Journal articles: 'Graph-based ranking'

1

Shams, Bita, and Saman Haratizadeh. "Graph-based collaborative ranking." Expert Systems with Applications 67 (January 2017): 59–70. http://dx.doi.org/10.1016/j.eswa.2016.09.013.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Shams, Bita, and Saman Haratizadeh. "Reliable graph-based collaborative ranking." Information Sciences 432 (March 2018): 116–32. http://dx.doi.org/10.1016/j.ins.2017.11.060.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Ramya, P., and J. Raja. "EMR a Scalable Graph-Based Ranking Model." Indian Journal of Public Health Research & Development 9, no. 2 (2018): 349. http://dx.doi.org/10.5958/0976-5506.2018.00146.8.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Li, Xuan, Liang Du, and Yi-Dong Shen. "Update Summarization via Graph-Based Sentence Ranking." IEEE Transactions on Knowledge and Data Engineering 25, no. 5 (May 2013): 1162–74. http://dx.doi.org/10.1109/tkde.2012.42.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Tang, Jinhui, Minxian Li, Zechao Li, and Chunxia Zhao. "Tag ranking based on salient region graph propagation." Multimedia Systems 21, no. 3 (February 15, 2014): 267–75. http://dx.doi.org/10.1007/s00530-014-0357-1.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

Zhu, Junnan, Lu Xiang, Yu Zhou, Jiajun Zhang, and Chengqing Zong. "Graph-based Multimodal Ranking Models for Multimodal Summarization." ACM Transactions on Asian and Low-Resource Language Information Processing 20, no. 4 (May 26, 2021): 1–21. http://dx.doi.org/10.1145/3445794.

Full text

Abstract:

Multimodal summarization aims to extract the most important information from the multimedia input. It is becoming increasingly popular due to the rapid growth of multimedia data in recent years. There are various researches focusing on different multimodal summarization tasks. However, the existing methods can only generate single-modal output or multimodal output. In addition, most of them need a lot of annotated samples for training, which makes it difficult to be generalized to other tasks or domains. Motivated by this, we propose a unified framework for multimodal summarization that can cover both single-modal output summarization and multimodal output summarization. In our framework, we consider three different scenarios and propose the respective unsupervised graph-based multimodal summarization models without the requirement of any manually annotated document-summary pairs for training: (1) generic multimodal ranking, (2) modal-dominated multimodal ranking, and (3) non-redundant text-image multimodal ranking. Furthermore, an image-text similarity estimation model is introduced to measure the semantic similarity between image and text. Experiments show that our proposed models outperform the single-modal summarization methods on both automatic and human evaluation metrics. Besides, our models can also improve the single-modal summarization with the guidance of the multimedia information. This study can be applied as the benchmark for further study on multimodal summarization task.

APA, Harvard, Vancouver, ISO, and other styles

7

Yao, Nan, Feng Qian, and Zuo Lei Sun. "Feature Dimension Reduction and Graph Based Ranking Based Image Classification." Applied Mechanics and Materials 380-384 (August 2013): 4035–38. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.4035.

Full text

Abstract:

Dimensionality reduction (DR) of image features plays an important role in image retrieval and classification tasks. Recently, two types of methods have been proposed to improve both the accuracy and efficiency for the dimensionality reduction problem. One uses Non-negative matrix factorization (NMF) to describe the image distribution on the space of base matrix. Another one for dimension reduction trains a subspace projection matrix to project original data space into some low-dimensional subspaces which have deep architecture, so that the low-dimensional codes would be learned. At the same time, the graph based similarity learning algorithm which tries to exploit contextual information for improving the effectiveness of image rankings is also proposed for image class and retrieval problem. In this paper, after above two methods mentioned are utilized to reduce the high-dimensional features of images respectively, we learn the graph based similarity for the image classification problem. This paper compares the proposed approach with other approaches on an image database.

APA, Harvard, Vancouver, ISO, and other styles

8

Devezas, José. "Graph-based entity-oriented search." ACM SIGIR Forum 55, no. 1 (June 2021): 1–2. http://dx.doi.org/10.1145/3476415.3476430.

Full text

Abstract:

