Готові списки джерел за темами / Graph extraction

Добірка наукової літератури з теми "Graph extraction"

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Опубліковано: 28 вересня 2022

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Статті в журналах з теми "Graph extraction":

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.

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.

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.

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.

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.

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.

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.

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.

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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<p>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.</p>

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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Дисертації з теми "Graph extraction":

Dandala, Bharath. "Graph-Based Keyphrase Extraction Using Wikipedia." Thesis, University of North Texas, 2010. https://digital.library.unt.edu/ark:/67531/metadc67939/.

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Keyphrases describe a document in a coherent and simple way, giving the prospective reader a way to quickly determine whether the document satisfies their information needs. The pervasion of huge amount of information on Web, with only a small amount of documents have keyphrases extracted, there is a definite need to discover automatic keyphrase extraction systems. Typically, a document written by human develops around one or more general concepts or sub-concepts. These concepts or sub-concepts should be structured and semantically related with each other, so that they can form the meaningful representation of a document. Considering the fact, the phrases or concepts in a document are related to each other, a new approach for keyphrase extraction is introduced that exploits the semantic relations in the document. For measuring the semantic relations between concepts or sub-concepts in the document, I present a comprehensive study aimed at using collaboratively constructed semantic resources like Wikipedia and its link structure. In particular, I introduce a graph-based keyphrase extraction system that exploits the semantic relations in the document and features such as term frequency. I evaluated the proposed system using novel measures and the results obtained compare favorably with previously published results on established benchmarks.

Qian, Yujie. "A graph-based framework for information extraction." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/122765.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 43-45).
Most modern Information Extraction (IE) systems are implemented as sequential taggers and only model local dependencies. Non-local and non-sequential context is, however, a valuable source of information to improve predictions. In this thesis, we introduce a graph-based framework (GraphIE) that operates over a graph representing a broad set of dependencies between textual units (i.e. words or sentences). The algorithm propagates information between connected nodes through graph convolutions, generating a richer representation that can be exploited to improve word-level predictions. Evaluation on three different tasks -- namely textual, social media and visual information extraction -- shows that GraphlE consistently outperforms the state-of-the-art sequence tagging model by a significant margin.
by Yujie Qian.
S.M.
S.M. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science

Huang, Zan, Wingyan Chung, and Hsinchun Chen. "A Graph Model for E-Commerce Recommender Systems." Wiley Periodicals, Inc, 2004. http://hdl.handle.net/10150/105683.

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Artificial Intelligence Lab, Department of MIS, University of Arizona
Information overload on the Web has created enormous challenges to customers selecting products for online purchases and to online businesses attempting to identify customersâ preferences efficiently. Various recommender systems employing different data representations and recommendation methods are currently used to address these challenges. In this research, we developed a graph model that provides a generic data representation and can support different recommendation methods. To demonstrate its usefulness and flexibility, we developed three recommendation methods: direct retrieval, association mining, and high-degree association retrieval. We used a data set from an online bookstore as our research test-bed. Evaluation results showed that combining product content information and historical customer transaction information achieved more accurate predictions and relevant recommendations than using only collaborative information. However, comparisons among different methods showed that high-degree association retrieval did not perform significantly better than the association mining method or the direct retrieval method in our test-bed.

Haugeard, Jean-Emmanuel. "Extraction et reconnaissance de primitives dans les façades de Paris à l'aide d'appariement de graphes." Thesis, Cergy-Pontoise, 2010. http://www.theses.fr/2010CERG0497.

