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Discovering Representative Space for Relational Similarity Measurement [chapter]

Huda Hakami, Angrosh Mandya, Danushka Bollegala
2018 Communications in Computer and Information Science  
In contrast, we propose a data-driven approach for discovering feature spaces for relational similarity measurement.  ...  We evaluate the discovered feature space by measuring the relational similarity for relational classification task in which we aim to classify a given word-pair to a specific relation from a predefined  ...  CONCLUSION We proposed the first-ever method for discovering a discriminative feature space for measuring relational similarity from data.  ... 
doi:10.1007/978-981-10-8438-6_7 fatcat:xwsjwgb2tbh4thid7cbi7rvbty

Page 390 of Computational Linguistics Vol. 32, Issue 3 [page]

2006 Computational Linguistics  
The Vector Space Model [his section examines past work on measuring attributional and relational similarity using the VSM. 4.1 Measuring Attributional Similarity with the Vector Space Model [he VSM was  ...  We believe that it may be helpful to view semantic frames and their semantic roles as sets of semantic relations; thus, a measure of relational similarity should help us to identify semantic roles.  ... 

Discovering biomedical semantic relations in PubMed queries for information retrieval and database curation

Chung-Chi Huang, Zhiyong Lu
2016 Database: The Journal of Biological Databases and Curation  
Discovering biomedical semantic relations in PubMed queries for information retrieval and database curation. Abstract Identifying relevant papers from the literature is a common task in biocuration.  ...  Finally, we mine semantically similar contextual patterns or semantic relations based on LSA topic distributions.  ...  Michael Simmons for his help of proofreading this article.  ... 
doi:10.1093/database/baw025 pmid:27016698 pmcid:PMC4808250 fatcat:l4mzj2p4fzeolkadktsejrmdf4

The need for metrics in visual information analysis

Nancy Miller, Beth Hetzler, Grant Nakamura, Paul Whitney
1997 Proceedings of the 1997 workshop on New paradigms in information visualization and manipulation - NPIV '97  
Fractals have a different intrinsic measure of similarity from the Euclidean space and cosine similarity projections typically used in information visualization.  ...  These signals form an n-dimensional vector representation of the information space; the n-dimensional vector for each document represents how strongly that document is related to each of n key topics for  ... 
doi:10.1145/275519.275523 dblp:conf/cikm/MillerHNW97 fatcat:bet322yk2va3ble7dpsiuxewfa

Automatic Feature Detection and Clustering Using Random Indexing [chapter]

Haïfa Nakouri, Mohamed Limam
2014 Lecture Notes in Computer Science  
We propose an automatic approach of image parsing, feature extraction, indexing and clustering, showing that the Feature Space model based on Random Indexing captures the semantic relation between similar  ...  Random Indexing is an incremental indexing approach that simultaneously performs an implicit Dimensionality Reduction and discovers higher order relations among features lying in the vector space.  ...  The collection of vectors all together represents the semantic nature of related features and image contexts.  ... 
doi:10.1007/978-3-319-07998-1_67 fatcat:w5vckuskdravtkjbbr2vjfkcai

Reinforcement Learning Transfer Using a Sparse Coded Inter-task Mapping [chapter]

Haitham Bou Ammar, Matthew E. Taylor, Karl Tuyls, Gerhard Weiss
2012 Lecture Notes in Computer Science  
Transfer improves learning by reusing learned behaviors in similar tasks, usually via an inter-task mapping, which defines how a pair of tasks are related.  ...  space, d n . 4 This stage should guarantee that we project the samples of the source task MDP into a high informational space where a similarity measure can be used to find a relation between the source  ...  for all φ do 2: Calculate the closest activation in C minimizing the Euclidean/similarity distance measure. 3: end for 4: Correspond the triplets with the minimum similarity measure as being inputs and  ... 
doi:10.1007/978-3-642-34799-3_1 fatcat:kuboj5cmc5f3rolapkg3ybmhrm

Contour analysis for interpretable leaf shape category discovery

Jorge Victorino, Francisco Gómez
2019 Plant Methods  
In particular, we overcome the task of discovering shape categories from different plant species for three different biological settings.  ...  Despite the importance of these visual descriptive systems, classifications based on this expert's knowledge may be ambiguous or limited when representing shapes in unknown scenarios, as expected for biological  ...  We thank Mary-Lee Berdugo, Darwin Martínez and Jorge Rudas for his helpful comments.  ... 
doi:10.1186/s13007-019-0497-6 pmid:31624489 pmcid:PMC6781385 fatcat:7h5iaonzhnfmbf4o77qo577iey

Discovering Multi-relational Latent Attributes by Visual Similarity Networks [chapter]

Fatemeh Shokrollahi Yancheshmeh, Joni-Kristian Kämäräinen, Ke Chen
2015 Lecture Notes in Computer Science  
Clustering is one of the popular unsupervised approaches, and the recent literature introduces similarity measures that help to discover visual attributes by clustering.  ...  Instead of clustering, a network (graph) containing multiple connections is a natural way to represent such multi-relational attributes between images.  ...  By using the similarity measure in network construction we noticed that the network can represent multi-relational attributes of discrete type (e.g. class/sub-class) and continuous type (e.g. 3D pose)  ... 
doi:10.1007/978-3-319-16634-6_1 fatcat:dlenin3kbfbbtl5mslrdvkv3fa

