the constructed knowledge base in the community studied in this paper.

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Ouadid, Youssef, Mohamed Fakir, and Brahim Minaoui. "Tifinagh Printed Character Recognition through Structural Feature Extraction." International Journal of Computer Vision and Image Processing 6, no. 2 (July 2016): 42–53. http://dx.doi.org/10.4018/ijcvip.2016070103.

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In this paper a system for the recognition of printed Tifinagh characters is presented. It is divided into three main steps: preprocessing, feature extraction, and classification. Image quality is enhanced through preprocessing which are: binarization, normalization and thinning. Then the image is given to a proposed structural feature extracting algorithm where the character is divided into several geometrically sample shapes which are segments, then transformed into an undirected graph with unique coordinate of all nodes. The character is classified by matching the graph of the character and its counterpart graph which is generated from the images in the IRCAM database using an efficient spectral graph matching algorithm. Experimental results and analysis are accomplished by the use of 3267 random characters to test the effectiveness. The system shows good results in term of accuracy and CPU time.

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Cramond, Fala, Alison O'Mara-Eves, Lee Doran-Constant, Andrew SC Rice, Malcolm Macleod, and James Thomas. "The development and evaluation of an online application to assist in the extraction of data from graphs for use in systematic reviews." Wellcome Open Research 3 (March 7, 2019): 157. http://dx.doi.org/10.12688/wellcomeopenres.14738.3.

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Background: The extraction of data from the reports of primary studies, on which the results of systematic reviews depend, needs to be carried out accurately. To aid reliability, it is recommended that two researchers carry out data extraction independently. The extraction of statistical data from graphs in PDF files is particularly challenging, as the process is usually completely manual, and reviewers need sometimes to revert to holding a ruler against the page to read off values: an inherently time-consuming and error-prone process. Methods: To mitigate some of the above problems we integrated and customised two existing JavaScript libraries to create a new web-based graphical data extraction tool to assist reviewers in extracting data from graphs. This tool aims to facilitate more accurate and timely data extraction through a user interface which can be used to extract data through mouse clicks. We carried out a non-inferiority evaluation to examine its performance in comparison with participants’ standard practice for extracting data from graphs in PDF documents. Results: We found that the customised graphical data extraction tool is not inferior to users’ (N=10) prior standard practice. Our study was not designed to show superiority, but suggests that, on average, participants saved around 6 minutes per graph using the new tool, accompanied by a substantial increase in accuracy. Conclusions: Our study suggests that the incorporation of this type of tool in online systematic review software would be beneficial in facilitating the production of accurate and timely evidence synthesis to improve decision-making.

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Liang, Jianming, Qing He, Damin Zhang, and Shuangshuang Fan. "Extraction of Joint Entity and Relationships with Soft Pruning and GlobalPointer." Applied Sciences 12, no. 13 (June 22, 2022): 6361. http://dx.doi.org/10.3390/app12136361.

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In recent years, scholars have paid increasing attention to the joint entity and relation extraction. However, the most difficult aspect of joint extraction is extracting overlapping triples. To address this problem, we propose a joint extraction model based on Soft Pruning and GlobalPointer, short for SGNet. In the first place, the BERT pretraining model is used to obtain the text word vector representation with contextual information, and then the local and non-local information of the word vector is obtained through graph operations. Specifically, to address the lack of information caused by the rule-based pruning strategies, we utilize the Gaussian Graph Generator and the attention-guiding layer to construct a fully connected graph. This process is called soft pruning for short. Then, to achieve node message passing and information integration, we employ GCNs and a thick connection layer. Next, we use the GlobalPointer decoder to convert triple extraction into quintuple extraction to tackle the problem of problematic overlapping triples extraction. The GlobalPointer decoder, unlike the typical feedforward neural network (FNN), can perform joint decoding. In the end, to evaluate the model performance, the experiment was carried out on two public datasets: the NYT and WebNLG. The experiments show that SGNet performs substantially better on overlapping extraction and achieves good results on two publicly available datasets.

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An, Dezhi, Xuejie Ma, Cao Jiang, Lei Liu, and Yanxu Wang. "Research and Application of Relation Extraction based on Triple Relation Graph Convolutional Networks." Journal of Physics: Conference Series 2166, no. 1 (January 1, 2022): 012060. http://dx.doi.org/10.1088/1742-6596/2166/1/012060.

