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Harnessing relationships for domain-specific subgraph extraction: A recommendation use case

Sarasi Lalithsena, Pavan Kapanipathi, Amit Sheth
2016 2016 IEEE International Conference on Big Data (Big Data)  
We demonstrate the applicability of this approach for a recommendation use case on two domains, i.e. movie and book.  ...  For example, a movie or a book recommendation system would require a subgraph that comprises knowledge relevant to the specific domain.  ...  Recommendation is a well-suited use case for domain-specific subgraph extraction as it considers the item relatedness among in-domain entities.  ... 
doi:10.1109/bigdata.2016.7840663 dblp:conf/bigdataconf/LalithsenaKS16 fatcat:d5zpdm4hj5anllx3hin5yi3xkq

Domain-specific hierarchical subgraph extraction: A recommendation use case

Sarasi Lalithsena, Sujan Perera, Pavan Kapanipathi, Amit Sheth
2017 2017 IEEE International Conference on Big Data (Big Data)  
We show the effectiveness of our approach with a recommendation use case for movie and book domains.  ...  Furthermore, the presented approach outperforms the recommendation results obtained with a stateof-the-art domain-specific subgraph extraction technique which uses supervised learning.  ...  Furthermore, to show the effectiveness on applications using KGs, we evaluated the quality of the domain-specific subgraph extracted with a recommendation use case.  ... 
doi:10.1109/bigdata.2017.8257982 dblp:conf/bigdataconf/LalithsenaPKS17 fatcat:ctmcha5bs5dolkitvqpwkesoiu

Extraction and Analysis of Fictional Character Networks

Vincent Labatut, Xavier Bost
2019 ACM Computing Surveys  
A character network is a graph extracted from a narrative, in which vertices represent characters and edges correspond to interactions between them.  ...  We then review the descriptive tools used to characterize character networks, with a focus on the way they are interpreted in this context.  ...  Acknowledgments The authors would like to thank the anonymous reviewers for their work and feedback, which helped significantly improve this article. Part of this work was funded by Agorantic FR 3621.  ... 
doi:10.1145/3344548 fatcat:zujg55eixfct7blvj6lxwo4usq

Improving Natural Language Inference Using External Knowledge in the Science Questions Domain [article]

Xiaoyan Wang, Pavan Kapanipathi, Ryan Musa, Mo Yu, Kartik Talamadupula, Ibrahim Abdelaziz, Maria Chang, Achille Fokoue, Bassem Makni, Nicholas Mattei, Michael Witbrock
2018 arXiv   pre-print
To address this, we present a combination of techniques that harness knowledge graphs to improve performance on the NLI problem in the science questions domain.  ...  While there are many open knowledge bases that contain various types of reasoning information, their use for NLI has not been well explored.  ...  This work harnesses WordNet as the external knowledge for NLI. WordNet, however, is a lexical database restricted to a small number of linguistic relationships among terms. Furthermore, Chen et al.  ... 
arXiv:1809.05724v2 fatcat:7dolmjp3rvgxljilc6qanqsgvy

Knowledge Graphs and Knowledge Networks: The Story in Brief [article]

Amit Sheth, Swati Padhee, Amelie Gyrard
2020 arXiv   pre-print
However, for dynamic real-world applications such as social networks, recommender systems, computational biology, relational knowledge representation has emerged as a challenging research problem where  ...  Knowledge Graphs (KGs) represent real-world noisy raw information in a structured form, capturing relationships between entities.  ...  Recent studies in domain-specific subgraph extraction have significantly contributed to improving the efficiency and quality of information extraction and complex task-specific algorithms by capturing  ... 
arXiv:2003.03623v1 fatcat:zle7g626mzeqrkmobgrr6xtfae

Improving Natural Language Inference Using External Knowledge in the Science Questions Domain

Xiaoyan Wang, Pavan Kapanipathi, Ryan Musa, Mo Yu, Kartik Talamadupula, Ibrahim Abdelaziz, Maria Chang, Achille Fokoue, Bassem Makni, Nicholas Mattei, Michael Witbrock
2019 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
To address this, we present a combination of techniques that harness external knowledge to improve performance on the NLI problem in the science questions domain.  ...  While there are many open knowledge bases that contain various types of reasoning information, their use for NLI has not been well explored.  ...  a subgraph from ConceptNet for a given premise p and a hypothesis h.  ... 
doi:10.1609/aaai.v33i01.33017208 fatcat:chb6qvwgrzav5f5x2olzavo5bi

Harnessing the Power of Ego Network Layers for Link Prediction in Online Social Networks [article]

Mustafa Toprak, Chiara Boldrini, Andrea Passarella, Marco Conti
2021 arXiv   pre-print
Finally, we show that social-awareness can be used in place of using a classifier (which may be costly or impractical) for targeting a specific category of users.  ...  Being able to recommend links between users in online social networks is important for users to connect with like-minded individuals as well as for the platforms themselves and third parties leveraging  ...  This implies that some nodes are domain-specific (in our case, gaming-related), while others are generic. Domain-specific nodes are homogeneous, since they share a common interest.  ... 
arXiv:2109.09190v1 fatcat:m2ock43efzd7vppwjkmqhkvz6y

Research Directions for Big Data Graph Analytics

John A. Miller, Lakshmish Ramaswamy, Krys J. Kochut, Arash Fard
2015 2015 IEEE International Congress on Big Data  
In the era of big data, interest in analysis and extraction of information from large data graphs is increasing rapidly.  ...  Still, the need to provide answers even for very large graphs is driving the research. Progress, trends and directions for future research are presented.  ...  In other situations, the relationships between data items is what is of most importance. In such cases, the data may be captured in a graph.  ... 
doi:10.1109/bigdatacongress.2015.132 dblp:conf/bigdata/MillerRKF15 fatcat:ws7anjfh3nh3lh7aw44cnwo4hm