Improving Collaborative Filtering based Recommenders using Topic Modelling [article]

Jobin Wilson, Santanu Chaudhury, Brejesh Lall, Prateek Kapadia
2014 arXiv   pre-print
While computing similarity between users, we make use of a combined similarity measure involving rating overlap as well as similarity in the latent topic space.  ...  very sparse rating spaces.  ...  IBCF(LL) represents standard Item Based CF with Log Likelihood as the similarity measure. IBCF(P) represents standard Item Based CF with Pearson Correlation as the similarity measure.  ... 
arXiv:1402.6238v1 fatcat:2byrrbtedzha3fqqb7inotf5de

Locality preserving indexing for document representation

Xiaofei He, Deng Cai, Haifeng Liu, Wei-Ying Ma
2004 Proceedings of the 27th annual international conference on Research and development in information retrieval - SIGIR '04  
In contrast to LSI which discovers the global structure of the document space, LPI discovers the local structure and obtains a compact document representation subspace that best detects the essential semantic  ...  LSI essentially detects the most representative features for document representation rather than the most discriminative features.  ...  Also, LPI seems to be superior to LSI for similarity measure, as shown in our experiments.  ... 
doi:10.1145/1008992.1009012 dblp:conf/sigir/HeCLM04 fatcat:bauei3gfarcbnogir2bso7p2cm

Heterogeneous Relational Reasoning in Knowledge Graphs with Reinforcement Learning [article]

Mandana Saebi, Steven Krieg, Chuxu Zhang, Meng Jiang, Nitesh Chawla
2020 arXiv   pre-print
Our solution uses graph neural network (GNN) for encoding the neighborhood information and utilizes entity types to prune the action space.  ...  However, these solutions still face several challenges, including large action space for the RL agent and accurate representation of entity neighborhood structure.  ...  These methods embed the KG into a vector space and use a similarity measure to identify the entities that are likely to be connected.  ... 
arXiv:2003.06050v1 fatcat:jfw2bdpgrbe7lbxjgtthkg5ggi

Discovering Verb Relations in Corpora: Distributional Versus Non-distributional Approaches [chapter]

Maria Teresa Pazienza, Marco Pennacchiotti, Fabio Massimo Zanzotto
2006 Lecture Notes in Computer Science  
For example, if a Question-Answering system could exploit the direction of the entailment relation win → play, it may expand the question "Who played against Liverpool?"  ...  Verbs represent a way in which ontological relationships between concepts and instances are expressed in natural language utterances.  ...  The idea there was to represent in a feature space the possible filler of the slots X and Y . The feature space represents intensionally a set of contexts of each pattern.  ... 
doi:10.1007/11779568_111 fatcat:pdibstfpxfczroxaey734vlniq

Learning Latent Factors for Community Identification and Summarization

Tiantian He, Lun Hu, Keith C. C. Chan, Pengwei Hu
2018 IEEE Access  
But few of them are able to discover communities and summarize their features simultaneously.  ...  To identify the optimal cluster membership for each vertex, a convergent algorithm for updating the variables in the objective function is derived and used by LFCIS.  ...  [27] , proposed to discover communities in relational data.  ... 
doi:10.1109/access.2018.2843726 fatcat:rb56mrm43vaf7aekard5vbj4te

Reinforcement Learning Transfer via Common Subspaces [chapter]

Haitham Bou Ammar, Matthew E. Taylor
2012 Lecture Notes in Computer Science  
Transfer techniques often use an inter-task mapping, which determines how a pair of tasks are related.  ...  Although reinforcement learning (RL) has been successfully deployed in a variety of tasks, learning speed remains a fundamental problem for applying RL in complex environments.  ...  If successful this step will discover new features in the source task that could better represent relations with the target task than the bases discovered in Section 4.1.1.  ... 
doi:10.1007/978-3-642-28499-1_2 fatcat:rvxgxa5awbfmvncaad3jo5jecu

A Similarity Reinforcement Algorithm for Heterogeneous Web Pages [chapter]

Ning Liu, Jun Yan, Fengshan Bai, Benyu Zhang, Wensi Xi, Weiguo Fan, Zheng Chen, Lei Ji, Chenyong Hu, Wei-Ying Ma
2005 Lecture Notes in Computer Science  
However, most early research works such as Vector Space Model or Latent Semantic Index only used single relationship to measure the similarity of data objects.  ...  In this paper, we first use an Intra-and Inter-Type Relationship Matrix (IITRM) to represent a set of heterogeneous data objects and their inter-relationships.  ...  Recently, researchers have tried to calculate the similarity of two data objects by measuring the similarity of their related data objects.  ... 
doi:10.1007/978-3-540-31849-1_13 fatcat:6xt2fy2qefgxto7w3sllxvfaxq
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