Entity-oriented search has revolutionized search engines. In the era of Google Knowledge Graph and Microsoft Satori, users demand an effortless process of search. Whether they express an information need through a keyword query, expecting documents and entities, or through a clicked entity, expecting related entities, there is an inherent need for the combination of corpora and knowledge bases to obtain an answer. Such integration frequently relies on independent signals extracted from inverted indexes, and from quad indexes indirectly accessed through queries to a triplestore. However, relying on two separate representation models inhibits the effective cross-referencing of information, discarding otherwise available relations that could lead to a better ranking. Moreover, different retrieval tasks often demand separate implementations, although the problem is, at its core, the same. With the goal of harnessing all available information to optimize retrieval, we explore joint representation models of documents and entities, while taking a step towards the definition of a more general retrieval approach. Specifically, we propose that graphs should be used to incorporate explicit and implicit information derived from the relations between text found in corpora and entities found in knowledge bases. We also take advantage of this framework to elaborate a general model for entity-oriented search, proposing a universal ranking function for the tasks of ad hoc document retrieval (leveraging entities), ad hoc entity retrieval, and entity list completion. At a conceptual stage, we begin by proposing the graph-of-entity, based on the relations between combinations of term and entity nodes. We introduce the entity weight as the corresponding ranking function, relying on the idea of seed nodes for representing the query, either directly through term nodes, or based on the expansion to adjacent entity nodes. The score is computed based on a series of geodesic distances to the remaining nodes, providing a ranking for the documents (or entities) in the graph. In order to improve on the low scalability of the graph-of-entity, we then redesigned this model in a way that reduced the number of edges in relation to the number of nodes, by relying on the hypergraph data structure. The resulting model, which we called hypergraph-of-entity, is the main contribution of this thesis. The obtained reduction was achieved by replacing binary edges with n -ary relations based on sets of nodes and entities (undirected document hyperedges), sets of entities (undirected hyperedges, either based on cooccurrence or a grouping by semantic subject), and pairs of a set of terms and a set of one entity (directed hyperedges, mapping text to an object). We introduce the random walk score as the corresponding ranking function, relying on the same idea of seed nodes, similar to the entity weight in the graph-of-entity. Scoring based on this function is highly reliant on the structure of the hypergraph, which we call representation-driven retrieval. As such, we explore several extensions of the hypergraph-of-entity, including relations of synonymy, or contextual similarity, as well as different weighting functions per node and hyperedge type. We also propose TF-bins as a discretization for representing term frequency in the hypergraph-of-entity. For the random walk score, we propose and explore several parameters, including length and repeats, with or without seed node expansion, direction, or weights, and with or without a certain degree of node and/or hyperedge fatigue, a concept that we also propose. For evaluation, we took advantage of TREC 2017 OpenSearch track, which relied on an online evaluation process based on the Living Labs API, and we also participated in TREC 2018 Common Core track, which was based on the newly introduced TREC Washington Post Corpus. Our main experiments were supported on the INEX 2009 Wikipedia collection, which proved to be a fundamental test collection for assessing retrieval effectiveness across multiple tasks. At first, our experiments solely focused on ad hoc document retrieval, ensuring that the model performed adequately for a classical task. We then expanded the work to cover all three entity-oriented search tasks. Results supported the viability of a general retrieval model, opening novel challenges in information retrieval, and proposing a new path towards generality in this area.

APA, Harvard, Vancouver, ISO, and other styles

9

He, Dongbin, Minjuan Wang, Abdul Mateen Khattak, Li Zhang, and Wanlin Gao. "Automatic Labeling of Topic Models Using Graph-Based Ranking." IEEE Access 7 (2019): 131593–608. http://dx.doi.org/10.1109/access.2019.2940516.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Yan, Yan, Gaowen Liu, Sen Wang, Jian Zhang, and Kai Zheng. "Graph-based clustering and ranking for diversified image search." Multimedia Systems 23, no. 1 (September 24, 2014): 41–52. http://dx.doi.org/10.1007/s00530-014-0419-4.

Full text

APA, Harvard, Vancouver, ISO, and other styles

11

Li, Hong, Wen Wu, and Enhua Wu. "Robust interactive image segmentation via graph-based manifold ranking." Computational Visual Media 1, no. 3 (September 2015): 183–95. http://dx.doi.org/10.1007/s41095-015-0024-2.

Full text

APA, Harvard, Vancouver, ISO, and other styles

12

Qi, Siyuan, and Yupin Luo. "Object retrieval with image graph traversal-based re-ranking." Signal Processing: Image Communication 41 (February 2016): 101–14. http://dx.doi.org/10.1016/j.image.2015.12.004.

Full text

APA, Harvard, Vancouver, ISO, and other styles

13

Park, Junekyu, and KyungAh Sohn. "Improving BERT-based Sentiment Analysis Model using Graph-based Ranking Mechanism." KIISE Transactions on Computing Practices 27, no. 2 (February 28, 2021): 90–97. http://dx.doi.org/10.5626/ktcp.2021.27.2.90.

Full text

APA, Harvard, Vancouver, ISO, and other styles

14

Wu, Jun, Yu He, Xiaohong Qin, Na Zhao, and Yingpeng Sang. "Click-boosted graph ranking for image retrieval." Computer Science and Information Systems 14, no. 3 (2017): 629–41. http://dx.doi.org/10.2298/csis170212020j.