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Abstract In recent years, data in non-Euclidean spaces is becoming more and more. Traditional methods cannot perform feature extraction on these data. Most of existing methods just extract contextual semantic features from relational instances. Their structural features in corpora are ignored. To solve this problem, the paper proposed a relation extraction method based on triple relation graph convolutional networks (TRGCN). Based on the extraction of semantic features of sentences using convolutional neural networks, this method used the concept of triple relation graphs to represent structural features. In other words, triple relation graphs were formed by considering triples formed by the relation between two entities in one sentence as nodes and triples with common entities and same relations as edges. Finally, multiple-layer graph convolutional networks were used for training. As shown by experimental results, the method proposed in this paper achieved an F1 value of 86.8% on the SemEval 2010 Tesk 8 data set, indicating that it is better than mainstream convolutional neural networks and recurrent neural networks.

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Pujara, Jay, Hui Miao, Lise Getoor, and William W. Cohen. "Using Semantics and Statistics to Turn Data into Knowledge." AI Magazine 36, no. 1 (March 25, 2015): 65–74. http://dx.doi.org/10.1609/aimag.v36i1.2568.

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Many information extraction and knowledge base construction systems are addressing the challenge of deriving knowledge from text. A key problem in constructing these knowledge bases from sources like the web is overcoming the erroneous and incomplete information found in millions of candidate extractions. To solve this problem, we turn to semantics — using ontological constraints between candidate facts to eliminate errors. In this article, we represent the desired knowledge base as a knowledge graph and introduce the problem of knowledge graph identification, collectively resolving the entities, labels, and relations present in the knowledge graph. Knowledge graph identification requires reasoning jointly over millions of extractions simultaneously, posing a scalability challenge to many approaches. We use probabilistic soft logic (PSL), a recently-introduced statistical relational learning framework, to implement an efficient solution to knowledge graph identification and present state-of-the-art results for knowledge graph construction while performing an order of magnitude faster than competing methods.

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Sun, Hui, Yu Gen Yi, Ying Hua Lv, Hui Tao Cai, and Jian Zhong Wang. "A Semi-Supervised Sparsity Discriminant Analysis Algorithm for Feature Extraction." Advanced Materials Research 546-547 (July 2012): 670–74. http://dx.doi.org/10.4028/www.scientific.net/amr.546-547.670.

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Recently, l1-graph was proposed as a new graph construction procedure. Compared with the kNN-graph and ε-graph, l1-graph possesses three advantages: robustness to data noise, sparsity and datum-adaptive neighborhood selection. In this paper, we propose a novel semi-supervised feature extraction method based on l1-graph termed Semi-supervised Sparsity Discriminant Analysis (S3DA). The proposed S3DA maintains the advantages of l1-graph, and more importantly, it has better capacity of discrimination for classification. Experimental results on face and gene expression databases demonstrate our proposed approach outperform some other state of the art algorithms, and also show the feasibility and effectiveness of our proposed approach.

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Wang, Jian, Xiaoyu Chen, Yu Zhang, Yijia Zhang, Jiabin Wen, Hongfei Lin, Zhihao Yang, and Xin Wang. "Document-Level Biomedical Relation Extraction Using Graph Convolutional Network and Multihead Attention: Algorithm Development and Validation." JMIR Medical Informatics 8, no. 7 (July 31, 2020): e17638. http://dx.doi.org/10.2196/17638.

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Background Automatically extracting relations between chemicals and diseases plays an important role in biomedical text mining. Chemical-disease relation (CDR) extraction aims at extracting complex semantic relationships between entities in documents, which contain intrasentence and intersentence relations. Most previous methods did not consider dependency syntactic information across the sentences, which are very valuable for the relations extraction task, in particular, for extracting the intersentence relations accurately. Objective In this paper, we propose a novel end-to-end neural network based on the graph convolutional network (GCN) and multihead attention, which makes use of the dependency syntactic information across the sentences to improve CDR extraction task. Methods To improve the performance of intersentence relation extraction, we constructed a document-level dependency graph to capture the dependency syntactic information across sentences. GCN is applied to capture the feature representation of the document-level dependency graph. The multihead attention mechanism is employed to learn the relatively important context features from different semantic subspaces. To enhance the input representation, the deep context representation is used in our model instead of traditional word embedding. Results We evaluate our method on CDR corpus. The experimental results show that our method achieves an F-measure of 63.5%, which is superior to other state-of-the-art methods. In the intrasentence level, our method achieves a precision, recall, and F-measure of 59.1%, 81.5%, and 68.5%, respectively. In the intersentence level, our method achieves a precision, recall, and F-measure of 47.8%, 52.2%, and 49.9%, respectively. Conclusions The GCN model can effectively exploit the across sentence dependency information to improve the performance of intersentence CDR extraction. Both the deep context representation and multihead attention are helpful in the CDR extraction task.

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YOU, QUBO, NANNING ZHENG, LING GAO, SHAOYI DU, and YANG WU. "ANALYSIS OF SOLUTION FOR SUPERVISED GRAPH EMBEDDING." International Journal of Pattern Recognition and Artificial Intelligence 22, no. 07 (November 2008): 1283–99. http://dx.doi.org/10.1142/s021800140800679x.

