Recent Submissions

  • Intent classification by the use of automatically generated knowledge graphs 

    Arcan, Mihael; Manjunath, Sampritha; Robin, Cécile; Verma, Ghanshyam; Pillai, Devishree; Sarkar, Simon; Dutta, Sourav; Assem, Haytham; McCrae, John P.; Buitelaar, Paul (MDPI, 2023-05-12)
    Intent classification is an essential task for goal-oriented dialogue systems for automatically identifying customers¿ goals. Although intent classification performs well in general settings, domain-specific user goals can ...
  • Towards an integrative approach for making sense distinctions 

    McCrae, John P.; Fransen, Theodorus; Ahmadi, Sina; Buitelaar, Paul; Goswami, Koustava (Frontiers Media, 2022-02-07)
    Word senses are the fundamental unit of description in lexicography, yet it is rarely the case that different dictionaries reach any agreement on the number and definition of senses in a language. With the recent rise in ...
  • Knowledge graph driven approach to represent video streams for spatiotemporal event pattern matching in complex event processing 

    Yadav, Piyush; Salwala, Dhaval; Das, Dibya Prakash; Curry, Edward (World Scientific Publishing, 2020)
    Complex Event Processing (CEP) is an event processing paradigm to perform real-time analytics over streaming data and match high-level event patterns. Presently, CEP is limited to process structured data stream. Video ...
  • Query-driven video event processing for the internet of multimedia things 

    Yadav, Piyush; Salwala, Dhaval; Arruda Pontes, Felipe; Dhingra, Praneet; Curry, Edward (VLDB Endowment, 2021-08)
    Advances in Deep Neural Network (DNN) techniques have revolutionized video analytics and unlocked the potential for querying and mining video event patterns. This paper details GNOSIS, an event processing platform to ...
  • VID-WIN: Fast video event matching with query-aware windowing at the edge for the internet of multimedia things 

    Yadav, Piyush; Salwala, Dhaval; Curry, Edward (Institute of Electrical and Electronics Engineers (IEEE), 2021-04-23)
    Efficient video processing is a critical component in many IoMT applications to detect events of interest. Presently, many window optimization techniques have been proposed in event processing with an underlying assumption ...
  • Toward distributed, global, deep learning using IoT devices 

    Sudharsan, Bharath; Patel, Pankesh; Breslin, John; Ali, Muhammad Intizar; Mitra, Karan; Dustdar, Schahram; Rana, Omer; Jayaraman, Prem Prakash; Ranjan, Rajiv (Institute of Electrical and Electronics Engineers (IEEE), 2021-07-20)
    Deep learning (DL) using large scale, high-quality IoT datasets can be computationally expensive. Utilizing such datasets to produce a problem-solving model within a reasonable time frame requires a scalable distributed ...
  • Synergy between embedding and protein functional association networks for drug label prediction using harmonic function 

    Timilsina, Mohan; Mc Kernan, Declan Patrick; Yang, Haixuan; d’Aquin, Mathieu (ACM and IEEE, 2020-10-16)
    Semi-Supervised Learning (SSL) is an approach to machine learning that makes use of unlabeled data for training with a small amount of labeled data. In the context of molecular biology and pharmacology, one can take advantage ...
  • Biological applications of knowledge graph embedding models 

    Mohamed, Sameh K.; Nounu, Aayah; Nováček, Vít (Oxford University Press (OUP), 2020-02-17)
    Complex biological systems are traditionally modelled as graphs of interconnected biological entities. These graphs, i.e. biological knowledge graphs, are then processed using graph exploratory approaches to perform different ...
  • A decade of Semantic Web research through the lenses of a mixed methods approach 

    Kirrane, Sabrina; Sabou, Marta; Fernandez, Javier D.; Osborne, Francesco; Robin, Cécile; Buitelaar, Paul; Motta, Enrico; Polleres, Axel (IOS Press, 2019-06-20)
    The identification of research topics and trends is an important scientometric activity, as it can help guide the direction of future research. In the Semantic Web area, initially topic and trend detection was primarily ...
  • Discovering protein drug targets using knowledge graph embeddings 

    Mohamed, Sameh K.; Nováček, Vít; Nounu, Aayah (Oxford University Press, 2019-08-01)
    Motivation Computational approaches for predicting drug-target interactions (DTIs) can provide valuable insights into the drug mechanism of action. DTI predictions can help to quickly identify new promising (on-target) ...
  • One size does not fit all: querying web polystores 