Full text

Abstract:

Graph ranking is one popular and successful technique for image retrieval, but its effectiveness is often limited by the well-known semantic gap. To bridge this gap, one of the current trends is to leverage the click-through data associated with images to facilitate the graph-based image ranking. However, the sparse and noisy properties of the image click-through data make the exploration of such resource challenging. Towards this end, this paper propose a novel click-boosted graph ranking framework for image retrieval, which consists of two coupled components. Concretely, the first one is a click predictor based on matrix factorization with visual regularization, in order to alleviate the sparseness of the click-through data. The second component is a soft-label graph ranker that conducts the image ranking by using the enriched click-through data noise-tolerantly. Extensive experiments for the tasks of click predicting and image ranking validate the effectiveness of the proposed methods in comparison to several existing approaches.

APA, Harvard, Vancouver, ISO, and other styles

15

Saha, Aadirupa, Rakesh Shivanna, and Chiranjib Bhattacharyya. "How Many Pairwise Preferences Do We Need to Rank a Graph Consistently?" Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 4830–37. http://dx.doi.org/10.1609/aaai.v33i01.33014830.

Full text

Abstract:

We consider the problem of optimal recovery of true ranking of n items from a randomly chosen subset of their pairwise preferences. It is well known that without any further assumption, one requires a sample size of Ω(n2) for the purpose. We analyze the problem with an additional structure of relational graph G([n],E) over the n items added with an assumption of locality: Neighboring items are similar in their rankings. Noting the preferential nature of the data, we choose to embed not the graph, but, its strong product to capture the pairwise node relationships. Furthermore, unlike existing literature that uses Laplacian embedding for graph based learning problems, we use a richer class of graph embeddings—orthonormal representations—that includes (normalized) Laplacian as its special case. Our proposed algorithm, Pref-Rank, predicts the underlying ranking using an SVM based approach using the chosen embedding of the product graph, and is the first to provide statistical consistency on two ranking losses: Kendall’s tau and Spearman’s footrule, with a required sample complexity of O(n2χ(G¯))⅔ pairs, χ(G¯) being the chromatic number of the complement graph G¯. Clearly, our sample complexity is smaller for dense graphs, with χ(G¯) characterizing the degree of node connectivity, which is also intuitive due to the locality assumption e.g. O(n4/3) for union of k-cliques, or O(n5/3) for random and power law graphs etc.—a quantity much smaller than the fundamental limit of Ω(n2) for large n. This, for the first time, relates ranking complexity to structural properties of the graph. We also report experimental evaluations on different synthetic and real-world datasets, where our algorithm is shown to outperform the state of the art methods.

APA, Harvard, Vancouver, ISO, and other styles

16

Daoud, Mariam, Lynda Tamine, and Mohand Boughanem. "A personalized search using a semantic distance measure in a graph-based ranking model." Journal of Information Science 37, no. 6 (November 14, 2011): 614–36. http://dx.doi.org/10.1177/0165551511420220.

Full text

Abstract:

The goal of search personalization is to tailor search results to individual users by taking into account their profiles, which include their particular interests and preferences. As these latter are multiple and change over time, personalization becomes effective when the search process takes into account the current user interest. This article presents a search personalization approach that models a semantic user profile and focuses on a personalized document ranking model based on an extended graph-based distance measure. Documents and user profiles are both represented by graphs of concepts issued from predefined web ontology, namely, the Open Directory Project directory (ODP). Personalization is then based on reordering the search results of related queries according to a graph-based document ranking model. This former is based on using a graph-based distance measure combining the minimum common supergraph and the maximum common subgraph between the document and the user profile graphs. We extend this measure in order to take into account a semantic recovery at exact and approximate concept-level matching. Experimental results show the effectiveness of our personalized graph-based ranking model compared with Yahoo and different personalized ranking models performed using classical graph-based measures or vector-space similarity measures.

APA, Harvard, Vancouver, ISO, and other styles

17

Wang, Xu, Shuai Zhao, Bo Cheng, Jiale Han, Yingting Li, Hao Yang, and Guoshun Nan. "HGMAN: Multi-Hop and Multi-Answer Question Answering Based on Heterogeneous Knowledge Graph (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 10 (April 3, 2020): 13953–54. http://dx.doi.org/10.1609/aaai.v34i10.7249.

Full text

Abstract:

Multi-hop question answering models based on knowledge graph have been extensively studied. Most existing models predict a single answer with the highest probability by ranking candidate answers. However, they are stuck in predicting all the right answers caused by the ranking method. In this paper, we propose a novel model that converts the ranking of candidate answers into individual predictions for each candidate, named heterogeneous knowledge graph based multi-hop and multi-answer model (HGMAN). HGMAN is capable of capturing more informative representations for relations assisted by our heterogeneous graph, which consists of multiple entity nodes and relation nodes. We rely on graph convolutional network for multi-hop reasoning and then binary classification for each node to get multiple answers. Experimental results on MetaQA dataset show the performance of our proposed model over all baselines.