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Recently, Graph Embedding Framework has been proposed for feature extraction. However, it is still an open issue on how to compute robust discriminant transformation for this purpose. In this paper, we show that supervised graph embedding algorithms share a general criterion. Based on the analysis of this criterion, we propose a general solution, called General Solution for Supervised Graph Embedding (GSSGE), for extracting the robust discriminant transformation of Supervised Graph Embedding. Then, we analyze the superiority of our algorithm over traditional algorithms. Extensive experiments on both artificial and real-world data are performed to demonstrate the effectiveness and robustness of our proposed GSSGE.

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Cheng, Binjie, Jin Zhang, Hong Liu, Meiling Cai, and Ying Wang. "Research on Medical Knowledge Graph for Stroke." Journal of Healthcare Engineering 2021 (March 24, 2021): 1–10. http://dx.doi.org/10.1155/2021/5531327.

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Knowledge graph can effectively analyze and construct the essential characteristics of data. At present, scholars have proposed many knowledge graph models from different perspectives, especially in the medical field, but there are still relatively few studies on stroke diseases using medical knowledge graphs. Therefore, this paper will build a medical knowledge graph model for stroke. Firstly, a stroke disease dictionary and an ontology database are built through the international standard medical term sets and semiautomatic extraction-based crowdsourcing website data. Secondly, the external data are linked to the nodes of the existing knowledge graph via the entity similarity measures and the knowledge representation is performed by the knowledge graph embedded model. Thirdly, the structure of the established knowledge graph is modified continuously through iterative updating. Finally, in the experimental part, the proposed stroke medical knowledge graph is applied to the real stroke data and the performance of the proposed knowledge graph approach on the series of Trans ∗ models is compared.

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Xue, Fuzhao, Aixin Sun, Hao Zhang, and Eng Siong Chng. "GDPNet: Refining Latent Multi-View Graph for Relation Extraction." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 16 (May 18, 2021): 14194–202. http://dx.doi.org/10.1609/aaai.v35i16.17670.

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Relation Extraction (RE) is to predict the relation type of two entities that are mentioned in a piece of text, e.g., a sentence or a dialogue. When the given text is long, it is challenging to identify indicative words for the relation prediction. Recent advances on RE task are from BERT-based sequence modeling and graph-based modeling of relationships among the tokens in the sequence. In this paper, we propose to construct a latent multi-view graph to capture various possible relationships among tokens. We then refine this graph to select important words for relation prediction. Finally, the representation of the refined graph and the BERT-based sequence representation are concatenated for relation extraction. Specifically, in our proposed GDPNet (Gaussian Dynamic Time Warping Pooling Net), we utilize Gaussian Graph Generator (GGG) to generate edges of the multi-view graph. The graph is then refined by Dynamic Time Warping Pooling (DTWPool). On DialogRE and TACRED, we show that GDPNet achieves the best performance on dialogue-level RE, and comparable performance with the state-of-the-arts on sentence-level RE. Our code is available at https://github.com/XueFuzhao/GDPNet.

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Maisonnave, Mariano, Fernando Delbianco, Fernando Tohme, Evangelos Milios, and Ana G. Maguitman. "Causal graph extraction from news: a comparative study of time-series causality learning techniques." PeerJ Computer Science 8 (August 3, 2022): e1066. http://dx.doi.org/10.7717/peerj-cs.1066.

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Causal graph extraction from news has the potential to aid in the understanding of complex scenarios. In particular, it can help explain and predict events, as well as conjecture about possible cause-effect connections. However, limited work has addressed the problem of large-scale extraction of causal graphs from news articles. This article presents a novel framework for extracting causal graphs from digital text media. The framework relies on topic-relevant variables representing terms and ongoing events that are selected from a domain under analysis by applying specially developed information retrieval and natural language processing methods. Events are represented as event-phrase embeddings, which make it possible to group similar events into semantically cohesive clusters. A time series of the selected variables is given as input to a causal structure learning techniques to learn a causal graph associated with the topic that is being examined. The complete framework is applied to the New York Times dataset, which covers news for a period of 246 months (roughly 20 years), and is illustrated through a case study. An initial evaluation based on synthetic data is carried out to gain insight into the most effective time-series causality learning techniques. This evaluation comprises a systematic analysis of nine state-of-the-art causal structure learning techniques and two novel ensemble methods derived from the most effective techniques. Subsequently, the complete framework based on the most promising causal structure learning technique is evaluated with domain experts in a real-world scenario through the use of the presented case study. The proposed analysis offers valuable insights into the problems of identifying topic-relevant variables from large volumes of news and learning causal graphs from time series.