    Khan, Yasar; Zimmermann, Antoine; Jha, Alokkumar; Gadepally, Vijay; d'Aquin, Mathieu; Sahay, Ratnesh (IEEE, 2019-01-17)
    Data retrieval systems are facing a paradigm shift due to the proliferation of specialized data storage engines (SQL, NoSQL, Column Stores, MapReduce, Data Stream, and Graph) supported by varied data models (CSV, JSON, ...
  • LargeRDFBench: A billion triples benchmark for SPARQL endpoint federation 

    Saleem, Muhammad; Hasnain, Ali; Ngonga Ngomo, Axel-Cyrille (Elsevier, 2018-01-12)
    Gathering information from the distributed Web of Data is commonly carried out by using SPARQL query federation approaches. However, the fitness of current SPARQL query federation approaches for real applications is difficult ...
  • A random walk model for entity relatedness 

    Torres-Tramón, Pablo; Hayes, Conor (Springer Verlag, 2018-10-31)
    Semantic relatedness is a critical measure for a wide variety of applications nowadays. Numerous models, including path-based, have been proposed for this task with great success in many applications during the last few ...
  • MixedEmotions: An open-source toolbox for multi-modal emotion analysis 

    Buitelaar, Paul; Wood, Ian D.; Negi, Sapna; Arcan, Mihael; McCrae, John P.; Abele, Andrejs; Robin, Cécile; Andryushechkin, Vladimir; Ziad, Housam; Sagha, Hesam; Schmitt, Maximilian; Schuller, Björn W.; Sánchez-Rada, J. Fernando; Iglesias, Carlos A.; Navarro, Carlos; Giefer, Andreas; Heise, Nicolaus; Masucci, Vincenzo; Danza, Francesco A.; Caterino, Ciro; Smrž, Pavel; Hradiš, Michal; Povolný, Filip; Klimeš, Marek; Matějka, Pavel; Tummarello, Giovanni (IEEE, 2018-01-25)
    Recently, there is an increasing tendency to embed the functionality of recognizing emotions from the user generated contents, to infer richer profile about the users or contents, that can be used for various automated ...
  • The colloquial WordNet: Extending Princeton WordNet with neologisms 

    McCrae, John P.; Wood, Ian D.; HIcks, Amanda (Springer International Publishing, 2017-05-27)
    Princeton WordNet is one of the most important resources for natural language processing, but has not been updated for over ten years and is not suitable for analyzing the fast moving language as used on social media. We ...
  • Privacy, security and policies: A review of problems and solutions with semantic web technologies 

    Kirrane, Sabrina; Villata, Serena; d’Aquin, Mathieu (IOS Press, 2018)
    Semantic Web technologies aim to simplify the distribution, sharing and exploitation of information and knowledge, across multiple distributed actors on the Web. As with all technologies that manipulate information, there ...
  • Facilitating scientometrics in learning analytics and educational data mining - The LAK dataset 

    Dietze, Stefan; Taibi, Davide; d’Aquin, Mathieu (IOS Press, 2016-11-06)
    The Learning Analytics and Knowledge (LAK) Dataset represents an unprecedented corpus which exposes a near complete collection of bibliographic resources for a specific research discipline, namely the connected areas of ...
  • Abstract A27: A linked data approach to discover HPV oncoprotiens and RB1 induced mutation associations for the retinoblastoma research 

    Jha, Alokkumar; Khan, Yasar; Rebholz-Schumann, Dietrich; Sahay, Ratnesh (American Association for Cancer Research, 2017-01)
    Background: LOSS or GAIN in tumor suppressor gene RB1 play a significant role as in case of loss low penetrance where only 39% of eye at risk develops in retinoblastoma. This research covers the multiple mutation types and ...
  • A linked data visualiser for finite element biosimulations 

    Mehdi, Muntazir; Khan, Yasar; Jares, Joao; Freitas, Andre; Jha, Alok Kumar; Sakellarios, Antonis; Sahay, Ratnesh (World Scientific Publishing, 2016)
    Biosimulation models are used to understand the multiple or different causative factors that cause impairment in human organs. Finite Element Method (FEM) provide a mathematical framework to simulate dynamic biological ...
  • Towards precision medicine: discovering novel gynecological cancer biomarkers and pathways using linked data 

    Jha, Alokkumar; Khan, Yasar; Mehdi, Muntazir; Karim, Md Rezaul; Mehmood, Qaiser; Zappa, Achille; Rebholz-Schuhmann, Dietrich; Sahay, Ratnesh (BioMed Central, 2017-09-19)
    Next Generation Sequencing (NGS) is playing a key role in therapeutic decision making for the cancer prognosis and treatment. The NGS technologies are producing a massive amount of sequencing datasets. Often, these datasets ...

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