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Cette dernière décennie, la modélisation des villes 3D est devenue l'un des enjeux de la recherche multimédia et un axe important en reconnaissance d'objets. Dans cette thèse nous nous sommes intéressés à localiser différentes primitives, plus particulièrement les fenêtres, dans les façades de Paris. Dans un premier temps, nous présentons une analyse des façades et des différentes propriétés des fenêtres. Nous en déduisons et proposons ensuite un algorithme capable d'extraire automatiquement des hypothèses de fenêtres. Dans une deuxième partie, nous abordons l'extraction et la reconnaissance des primitives à l'aide d'appariement de graphes de contours. En effet une image de contours est lisible par l'oeil humain qui effectue un groupement perceptuel et distingue les entités présentes dans la scène. C'est ce mécanisme que nous avons cherché à reproduire. L'image est représentée sous la forme d'un graphe d'adjacence de segments de contours, valué par des informations d'orientation et de proximité des segments de contours. Pour la mise en correspondance inexacte des graphes, nous proposons plusieurs variantes d'une nouvelle similarité basée sur des ensembles de chemins tracés sur les graphes, capables d'effectuer les groupements des contours et robustes aux changements d'échelle. La similarité entre chemins prend en compte la similarité des ensembles de segments de contours et la similarité des régions définies par ces chemins. La sélection des images d'une base contenant un objet particulier s'effectue à l'aide d'un classifieur SVM ou kppv. La localisation des objets dans l'image utilise un système de vote à partir des chemins sélectionnés par l'algorithme d'appariement
This last decade, modeling of 3D city became one of the challenges of multimedia search and an important focus in object recognition. In this thesis we are interested to locate various primitive, especially the windows, in the facades of Paris. At first, we present an analysis of the facades and windows properties. Then we propose an algorithm able to extract automatically window candidates. In a second part, we discuss about extraction and recognition primitives using graph matching of contours. Indeed an image of contours is readable by the human eye, which uses perceptual grouping and makes distinction between entities present in the scene. It is this mechanism that we have tried to replicate. The image is represented as a graph of adjacency of segments of contours, valued by information orientation and proximity to edge segments. For the inexact matching of graphs, we propose several variants of a new similarity based on sets of paths, able to group several contours and robust to scale changes. The similarity between paths takes into account the similarity of sets of segments of contours and the similarity of the regions defined by these paths. The selection of images from a database containing a particular object is done using a KNN or SVM classifier

Nguyen, Quan M. Eng (Quan T. ). Massachusetts Institute of Technology. "Parallel and scalable neural image segmentation for connectome graph extraction." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/100644.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Title as it appears in MIT Commencement Exercises program, June 5, 2015: Connectomics project : performance engineering neural image segmentation. Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 77-79).
Segmentation of images, the process of grouping together pixels of the same object, is one of the major challenges in connectome extraction. Since connectomics data consist of large quantity of digital information generated by the electron microscope, there is a necessity for a highly scalable system that performs segmentation. To date, the state-of-the-art segmentation libraries such as GALA and NeuroProof lack parallel capability to be run on multicore machines in a distributed setting in order to achieve the scalability desired. Employing many performance engineering techniques, I parallelize a pipeline that uses the existing segmentation algorithms as building blocks to perform segmentation on EM grayscale images. For an input image stack of dimensions 1024 x 1024 x 100, the parallel segmentation program achieves a speedup of 5.3 counting I/O and 9.4 not counting I/O running on an 18-core machine. The program has become I/O bound, which is a better fit to run on a distributed computing framework. In this thesis, the contribution includes coming up with parallel algorithms for constructing a regional adjacency graph from labeled pixels and agglomerating an over-segmentation to obtain the final segmentation. The agglomeration process in particular is challenging to parallelize because most graph-based segmentation libraries entail very complex dependency. This has led many people to believe that the process is inherently sequential. However, I found a way to get good speedup by sacrificing some segmentation quality. It turns out that one could trade o a negligible amount in quality for a large gain in parallelism.
by Quan Nguyen.
M. Eng.

Florescu, Corina Andreea. "SurfKE: A Graph-Based Feature Learning Framework for Keyphrase Extraction." Thesis, University of North Texas, 2019. https://digital.library.unt.edu/ark:/67531/metadc1538730/.

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Current unsupervised approaches for keyphrase extraction compute a single importance score for each candidate word by considering the number and quality of its associated words in the graph and they are not flexible enough to incorporate multiple types of information. For instance, nodes in a network may exhibit diverse connectivity patterns which are not captured by the graph-based ranking methods. To address this, we present a new approach to keyphrase extraction that represents the document as a word graph and exploits its structure in order to reveal underlying explanatory factors hidden in the data that may distinguish keyphrases from non-keyphrases. Experimental results show that our model, which uses phrase graph representations in a supervised probabilistic framework, obtains remarkable improvements in performance over previous supervised and unsupervised keyphrase extraction systems.

Shah, Faaiz Hussain. "Gradual Pattern Extraction from Property Graphs." Thesis, Montpellier, 2019. http://www.theses.fr/2019MONTS025/document.