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LI, HAIBO, YUTAKA MATSUO, and MITSURU ISHIZUKA. "GRAPH BASED MULTI-VIEW LEARNING FOR SEMANTIC RELATION EXTRACTION." International Journal of Semantic Computing 04, no. 03 (September 2010): 285–300. http://dx.doi.org/10.1142/s1793351x10001024.

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To understand text contents better, many research efforts have been made exploring detection and classification of the semantic relation between a concept pair. As described herein, we present our study of a semantic relation classification task as a graph-based multi-view learning task. Semantic relation can be naturally represented from two views: entity pair view and context view. Then we construct a weighted complete graph for each view and a bipartite graph to combine information of different views. An instance's label score is propagated on each intra-view graph and inter-view graph. The proposed algorithm is evaluated using the Concept Description Language for Natural Language (CDL) corpus and SemEval-2007 Task 04 dataset. The experimental results validate its effectiveness.

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Mao, Ningyi, Wenti Huang, and Hai Zhong. "KGGCN: Knowledge-Guided Graph Convolutional Networks for Distantly Supervised Relation Extraction." Applied Sciences 11, no. 16 (August 22, 2021): 7734. http://dx.doi.org/10.3390/app11167734.

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Distantly supervised relation extraction is the most popular technique for identifying semantic relation between two entities. Most prior models only focus on the supervision information present in training sentences. In addition to training sentences, external lexical resource and knowledge graphs often contain other relevant prior knowledge. However, relation extraction models usually ignore such readily available information. Moreover, previous works only utilize a selective attention mechanism over sentences to alleviate the impact of noise, they lack the consideration of the implicit interaction between sentences with relation facts. In this paper, (1) a knowledge-guided graph convolutional network is proposed based on the word-level attention mechanism to encode the sentences. It can capture the key words and cue phrases to generate expressive sentence-level features by attending to the relation indicators obtained from the external lexical resource. (2) A knowledge-guided sentence selector is proposed, which explores the semantic and structural information of triples from knowledge graph as sentence-level knowledge attention to distinguish the importance of each individual sentence. Experimental results on two widely used datasets, NYT-FB and GDS, show that our approach is able to efficiently use the prior knowledge from the external lexical resource and knowledge graph to enhance the performance of distantly supervised relation extraction.

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Jiang, Ming, Jiecheng He, Jianping Wu, Chengjie Qi, and Min Zhang. "Relation extraction based on semantic dependency graph." Journal of Computational Methods in Sciences and Engineering 20, no. 1 (April 10, 2020): 279–90. http://dx.doi.org/10.3233/jcm-193723.

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Sudhakar, Ch, A. Siva Pavan, N. Thirupathi Rao, and Debnath Bhattacharyya. "Map-Reduce based Frequent Sub-Graph Extraction." International Journal of Multimedia and Ubiquitous Engineering 15, no. 1 (May 30, 2020): 27–34. http://dx.doi.org/10.21742/ijmue.2020.15.1.03.

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LIU, Zhongbao. "Face feature extraction method based on graph." Journal of Computer Applications 33, no. 5 (October 14, 2013): 1432–34. http://dx.doi.org/10.3724/sp.j.1087.2013.01432.

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Şensoy, Murat, Burcu Yilmaz, and Timothy J. Norman. "Stage: Stereotypical Trust Assessment Through Graph Extraction." Computational Intelligence 32, no. 1 (July 3, 2014): 72–101. http://dx.doi.org/10.1111/coin.12046.

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Shao, Yingxia, Kai Lei, Lei Chen, Zi Huang, Bin Cui, Zhongyi Liu, Yunhai Tong, and Jin Xu. "Fast Parallel Path Concatenation for Graph Extraction." IEEE Transactions on Knowledge and Data Engineering 29, no. 10 (October 1, 2017): 2210–22. http://dx.doi.org/10.1109/tkde.2017.2716939.

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Wang, Tao, Jian Yang, Quansen Sun, Zexuan Ji, Peng Fu, and Qi Ge. "Global graph diffusion for interactive object extraction." Information Sciences 460-461 (September 2018): 103–14. http://dx.doi.org/10.1016/j.ins.2018.05.040.

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Liu, Jiayi, and Kun He. "Multi-scale foreground extraction on graph cut." MATEC Web of Conferences 277 (2019): 02031. http://dx.doi.org/10.1051/matecconf/201927702031.

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In order to improve Grab Cut implementation effect for real images, we propose a novel improvement which extends the Grab Cut in three aspects: 1) a series of edge-preserved components are generated via the TV smoothing model; 2) the number of sub-regions is estimated by histogram shape analysis to remove the negative effects on the unreasonable number of the sub-regions; 3) a segmentation termination condition is constructed by integrating the multi-scale components. The experiment result indicates that this method performs well compared to other methods based on graph cut and is insensitive to sub-regions.

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