APA, Harvard, Vancouver, ISO, and other styles

18

Premsai, C. "Traffic Sign Detection Via Graph-Based Ranking and Segmentation Algorithm." International Journal for Research in Applied Science and Engineering Technology V, no. IV (April 26, 2017): 897–904. http://dx.doi.org/10.22214/ijraset.2017.4164.

Full text

APA, Harvard, Vancouver, ISO, and other styles

19

Wang, Bin, Feng Pan, Kai-Mo Hu, and Jean-Claude Paul. "Manifold-ranking based retrieval using k-regular nearest neighbor graph." Pattern Recognition 45, no. 4 (April 2012): 1569–77. http://dx.doi.org/10.1016/j.patcog.2011.09.006.

Full text

APA, Harvard, Vancouver, ISO, and other styles

20

Fareed, Mian Muhammad Sadiq, Gulnaz Ahmed, and Qi Chun. "Salient region detection through sparse reconstruction and graph-based ranking." Journal of Visual Communication and Image Representation 32 (October 2015): 144–55. http://dx.doi.org/10.1016/j.jvcir.2015.08.002.

Full text

APA, Harvard, Vancouver, ISO, and other styles

21

Yuan, Xue, Jiaqi Guo, Xiaoli Hao, and Houjin Chen. "Traffic Sign Detection via Graph-Based Ranking and Segmentation Algorithms." IEEE Transactions on Systems, Man, and Cybernetics: Systems 45, no. 12 (December 2015): 1509–21. http://dx.doi.org/10.1109/tsmc.2015.2427771.

Full text

APA, Harvard, Vancouver, ISO, and other styles

22

Herskovic, Jorge R., Trevor Cohen, Devika Subramanian, M. Sriram Iyengar, Jack W. Smith, and Elmer V. Bernstam. "MEDRank: Using graph-based concept ranking to index biomedical texts." International Journal of Medical Informatics 80, no. 6 (June 2011): 431–41. http://dx.doi.org/10.1016/j.ijmedinf.2011.02.008.

Full text

APA, Harvard, Vancouver, ISO, and other styles

23

Xue, Han, Bing Qin, and Ting Liu. "Topical key concept extraction from folksonomy through graph-based ranking." Multimedia Tools and Applications 75, no. 15 (October 12, 2014): 8875–93. http://dx.doi.org/10.1007/s11042-014-2303-9.

Full text

APA, Harvard, Vancouver, ISO, and other styles

24

Bin Xu, Jiajun Bu, Chun Chen, Can Wang, Deng Cai, and Xiaofei He. "EMR: A Scalable Graph-Based Ranking Model for Content-Based Image Retrieval." IEEE Transactions on Knowledge and Data Engineering 27, no. 1 (January 2015): 102–14. http://dx.doi.org/10.1109/tkde.2013.70.

Full text

APA, Harvard, Vancouver, ISO, and other styles

25

Kim, Heung-Nam, Mark Bloess, and Abdulmotaleb El Saddik. "Folkommender: a group recommender system based on a graph-based ranking algorithm." Multimedia Systems 19, no. 6 (December 25, 2012): 509–25. http://dx.doi.org/10.1007/s00530-012-0298-5.

Full text

APA, Harvard, Vancouver, ISO, and other styles

26

Kim, Hyun-Jin, Ji-Won Baek, and Kyungyong Chung. "Optimization of Associative Knowledge Graph using TF-IDF based Ranking Score." Applied Sciences 10, no. 13 (July 2, 2020): 4590. http://dx.doi.org/10.3390/app10134590.

Full text

Abstract:

This study proposes the optimization method of the associative knowledge graph using TF-IDF based ranking scores. The proposed method calculates TF-IDF weights in all documents and generates term ranking. Based on the terms with high scores from TF-IDF based ranking, optimized transactions are generated. News data are first collected through crawling and then are converted into a corpus through preprocessing. Unnecessary data are removed through preprocessing including lowercase conversion, removal of punctuation marks and stop words. In the document term matrix, words are extracted and then transactions are generated. In the data cleaning process, the Apriori algorithm is applied to generate association rules and make a knowledge graph. To optimize the generated knowledge graph, the proposed method utilizes TF-IDF based ranking scores to remove terms with low scores and recreate transactions. Based on the result, the association rule algorithm is applied to create an optimized knowledge model. The performance is evaluated in rule generation speed and usefulness of association rules. The association rule generation speed of the proposed method is about 22 seconds faster. And the lift value of the proposed method for usefulness is about 0.43 to 2.51 higher than that of each one of conventional association rule algorithms.

APA, Harvard, Vancouver, ISO, and other styles

27

Khan, Atif, Muhammad Adnan Gul, Mahdi Zareei, R. R. Biswal, Asim Zeb, Muhammad Naeem, Yousaf Saeed, and Naomie Salim. "Movie Review Summarization Using Supervised Learning and Graph-Based Ranking Algorithm." Computational Intelligence and Neuroscience 2020 (June 2, 2020): 1–14. http://dx.doi.org/10.1155/2020/7526580.