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Les bases de données orientées graphes (NoSQL par exemple) permettent de gérer des données dans lesquelles les liens sont importants et des requêtes complexes sur ces données à l’aide d’un environnement dédié offrant un stockage et des traitements spécifiquement destinés à la structure de graphe. Un graphe de propriété dans un environnement NoSQL est alors vu comme un graphe orienté étiqueté dans lequel les étiquettes des nœuds et les relations sont des ensembles d’attributs (propriétés) de la forme (clé:valeur). Cela facilite la représentation de données et de connaissances sous la forme de graphes. De nombreuses applications réelles de telles bases de données sont actuellement connues dans le monde des réseaux sociaux, mais aussi des systèmes de recommandation, de la détection de fraudes, du data-journalisme (pour les panama papers par exemple). De telles structures peuvent cependant être assimilées à des bases NoSQL semi-structurées dans lesquelles toutes les propriétés ne sont pas présentes partout, ce qui conduit à des valeurs non présentes de manière homogène, soit parce que la valeur n’est pas connue (l’âge d’une personne par exemple) ou parce qu’elle n’est pas applicable (l’année du service militaire d’une femme par exemple dans un pays et à une époque à laquelle les femmes ne le faisaient pas). Cela gêne alors les algorithmes d’extraction de connaissance qui ne sont pas tous robustes aux données manquantes. Des approches ont été proposées pour remplacer les données manquantes et permettre aux algorithmes d’être appliqués. Cependant,nous considérons que de telles approches ne sont pas satisfaisantes car elles introduisent un biais ou même des erreurs quand aucune valeur n’était applicable. Dans nos travaux, nous nous focalisons sur l’extraction de motifs graduels à partir de telles bases de données. Ces motifs permettent d’extraire automatiquement les informations corrélées. Une première contribution est alors de définir quels sont les motifs pouvant être extraits à partir de telles bases de données. Nous devons, dans un deuxième temps, étendre les travaux existant dans la littérature pour traiter les valeurs manquantes dans les bases de données graphe, comme décrit ci-dessus. L’application de telles méthodes est alors rendue difficile car les propriétés classiquement appliquées en fouille de données (anti-monotonie) ne sont plus valides. Nous proposons donc une nouvelle approche qui est testée sur des données réelles et synthétiques. Une première forme de motif est extrait à partir des propriétés des nœuds et est étendue pour prendre en compte les relations entre nœuds. Enfin, notre approche est étendue au cas des motifs graduels flous afin de mieux prendre en compte la nature imprécise des connaissances présentes et à extraire. Les expérimentations sur des bases synthétiques ont été menées grâce au développement d’un générateur de bases de données de graphes de propriétés synthétiques. Nous en montrons les résultats en termes de temps calcul et consommation mémoire ainsi qu’en nombre de motifs générés
Graph databases (NoSQL oriented graph databases) provide the ability to manage highly connected data and complex database queries along with the native graph-storage and processing. A property graph in a NoSQL graph engine is a labeled directed graph composed of nodes connected through relationships with a set of attributes or properties in the form of (key:value) pairs. It facilitates to represent the data and knowledge that are in form of graphs. Practical applications of graph database systems have been seen in social networks, recommendation systems, fraud detection, and data journalism, as in the case for panama papers. Often, we face the issue of missing data in such kind of systems. In particular, these semi-structured NoSQL databases lead to a situation where some attributes (properties) are filled-in while other ones are not available, either because they exist but are missing (for instance the age of a person that is unknown) or because they are not applicable for a particular case (for instance the year of military service for a girl in countries where it is mandatory only for boys). Therefore, some keys can be provided for some nodes and not for other ones. In such a scenario, when we want to extract knowledge from these new generation database systems, we face the problem of missing data that arise need for analyzing them. Some approaches have been proposed to replace missing values so as to be able to apply data mining techniques. However, we argue that it is not relevant to consider such approaches so as not to introduce biases or errors. In our work, we focus on the extraction of gradual patterns from property graphs that provide end-users with tools for mining correlations in the data when there exist missing values. Our approach requires first to define gradual patterns in the context of NoSQL property graph and then to extend existing algorithms so as to treat the missing values, because anti-monotonicity of the support can not be considered anymore in a simple manner. Thus, we introduce a novel approach for mining gradual patterns in the presence of missing values and we test it on real and synthetic data. Further to this work, we present our approach for mining such graphs in order to extract frequent gradual patterns in the form of ``the more/less $A_1$,..., the more/less $A_n$" where $A_i$ are information from the graph, should it be from the nodes or from the relationships. In order to retrieve more valuable patterns, we consider fuzzy gradual patterns in the form of ``The more/less the A_1 is F_1,...,the more/less the A_n is F_n" where A_i are attributes retrieved from the graph nodes or relationships and F_i are fuzzy descriptions. For this purpose, we introduce the definitions of such concepts, the corresponding method for extracting the patterns, and the experiments that we have led on synthetic graphs using a graph generator. We show the results in terms of time utilization, memory consumption and the number of patterns being generated