Scalable Combinatorial Tools for Health Disparities Research

Michael Langston, Robert Levine, Barbara Kilbourne, Gary Rogers, Anne Kershenbaum, Suzanne Baktash, Steven Coughlin, Arnold Saxton, Vincent Agboto, Darryl Hood, Maureen Litchveld, Tonny Oyana (+2 others)
2014 International Journal of Environmental Research and Public Health  
While standard techniques can scrutinize at most a handful of parameters for obvious dependencies, combinatorial methods are able to extract latent signal from a sea of even only modest correlations spread  ...  The public health exposome is used as a contemporary focus for addressing the complex nature of this subject. on the exposome paradigm [2], and is aimed at describing the effects of multiple and cumulative  ...  We thank the anonymous reviewers for their thoughtful critiques and helpful comments.  ... 
doi:10.3390/ijerph111010419 pmid:25310540 pmcid:PMC4210988 fatcat:smmhbbw44rh45pxn73i46mmeie

Graph Learning for Cognitive Digital Twins in Manufacturing Systems [article]

Trier Mortlock, Deepan Muthirayan, Shih-Yuan Yu, Pramod P. Khargonekar, Mohammad A. Al Faruque
2021 arXiv   pre-print
Digital twins incorporate a physical twin, a digital twin, and the connection between the two.  ...  Benefits of using digital twins, especially in manufacturing, are abundant as they can increase efficiency across an entire manufacturing life-cycle.  ...  Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect views of our funding agencies.  ... 
arXiv:2109.08632v1 fatcat:63kqrdg2hzakpj3jplptuayyjy

Query-driven on-the-fly knowledge base construction

Dat Ba Nguyen, Abdalghani Abujabal, Nam Khanh Tran, Martin Theobald, Gerhard Weikum
2017 Proceedings of the VLDB Endowment  
QKBfly is based on a semantic-graph representation of sentences, by which we perform three key IE tasks, namely named-entity disambiguation, co-reference resolution and relation extraction, in a light-weight  ...  To overcome both of these limitations, we propose a novel approach to build on-the-fly knowledge bases in a query-driven manner.  ...  As an extrinsic use-case, we harness QKBfly for KB-QA.  ... 
doi:10.14778/3151113.3151119 fatcat:tll2ue5gmraxbfbrc3r5b7sdri

Semantically-Guided Clustering of Text Documents via Frequent Subgraphs Discovery [chapter]

Rafal A. Angryk, M. Shahriar Hossain, Brandon Norick
2011 Lecture Notes in Computer Science  
In this paper we introduce and analyze two improvements to GDClust [1], a system for document clustering based on the co-occurrence of frequent subgraphs.  ...  Text documents are transformed to hierarchical document-graphs, and an efficient graph-mining technique is used to find frequent subgraphs.  ...  for organizing the numerous documents available to us on a daily basis.  ... 
doi:10.1007/978-3-642-21916-0_44 fatcat:2ynpfgcihng5ljccbkl5pcnoqu

Emerging, Collective Intelligence for Personal, Organisational and Social Use [chapter]

Sotiris Diplaris, Andreas Sonnenbichler, Tomasz Kaczanowski, Phivos Mylonas, Ansgar Scherp, Maciej Janik, Symeon Papadopoulos, Michael Ovelgoenne, Yiannis Kompatsiaris
2011 Studies in Computational Intelligence  
In [19] tags and visual information together with geo-location are used for objects (e.g. monuments) and events extraction.  ...  The exploitation of the emerging Collective Intelligence results is showcased in two distinct case studies: an Emergency Response and a Consumers Social Group 3 case study.  ...  number of within-subgraph connections for subgraph S, and outd(S) stands for the number of connections from subgraph nodes to the rest of the graph.  ... 
doi:10.1007/978-3-642-20344-2_20 fatcat:crgix2grbvfupaht5mp3cjriii

Mining Biomedical Ontologies and Data Using RDF Hypergraphs

Haishan Liu, Dejing Dou, Ruoming Jin, Paea Lependu, Nigam Shah
2013 2013 12th International Conference on Machine Learning and Applications  
By representing both ontologies and data using RDF hypergraphs, and subsequently transforming the hypergraphs to corresponding bipartite forms, we provide a generalized data mining method that scales beyond  ...  in a systematic and scalable way.  ...  Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the supporting institutions.  ... 
doi:10.1109/icmla.2013.31 dblp:conf/icmla/LiuDJLS13 fatcat:ftgbjka5lnb7vkc53utif4pkje

HAR: Hub, Authority and Relevance Scores in Multi-Relational Data for Query Search [chapter]

Xutao Li, Michael K. Ng, Yunming Ye
2012 Proceedings of the 2012 SIAM International Conference on Data Mining  
can incorporate input query vectors to handle query-specific search; (ii) show existence and uniqueness of such limiting probabilities so that they can be used for query search effectively; and (iii)  ...  In this paper, we propose a framework HAR to study the hub and authority scores of objects, and the relevance scores of relations in multi-relational data for query search.  ...  Rendle el al. proposed a tensor factorization model to exploit the ternary relationships in tagging data and personalize the tag recommender [19] . Kolda et al.  ... 
doi:10.1137/1.9781611972825.13 dblp:conf/sdm/LiNY12 fatcat:kadu6nbcbngcfgvdzvhulrk7l4
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