Full text

Abstract:

With the growing information on web, online movie review is becoming a significant information resource for Internet users. However, online users post thousands of movie reviews on daily basis and it is hard for them to manually summarize the reviews. Movie review mining and summarization is one of the challenging tasks in natural language processing. Therefore, an automatic approach is desirable to summarize the lengthy movie reviews, and it will allow users to quickly recognize the positive and negative aspects of a movie. This study employs a feature extraction technique called bag of words (BoW) to extract features from movie reviews and represent the reviews as a vector space model or feature vector. The next phase uses Naïve Bayes machine learning algorithm to classify the movie reviews (represented as feature vector) into positive and negative. Next, an undirected weighted graph is constructed from the pairwise semantic similarities between classified review sentences in such a way that the graph nodes represent review sentences, while the edges of graph indicate semantic similarity weight. The weighted graph-based ranking algorithm (WGRA) is applied to compute the rank score for each review sentence in the graph. Finally, the top ranked sentences (graph nodes) are chosen based on highest rank scores to produce the extractive summary. Experimental results reveal that the proposed approach is superior to other state-of-the-art approaches.

APA, Harvard, Vancouver, ISO, and other styles

28

Varade, Saurabh, Ejaaz Sayyed, Vaibhavi Nagtode, and Shilpa Shinde. "Text Summarization using Extractive and Abstractive Methods." ITM Web of Conferences 40 (2021): 03023. http://dx.doi.org/10.1051/itmconf/20214003023.

Full text

Abstract:

Text Summarization is a process where a huge text file is converted into summarized version which will preserve the original meaning and context. The main aim of any text summarization is to provide a accurate and precise summary. One approach is to use a sentence ranking algorithm. This comes under extractive summarization. Here, a graph based ranking algorithm is used to rank the sentences in the text and then top k-scored sentences are included in the summary. The most widely used algorithm to decide the importance of any vertex in a graph based on the information retrieved from the graph is Graph Based Ranking Algorithm. TextRank is one of the most efficient ranking algorithms which is used for Web link analysis that is for measuring the importance of website pages. Another approach is abstractive summarization where a LSTM encoder decoder model is used along with attention mechanism which focuses on some important words from the input. Encoder encodes the input sequence and decoder along with attention mechanism gives the summary as the output.

APA, Harvard, Vancouver, ISO, and other styles

29

He, Y., X. Wang, X. Y. Hu, and S. H. Liu. "MAN-MADE OBJECT EXTRACTION FROM REMOTE SENSING IMAGERY BY GRAPH-BASED MANIFOLD RANKING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (April 30, 2018): 511–16. http://dx.doi.org/10.5194/isprs-archives-xlii-3-511-2018.

Full text

Abstract:

The automatic extraction of man-made objects from remote sensing imagery is useful in many applications. This paper proposes an algorithm for extracting man-made objects automatically by integrating a graph model with the manifold ranking algorithm. Initially, we estimate a priori value of the man-made objects with the use of symmetric and contrast features. The graph model is established to represent the spatial relationships among pre-segmented superpixels, which are used as the graph nodes. Multiple characteristics, namely colour, texture and main direction, are used to compute the weights of the adjacent nodes. Manifold ranking effectively explores the relationships among all the nodes in the feature space as well as initial query assignment; thus, it is applied to generate a ranking map, which indicates the scores of the man-made objects. The man-made objects are then segmented on the basis of the ranking map. Two typical segmentation algorithms are compared with the proposed algorithm. Experimental results show that the proposed algorithm can extract man-made objects with high recognition rate and low omission rate.

APA, Harvard, Vancouver, ISO, and other styles

30

Qi, Xiaojun, and Ran Chang. "A Scalable Graph-Based Semi-Supervised Ranking System for Content-Based Image Retrieval." International Journal of Multimedia Data Engineering and Management 4, no. 4 (October 2013): 15–34. http://dx.doi.org/10.4018/ijmdem.2013100102.

Full text

Abstract:

The authors propose a scalable graph-based semi-supervised ranking system for image retrieval. This system exploits the synergism between relevance feedback based transductive short-term learning and semantic feature-based long-term learning to improve retrieval performance. Active learning is applied to build a dynamic feedback log to extract semantic features of images. Two-layer manifold graphs are then built in both low-level visual and high-level semantic spaces. One graph is constructed at the first layer using anchor images obtained from the feedback log. Several graphs are constructed at the second layer using images in their respective cluster formed around each anchor image. An asymmetric relevance vector is created for each second layer graph by propagating initial scores from the first layer. These vectors are fused to propagate relevance scores of labeled images to unlabeled images. The authors’ extensive experiments demonstrate the proposed system outperforms four manifold-based and five state-of-the-art long-term-based image retrieval systems.