Sánchez, Yagüe Mónica. "Information extraction and validation of CDFG in NoGap." Thesis, Linköpings universitet, Datorteknik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-93905.

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A Control Data Flow Graph (CDFG) is a Directed Acyclic Graph (DAG) in which a node can be either an operation node or a control node. The target of this kind of graph is to capture allt he control and data flow information of the original hardware description while preserving the various dependencies. This kind of graph is generated by Novel Generator of Accelerators and Processors (NoGap), a design automation tool for Application Specific Instruction-set Processor (ASIP) and accelerator design developed by Per Karlström from the Department of Electrical Engineering of Linköping University. The aim of this project is to validate the graph, check if it fulfills the requirements of its definition. If it does not, it is considered an error and the running process will be aborted. Moreover, useful information will be extracted from the graph for futute work.

Lilliehöök, Hampus. "Extraction of word senses from bilingual resources using graph-based semantic mirroring." Thesis, Linköpings universitet, Interaktiva och kognitiva system, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-91880.

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In this thesis we retrieve semantic information that exists implicitly in bilingual data. We gather input data by repeatedly applying the semantic mirroring procedure. The data is then represented by vectors in a large vector space. A resource of synonym clusters is then constructed by performing K-means centroid-based clustering on the vectors. We evaluate the result manually, using dictionaries, and against WordNet, and discuss prospects and applications of this method.
I det här arbetet utvinner vi semantisk information som existerar implicit i tvåspråkig data. Vi samlar indata genom att upprepa proceduren semantisk spegling. Datan representeras som vektorer i en stor vektorrymd. Vi bygger sedan en resurs med synonymkluster genom att applicera K-means-algoritmen på vektorerna. Vi granskar resultatet för hand med hjälp av ordböcker, och mot WordNet, och diskuterar möjligheter och tillämpningar för metoden.

Hamid, Fahmida. "Evaluation Techniques and Graph-Based Algorithms for Automatic Summarization and Keyphrase Extraction." Thesis, University of North Texas, 2016. https://digital.library.unt.edu/ark:/67531/metadc862796/.

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Automatic text summarization and keyphrase extraction are two interesting areas of research which extend along natural language processing and information retrieval. They have recently become very popular because of their wide applicability. Devising generic techniques for these tasks is challenging due to several issues. Yet we have a good number of intelligent systems performing the tasks. As different systems are designed with different perspectives, evaluating their performances with a generic strategy is crucial. It has also become immensely important to evaluate the performances with minimal human effort. In our work, we focus on designing a relativized scale for evaluating different algorithms. This is our major contribution which challenges the traditional approach of working with an absolute scale. We consider the impact of some of the environment variables (length of the document, references, and system-generated outputs) on the performance. Instead of defining some rigid lengths, we show how to adjust to their variations. We prove a mathematically sound baseline that should work for all kinds of documents. We emphasize automatically determining the syntactic well-formedness of the structures (sentences). We also propose defining an equivalence class for each unit (e.g. word) instead of the exact string matching strategy. We show an evaluation approach that considers the weighted relatedness of multiple references to adjust to the degree of disagreements between the gold standards. We publish the proposed approach as a free tool so that other systems can use it. We have also accumulated a dataset (scientific articles) with a reference summary and keyphrases for each document. Our approach is applicable not only for evaluating single-document based tasks but also for evaluating multiple-document based tasks. We have tested our evaluation method for three intrinsic tasks (taken from DUC 2004 conference), and in all three cases, it correlates positively with ROUGE. Based on our experiments for DUC 2004 Question-Answering task, it correlates with the human decision (extrinsic task) with 36.008% of accuracy. In general, we can state that the proposed relativized scale performs as well as the popular technique (ROUGE) with flexibility for the length of the output. As part of the evaluation we have also devised a new graph-based algorithm focusing on sentiment analysis. The proposed model can extract units (e.g. words or sentences) from the original text belonging either to the positive sentiment-pole or to the negative sentiment-pole. It embeds both (positive and negative) types of sentiment-flow into a single text-graph. The text-graph is composed with words or phrases as nodes, and their relations as edges. By recursively calling two mutually exclusive relations the model builds the final rank of the nodes. Based on the final rank, it splits two segments from the article: one with highly positive sentiment and the other with highly negative sentiments. The output of this model was tested with the non-polar TextRank generated output to quantify how much of the polar summaries actually covers the fact along with sentiment.

