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1. Recommender-as-a-Service with Chatbot Guided Domain-science Knowledge Discovery in a Science Gateway

   https://doi.org/10.1002/cpe.6080  

   Vekaria, K.; Calyam, P.; Sivarathri, S.; Wang, S.; Zhang, Y.; Pandey, A.; Chen, C.; Xu, D.; Joshi, T.; Nair, S.S. (January 2020, Wiley Concurrency and Computation: Practice and Experience)

   Scientists in disciplines such as neuroscience and bioinformatics are increasingly relying on science gateways for experimentation on voluminous data, as well as analysis and visualization in multiple perspectives. Though current science gateways provide easy access to computing resources, datasets and tools specific to the disciplines, scientists often use slow and tedious manual efforts to perform knowledge discovery to accomplish their research/education tasks. Recommender systems can provide expert guidance and can help them to navigate and discover relevant publications, tools, data sets, or even automate cloud resource configurations suitable for a given scientific task. To realize the potential of integration of recommenders in science gateways in order to spur research productivity,we present a novel “OnTimeRecommend" recommender system. The OnTimeRecommend comprises of several integrated recommender modules implemented as microservices that can be augmented to a science gateway in the form of a recommender-as-a-service. The guidance for use of the recommender modules in a science gateway is aided by a chatbot plug-in viz., Vidura Advisor. To validate our OnTimeRecommend, we integrate and show benefits for both novice and expert users in domain-specific knowledge discovery within two exemplar science gateways, one in neuroscience (CyNeuro) and the other in bioinformatics (KBCommons). 

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2. Chatbot Guided Domain-science Knowledge Discovery in a Science Gateway Application

   S. Sivarathri, S.; Calyam, P.; Zhang, Y.; Pandey, A.; Chen, C.; Xu, D.; Joshi, T.; Nair, S.S. (January 2019, 14th Gateway Computing Environments Conference)

   Neuroscientists are increasingly relying on high performance/throughput computing resources for experimentation on voluminous data, analysis and visualization at multiple neural levels. Though current science gateways provide access to computing resources, datasets and tools specific to the disciplines, neuroscientists require guided knowledge discovery at various levels to accomplish their research/education tasks. The guidance can help them to navigate them through relevant publications, tools, topic associations and cloud platform options as they accomplish important research and education activities. To address this need and to spur research productivity and rapid learning platform development, we present “OnTimeRecommend”, a novel recommender system that comprises of several integrated recommender modules through RESTful web services. We detail a neuroscience use case in a CyNeuro science gateway, and show how the OnTimeRecommend design can enable novice/expert user interfaces, as well as template-driven control of heterogeneous cloud resources. 

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3. Supervised Neural Topic Modeling with Label Alignment

   https://doi.org/10.1162/tacl_a_00738  

   Chen, Ruihao; Chen, Hegang; Lu, Yuyin; Rao, Yanghui; Zhu, Chunjiang (January 2025, Transactions of the Association for Computational Linguistics)

   Abstract Neural topic modeling is a scalable automated technique for text data mining. In various downstream tasks of topic modeling, it is preferred that the discovered topics well align with labels. However, due to the lack of guidance from labels, unsupervised neural topic models are less powerful in this situation. Existing supervised neural topic models often adopt a label-free prior to generate the latent document-topic distributions and use them to predict the labels and thus achieve label-topic alignment indirectly. Such a mechanism faces the following issues: 1) The label-free prior leads to topics blending the latent patterns of multiple labels; and 2) One is unable to intuitively identify the explicit relationships between labels and the discovered topics. To tackle these problems, we develop a novel supervised neural topic model which utilizes a chain-structured graphical model with a label-conditioned prior. Soft indicators are introduced to explicitly construct the label-topic relationships. To obtain well-organized label-topic relationships, we formalize an entropy-regularized optimal transport problem on the embedding space and model them as the transport plan. Moreover, our proposed method can be flexibly integrated with most existing unsupervised neural topic models. Experimental results on multiple datasets demonstrate that our model can greatly enhance the alignment between labels and topics while maintaining good topic quality. 

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4. Scalable Probabilistic Causal Structure Discovery

   https://doi.org/10.24963/ijcai.2018/709  

   Sridhar, Dhanya; Pujara, Jay; Getoor, Lise (July 2018, International Joint Conference on Artificial Intelligence (IJCAI))

   Complex causal networks underlie many real-world problems, from the regulatory interactions between genes to the environmental patterns used to understand climate change. Computational methods seek to infer these causal networks using observational data and domain knowledge. In this paper, we identify three key requirements for inferring the structure of causal networks for scientific discovery: (1) robustness to noise in observed measurements; (2) scalability to handle hundreds of variables; and (3) flexibility to encode domain knowledge and other structural constraints. We first formalize the problem of joint probabilistic causal structure discovery.  We develop an approach using probabilistic soft logic (PSL) that exploits multiple statistical tests, supports efficient optimization over hundreds of variables, and can easily incorporate structural constraints, including imperfect domain knowledge. We compare our method against multiple well-studied approaches on biological and synthetic datasets, showing improvements of up to 20% in F1-score over the best performing baseline in realistic settings. 

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5. Effective Seed-Guided Topic Discovery by Integrating Multiple Types of Contexts

   https://doi.org/10.1145/3539597.3570475  

   Zhang, Yu; Zhang, Yunyi; Michalski, Martin; Jiang, Yucheng; Meng, Yu; Han, Jiawei (February 2023, ACM)

   Proceedings of the Sixteenth (Ed.)

   Instead of mining coherent topics from a given text corpus in a completely unsupervised manner, seed-guided topic discovery methods leverage user-provided seed words to extract distinctive and coherent topics so that the mined topics can better cater to the user’s interest. To model the semantic correlation between words and seeds for discovering topic-indicative terms, existing seedguided approaches utilize different types of context signals, such as document-level word co-occurrences, sliding window-based local contexts, and generic linguistic knowledge brought by pre-trained language models. In this work, we analyze and show empirically that each type of context information has its value and limitation in modeling word semantics under seed guidance, but combining three types of contexts (i.e., word embeddings learned from local contexts, pre-trained language model representations obtained from general-domain training, and topic-indicative sentences retrieved based on seed information) allows them to complement each other for discovering quality topics. We propose an iterative framework, SeedTopicMine, which jointly learns from the three types of contexts and gradually fuses their context signals via an ensemble ranking process. Under various sets of seeds and on multiple datasets, SeedTopicMine consistently yields more coherent and accurate topics than existing seed-guided topic discovery approaches. 

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