APA, Harvard, Vancouver, ISO, and other styles

31

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

32

ORTEGA, F. JAVIER, JOSÉ A. TROYANO, FRANCISCO J. GALÁN, CARLOS G. VALLEJO, and FERMÍN CRUZ. "STR: A GRAPH-BASED TAGGING TECHNIQUE." International Journal on Artificial Intelligence Tools 20, no. 05 (October 2011): 955–67. http://dx.doi.org/10.1142/s0218213011000437.

Full text

Abstract:

This paper presents the ideas, experiments and specifications related to the Supervised TextRank (STR) technique, a word tagging method based on the TextRank algorithm. The main innovation of STR technique is the use of a graph-based ranking algorithm similar to PageRank in a supervised fashion, gathering the information needed to build the graph representations of the text from a tagged corpus. We also propose a flexible graph specification language that allows to easily experiment with multiple configurations for the topology of the graph and for the information associated to the nodes and the edges. We have carried experiments in the Part-Of-Speech task, a common tagging problem in Natural Language Processing. In our best result we have achieved a precision of 96.16%, at the same level of the best tagging tools.

APA, Harvard, Vancouver, ISO, and other styles

33

Zhang, Hao, and Jie Wang. "An unsupervised semantic sentence ranking scheme for text documents." Integrated Computer-Aided Engineering 28, no. 1 (December 21, 2020): 17–33. http://dx.doi.org/10.3233/ica-200626.

Full text

Abstract:

This paper presents Semantic SentenceRank (SSR), an unsupervised scheme for automatically ranking sentences in a single document according to their relative importance. In particular, SSR extracts essential words and phrases from a text document, and uses semantic measures to construct, respectively, a semantic phrase graph over phrases and words, and a semantic sentence graph over sentences. It applies two variants of article-structure-biased PageRank to score phrases and words on the first graph and sentences on the second graph. It then combines these scores to generate the final score for each sentence. Finally, SSR solves a multi-objective optimization problem for ranking sentences based on their final scores and topic diversity through semantic subtopic clustering. An implementation of SSR that runs in quadratic time is presented, and it outperforms, on the SummBank benchmarks, each individual judge’s ranking and compares favorably with the combined ranking of all judges.

APA, Harvard, Vancouver, ISO, and other styles

34

., Krithika L. B. "TEST CASE PRIORITIZATION USING HYPERLINK RANKING - A GRAPH THEORY BASED APPROACH." International Journal of Research in Engineering and Technology 02, no. 11 (November 25, 2013): 29–32. http://dx.doi.org/10.15623/ijret.2013.0211005.

Full text

APA, Harvard, Vancouver, ISO, and other styles

35

Abd El-Wahab, Marwa Gaber, Amal Elsayed Aboutabl, and Wessam M. H. EL Behaidy. "Graph Mining for Software Fault Localization: An Edge Ranking based Approach." Journal of Communications Software and Systems 13, no. 4 (2018): 178–88. http://dx.doi.org/10.24138/jcomss.v13i4.402.

Full text

APA, Harvard, Vancouver, ISO, and other styles

36

Sidiropoulos, Antonis, and Yannis Manolopoulos. "Generalized comparison of graph-based ranking algorithms for publications and authors." Journal of Systems and Software 79, no. 12 (December 2006): 1679–700. http://dx.doi.org/10.1016/j.jss.2006.01.011.

Full text

APA, Harvard, Vancouver, ISO, and other styles

37

Qi, Shuhan, Xuan Wang, Xi Zhang, Xuemeng Song, and Zoe L. Jiang. "Scalable graph based non-negative multi-view embedding for image ranking." Neurocomputing 274 (January 2018): 29–36. http://dx.doi.org/10.1016/j.neucom.2016.06.097.

Full text

APA, Harvard, Vancouver, ISO, and other styles

38

Zhong, Zhaoman, and Zongtian Liu. "Ranking Events Based on Event Relation Graph for a Single Document." Information Technology Journal 9, no. 1 (December 15, 2009): 174–78. http://dx.doi.org/10.3923/itj.2010.174.178.

Full text

APA, Harvard, Vancouver, ISO, and other styles

39

Deng, Cheng, Xu Yang, Feiping Nie, and Dapeng Tao. "Saliency Detection via a Multiple Self-Weighted Graph-Based Manifold Ranking." IEEE Transactions on Multimedia 22, no. 4 (April 2020): 885–96. http://dx.doi.org/10.1109/tmm.2019.2934833.

Full text

APA, Harvard, Vancouver, ISO, and other styles

40

Wang, Chengyu, Guomin Zhou, Xiaofeng He, and Aoying Zhou. "NERank+: a graph-based approach for entity ranking in document collections." Frontiers of Computer Science 12, no. 3 (May 11, 2018): 504–17. http://dx.doi.org/10.1007/s11704-017-6471-4.