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Книги з теми "Graph extraction":

Lin, I.-Jong. Video object extraction and representation: Theory and applications. Boston, Mass: Kluwer Academic Publisher, 2000.

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Kung, S. Y., and I.-Jong Lin. Video Object Extraction and Representation: Theory and Applications (The Springer International Series in Engineering and Computer Science). Springer, 2000.

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Частини книг з теми "Graph extraction":

Gibson, David, Ravi Kumar, Kevin S. McCurley, and Andrew Tomkins. "Dense Subgraph Extraction." In Mining Graph Data, 411–41. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2006. http://dx.doi.org/10.1002/9780470073049.ch16.

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Kejriwal, Mayank. "Information Extraction." In Domain-Specific Knowledge Graph Construction, 9–31. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-12375-8_2.

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Alqaryouti, Omar, Hassan Khwileh, Tarek Farouk, Ahmed Nabhan, and Khaled Shaalan. "Graph-Based Keyword Extraction." In Intelligent Natural Language Processing: Trends and Applications, 159–72. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67056-0_9.

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Shao, Yingxia, Bin Cui, and Lei Chen. "Efficient Parallel Graph Extraction." In Large-scale Graph Analysis: System, Algorithm and Optimization, 87–114. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3928-2_5.

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Gallego-Sánchez, Antonio-Javier, Jorge Calera-Rubio, and Damián López. "Structural Graph Extraction from Images." In Advances in Intelligent and Soft Computing, 717–24. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28765-7_86.

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Torre, Ilaria, Luca Mirenda, Gianni Vercelli, and Fulvio Mastrogiovanni. "Prerequisite Graph Extraction from Lectures." In Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium, 616–19. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-11647-6_128.

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He, Songtao, Favyen Bastani, Satvat Jagwani, Mohammad Alizadeh, Hari Balakrishnan, Sanjay Chawla, Mohamed M. Elshrif, Samuel Madden, and Mohammad Amin Sadeghi. "Sat2Graph: Road Graph Extraction Through Graph-Tensor Encoding." In Computer Vision – ECCV 2020, 51–67. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58586-0_4.

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Wang, Shuo, Qiushuo Zheng, Zherong Su, Chongning Na, and Guilin Qi. "MEED: A Multimodal Event Extraction Dataset." In Knowledge Graph and Semantic Computing: Knowledge Graph Empowers New Infrastructure Construction, 288–94. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-6471-7_23.

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Galmar, Eric, and Benoit Huet. "Graph-Based Spatio-temporal Region Extraction." In Lecture Notes in Computer Science, 236–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11867586_23.

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Duarte, Lucio Mauro, and Leila Ribeiro. "Graph Grammar Extraction from Source Code." In Lecture Notes in Computer Science, 52–69. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70848-5_5.

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Тези доповідей конференцій з теми "Graph extraction":

Shi, Yunzhou, and Yujiu Yang. "Relational Facts Extraction with Splitting Mechanism." In 2020 IEEE International Conference on Knowledge Graph (ICKG). IEEE, 2020. http://dx.doi.org/10.1109/icbk50248.2020.00060.

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Li, Qingquan, Qifan Zhang, Junjie Yao, and Yingjie Zhang. "Event Extraction for Criminal Legal Text." In 2020 IEEE International Conference on Knowledge Graph (ICKG). IEEE, 2020. http://dx.doi.org/10.1109/icbk50248.2020.00086.