Full text

APA, Harvard, Vancouver, ISO, and other styles

41

Jiang, Xiaorui, Xiaoping Sun, and Hai Zhuge. "Graph-based algorithms for ranking researchers: not all swans are white!" Scientometrics 96, no. 3 (January 13, 2013): 743–59. http://dx.doi.org/10.1007/s11192-012-0943-y.

Full text

APA, Harvard, Vancouver, ISO, and other styles

42

Gao, Shengxiang, Zhengtao Yu, Yunlong Li, Yusen Wang, and Yafei Zhang. "Chinese–Vietnamese bilingual news event summarization based on distributed graph ranking." Journal of Supercomputing 76, no. 2 (November 21, 2019): 1034–48. http://dx.doi.org/10.1007/s11227-019-03006-1.

Full text

APA, Harvard, Vancouver, ISO, and other styles

43

Christoforou, Evgenia, Alessandro Nordio, Alberto Tarable, and Emilio Leonardi. "Ranking a Set of Objects: A Graph Based Least-Square Approach." IEEE Transactions on Network Science and Engineering 8, no. 1 (January 1, 2021): 803–13. http://dx.doi.org/10.1109/tnse.2021.3053423.

Full text

APA, Harvard, Vancouver, ISO, and other styles

44

Gao, Wei, and Tianwei Xu. "Stability Analysis of Learning Algorithms for Ontology Similarity Computation." Abstract and Applied Analysis 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/174802.

Full text

Abstract:

Ontology, as a useful tool, is widely applied in lots of areas such as social science, computer science, and medical science. Ontology concept similarity calculation is the key part of the algorithms in these applications. A recent approach is to make use of similarity between vertices on ontology graphs. It is, instead of pairwise computations, based on a function that maps the vertex set of an ontology graph to real numbers. In order to obtain this, the ranking learning problem plays an important and essential role, especiallyk-partite ranking algorithm, which is suitable for solving some ontology problems. A ranking function is usually used to map the vertices of an ontology graph to numbers and assign ranks of the vertices through their scores. Through studying a training sample, such a function can be learned. It contains a subset of vertices of the ontology graph. A good ranking function means small ranking mistakes and good stability. For ranking algorithms, which are in a well-stable state, we study generalization bounds via some concepts of algorithmic stability. We also find that kernel-based ranking algorithms stated as regularization schemes in reproducing kernel Hilbert spaces satisfy stability conditions and have great generalization abilities.

APA, Harvard, Vancouver, ISO, and other styles

45

Boaventura-Netto, Paulo Oswaldo. "Ranking graph edges by the weight of their spanning arborescences or trees." Pesquisa Operacional 28, no. 1 (April 2008): 59–73. http://dx.doi.org/10.1590/s0101-74382008000100004.

Full text

Abstract:

A result based on a classic theorem of graph theory is generalized for edge-valued graphs, allowing determination of the total value of the spanning arborescences with a given root and containing a given arc in a directed valued graph. A corresponding result for undirected valued graphs is also presented. In both cases, the technique allows for a ranking of the graph edges by importance under this criterion. This ranking is proposed as a tool to determine the relative importance of the edges of a graph in network vulnerability studies. Some examples of application are presented.

APA, Harvard, Vancouver, ISO, and other styles

46

Yeh, Jen-Yuan, and Cheng-Jung Tsai. "A graph-based feature selection method for learning to rank using spectral clustering for redundancy minimization and biased PageRank for relevance analysis." Computer Science and Information Systems, no. 00 (2021): 42. http://dx.doi.org/10.2298/csis201220042y.

Full text

Abstract:

This paper addresses the feature selection problem in learning to rank (LTR). We propose a graph-based feature selection method, named FS-SCPR, which comprises four steps: (i) use ranking information to assess the similarity between features and construct an undirected feature similarity graph; (ii) apply spectral clustering to cluster features using eigenvectors of matrices extracted from the graph; (iii) utilize biased PageRank to assign a relevance score with respect to the ranking problem to each feature by incorporating each feature?s ranking performance as preference to bias the PageRank computation; and (iv) apply optimization to select the feature from each cluster with both the highest relevance score and most information of the features in the cluster. We also develop a new LTR for information retrieval (IR) approach that first exploits FS-SCPR as a preprocessor to determine discriminative and useful features and then employs Ranking SVM to derive a ranking model with the selected features. An evaluation, conducted using the LETOR benchmark datasets, demonstrated the competitive performance of our approach compared to representative feature selection methods and state-of-the-art LTR methods.

APA, Harvard, Vancouver, ISO, and other styles

47

Maurya, Sunil Kumar, Xin Liu, and Tsuyoshi Murata. "Graph Neural Networks for Fast Node Ranking Approximation." ACM Transactions on Knowledge Discovery from Data 15, no. 5 (June 26, 2021): 1–32. http://dx.doi.org/10.1145/3446217.