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Zhong, Lingfeng, and Yi Zhu. "Relation Extraction with Proactive Domain Adaptation Strategy." In 2020 IEEE International Conference on Knowledge Graph (ICKG). IEEE, 2020. http://dx.doi.org/10.1109/icbk50248.2020.00069.

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Pu, Tianling, Qifan Zhang, Junjie Yao, and Yingjie Zhang. "Medical Entity Extraction from Health Insurance Documents." In 2020 IEEE International Conference on Knowledge Graph (ICKG). IEEE, 2020. http://dx.doi.org/10.1109/icbk50248.2020.00085.

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Yuan, Jiayi, Hongye Li, Meng Wang, Ruyang Liu, Chuanyou Li, and Beilun Wang. "An OpenCV-based Framework for Table Information Extraction." In 2020 IEEE International Conference on Knowledge Graph (ICKG). IEEE, 2020. http://dx.doi.org/10.1109/icbk50248.2020.00093.

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Han, Li. "Curvature-Constrained Feature Graph Extraction." In 2011 International Conference on Virtual Reality and Visualization (ICVRV). IEEE, 2011. http://dx.doi.org/10.1109/icvrv.2011.31.

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Mahon, Louis, Eleonora Giunchiglia, Bowen Li, and Thomas Lukasiewicz. "Knowledge Graph Extraction from Videos." In 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 2020. http://dx.doi.org/10.1109/icmla51294.2020.00014.

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Lv, Jianghai, Junping Du, Nan Zhou, and Zhe Xue. "BERT-BIGRU-CRF: A Novel Entity Relationship Extraction Model." In 2020 IEEE International Conference on Knowledge Graph (ICKG). IEEE, 2020. http://dx.doi.org/10.1109/icbk50248.2020.00032.

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Shen, Yinghan, Xuhui Jiang, Yuanzhuo Wang, Xiaolong Jin, and Xueqi Cheng. "Dynamic Relation Extraction with A Learnable Temporal Encoding Method." In 2020 IEEE International Conference on Knowledge Graph (ICKG). IEEE, 2020. http://dx.doi.org/10.1109/icbk50248.2020.00042.

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Yu, Erxin, Yantao Jia, Shang Wang, Fengfu Li, and Yi Chang. "Context and Type Enhanced Representation Learning for Relation Extraction." In 2020 IEEE International Conference on Knowledge Graph (ICKG). IEEE, 2020. http://dx.doi.org/10.1109/icbk50248.2020.00054.

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Звіти організацій з теми "Graph extraction":

Reedy, Geoffrey, Alex Bertels, and Asael Sorensen. Understanding Data Structures by Extracting Memory Access Graphs. Office of Scientific and Technical Information (OSTI), October 2017. http://dx.doi.org/10.2172/1813903.

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Reisch, Bruce, Pinhas Spiegel-Roy, Norman Weeden, Gozal Ben-Hayyim, and Jacques Beckmann. Genetic Analysis in vitis Using Molecular Markers. United States Department of Agriculture, April 1995. http://dx.doi.org/10.32747/1995.7613014.bard.

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Genetic analysis and mapping in grapes has been difficult because of the long generation period and paucity of genetic markers. In the present work, chromosome linkage maps were developed with RAPD, RFLP and isozyme loci in interspecific hybrid cultivars, and RAPD markers were produced in a V. vinifera population. In three cultivars, there were 19 linkage groups as expected for a species with 38 somatic chromosomes. These maps were used to locate chromosome regions with linkages to important genes, including those influencing powdery mildew and botrytis bunch rot resistance; flower sex; and berry shape. In V. vinifera, the occurrence of specific markers was correlated with seedlessness, muscat flavor and fruit color. Polymorphic RAPD bands included single copy as well as repetitive DNA. Mapping procedures were improved by optimizing PCR parameters with grape DNA; by the development of an efficient DNA extraction protocol; and with the use of long (17- to 24-mer) primers which amplify more polymorphic loci per primer. DNA fingerprint analysis with RAPD markers indicated that vinifera cultivars could be separated readily with RAPD profiles. Pinot gris, thought to be a sort of Pinot noir, differed by 12 bands from Pinot noir. This suggests that while Pinot gris may be related to Pinot noir, it is not likely to be a clone. The techniques developed in this project are now being further refined to use marker-assisted selection in breeding programs for the early selection of elite seedlings. Furthermore, the stage has been set for future attempts to clone genes from grapes based upon map locations.