Full text

Abstract:

Graphs arise naturally in numerous situations, including social graphs, transportation graphs, web graphs, protein graphs, etc. One of the important problems in these settings is to identify which nodes are important in the graph and how they affect the graph structure as a whole. Betweenness centrality and closeness centrality are two commonly used node ranking measures to find out influential nodes in the graphs in terms of information spread and connectivity. Both of these are considered as shortest path based measures as the calculations require the assumption that the information flows between the nodes via the shortest paths. However, exact calculations of these centrality measures are computationally expensive and prohibitive, especially for large graphs. Although researchers have proposed approximation methods, they are either less efficient or suboptimal or both. We propose the first graph neural network (GNN) based model to approximate betweenness and closeness centrality. In GNN, each node aggregates features of the nodes in multihop neighborhood. We use this feature aggregation scheme to model paths and learn how many nodes are reachable to a specific node. We demonstrate that our approach significantly outperforms current techniques while taking less amount of time through extensive experiments on a series of synthetic and real-world datasets. A benefit of our approach is that the model is inductive, which means it can be trained on one set of graphs and evaluated on another set of graphs with varying structures. Thus, the model is useful for both static graphs and dynamic graphs. Source code is available at https://github.com/sunilkmaurya/GNN_Ranking

APA, Harvard, Vancouver, ISO, and other styles

48

Gao, Jianliang, Xiaoting Ying, Cong Xu, Jianxin Wang, Shichao Zhang, and Zhao Li. "Graph-Based Stock Recommendation by Time-Aware Relational Attention Network." ACM Transactions on Knowledge Discovery from Data 16, no. 1 (July 3, 2021): 1–21. http://dx.doi.org/10.1145/3451397.

Full text

Abstract:

The stock market investors aim at maximizing their investment returns. Stock recommendation task is to recommend stocks with higher return ratios for the investors. Most stock prediction methods study the historical sequence patterns to predict stock trend or price in the near future. In fact, the future price of a stock is correlated not only with its historical price, but also with other stocks. In this article, we take into account the relationships between stocks (corporations) by stock relation graph. Furthermore, we propose a Time-aware Relational Attention Network (TRAN) for graph-based stock recommendation according to return ratio ranking. In TRAN, the time-aware relational attention mechanism is designed to capture time-varying correlation strengths between stocks by the interaction of historical sequences and stock description documents. With the dynamic strengths, the nodes of the stock relation graph aggregate the features of neighbor stock nodes by graph convolution operation. For a given group of stocks, the proposed TRAN model can output the ranking results of stocks according to their return ratios. The experimental results on several real-world datasets demonstrate the effectiveness of our TRAN for stock recommendation.

APA, Harvard, Vancouver, ISO, and other styles

49

Zhang, Xiao, Meng Liu, Jianhua Yin, Zhaochun Ren, and Liqiang Nie. "Question Tagging via Graph-guided Ranking." ACM Transactions on Information Systems 40, no. 1 (January 31, 2022): 1–23. http://dx.doi.org/10.1145/3468270.

Full text

Abstract:

With the increasing prevalence of portable devices and the popularity of community Question Answering (cQA) sites, users can seamlessly post and answer many questions. To effectively organize the information for precise recommendation and easy searching, these platforms require users to select topics for their raised questions. However, due to the limited experience, certain users fail to select appropriate topics for their questions. Thereby, automatic question tagging becomes an urgent and vital problem for the cQA sites, yet it is non-trivial due to the following challenges. On the one hand, vast and meaningful topics are available yet not utilized in the cQA sites; how to model and tag them to relevant questions is a highly challenging problem. On the other hand, related topics in the cQA sites may be organized into a directed acyclic graph. In light of this, how to exploit relations among topics to enhance their representations is critical. To settle these challenges, we devise a graph-guided topic ranking model to tag questions in the cQA sites appropriately. In particular, we first design a topic information fusion module to learn the topic representation by jointly considering the name and description of the topic. Afterwards, regarding the special structure of topics, we propose an information propagation module to enhance the topic representation. As the comprehension of questions plays a vital role in question tagging, we design a multi-level context-modeling-based question encoder to obtain the enhanced question representation. Moreover, we introduce an interaction module to extract topic-aware question information and capture the interactive information between questions and topics. Finally, we utilize the interactive information to estimate the ranking scores for topics. Extensive experiments on three Chinese cQA datasets have demonstrated that our proposed model outperforms several state-of-the-art competitors.

APA, Harvard, Vancouver, ISO, and other styles

50

GUO Shao-jun, 郭少军, 娄树理 LOU Shu-li, and 刘峰 LIU Feng. "Ship-target saliency detection via image fusion and graph-based manifold ranking." Chinese Journal of Liquid Crystals and Displays 31, no. 10 (2016): 1006–15. http://dx.doi.org/10.3788/yjyxs20163110.1006.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Journal articles on the topic 'Graph-based ranking'

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles