More Like this

1. ECIR 23 Tutorial: Neuro-Symbolic Approaches for Information Retrieval

   https://doi.org/10.1007/978-3-031-28241-6_33  

   Dietz, Laura; Bast, Hannah; Chatterjee, Shubham; Dalton, Jeff; Meij, Edgar Meij; de Vries, Arjen (April 2023, Springer)

   This tutorial will provide an overview of recent advances on neuro-symbolic approaches for information retrieval. A decade ago, knowledge graphs and semantic annotations technology led to active re- search on how to best leverage symbolic knowledge. At the same time, neural methods have demonstrated to be versatile and highly effective. From a neural network perspective, the same representation approach can service document ranking or knowledge graph reasoning. End-to-end training allows to optimize complex methods for downstream tasks. We are at the point where both the symbolic and the neural research advances are coalescing into neuro-symbolic approaches. The underlying research questions are how to best combine symbolic and neural ap- proaches, what kind of symbolic/neural approaches are most suitable for which use case, and how to best integrate both ideas to advance the state of the art in information retrieval. 

   more » « less
2. Dynamic Neuro-Symbolic Knowledge Graph Construction for Zero-shot Commonsense Question Answering

   Bosselut, Antoine; Le Bras, Ronan; Choi, Yejin (November 2020, The Thirty-Fifth AAAI Conference on Artificial Intelligence)

   Understanding narratives requires reasoning about implicit world knowledge related to the causes, effects, and states of situations described in text. At the core of this challenge is how to access contextually relevant knowledge on demand and reason over it. In this paper, we present initial studies toward zero-shot commonsense question answering by formulating the task as inference over dynamically generated commonsense knowledge graphs. In contrast to previous studies for knowledge integration that rely on retrieval of existing knowledge from static knowledge graphs, our study requires commonsense knowledge integration where contextually relevant knowledge is often not present in existing knowledge bases. Therefore, we present a novel approach that generates contextually-relevant symbolic knowledge structures on demand using generative neural commonsense knowledge models. Empirical results on two datasets demonstrate the efficacy of our neuro-symbolic approach for dynamically constructing knowledge graphs for reasoning. Our approach achieves significant performance boosts over pretrained language models and vanilla knowledge models, all while providing interpretable reasoning paths for its predictions. 

   more » « less
3. Prompt2DeModel: Declarative Neuro-Symbolic Modeling with Natural Language

   Faghihi, Hossein Rajaby; Nafar, Aliakbar; Uszok, Andrzej; Karimian, Hamid; Kordjamshidi, Parisa (September 2024, Springer-Verlag)

   This paper presents a conversational pipeline for crafting domain knowledge for complex neuro-symbolic models through natural language prompts. It leverages large language models to generate declarative programs in the DomiKnowS framework. The programs in this framework express concepts and their relationships as a graph in addition to logical constraints between them. The graph, later, can be connected to trainable neural models according to those specifications. Our proposed pipeline utilizes techniques like dynamic in-context demonstration retrieval, model refinement based on feedback from a symbolic parser, visualization, and user interaction to generate the tasks’ structure and formal knowledge representation. This approach empowers domain experts, even those not well-versed in ML/AI, to formally declare their knowledge to be incorporated in customized neural models in the DomiKnowS framework. 

   more » « less
4. A Hybrid Retrieval-Generation Neural Conversation Model

   https://doi.org/10.1145/3357384.3357881  

   Yang, Liu; Hu, Junjie; Qiu, Minghui; Qu, Chen; Gao, Jianfeng; Croft, W. Bruce; Liu, Xiaodong; Shen, Yelong; Liu, Jingjing (November 2019, Proceedings of the 28th ACM International Conference on Information and Knowledge Management - CIKM '19)

   Intelligent personal assistant systems, with either text-based or voice-based conversational interfaces, are becoming increasingly popular. Most previous research has used either retrieval-based or generation-based methods. Retrieval-based methods have the advantage of returning fluent and informative responses with great diversity. The retrieved responses are easier to control and explain. However, the response retrieval performance is limited by the size of the response repository. On the other hand, although generation-based methods can return highly coherent responses given conversation context, they are likely to return universal or general responses with insufficient ground knowledge information. In this paper, we build a hybrid neural conversation model with the capability of both response retrieval and generation, in order to combine the merits of these two types of methods. Experimental results on Twitter and Foursquare data show that the proposed model can outperform both retrieval-based methods and generation-based methods (including a recently proposed knowledge-grounded neural conversation model) under both automatic evaluation metrics and human evaluation. Our models and research findings provide new insights on how to integrate text retrieval and text generation models for building conversation systems. 

   more » « less
5. NeuPSL: Neural Probabilistic Soft Logic

   Pryor, Connor; Dickens, Charles Andrew; Augustine, Eriq; Albalak; Alon; Wang; William Yang; Getoor, Lise (June 2022, ArXivorg)

   We present Neural Probabilistic Soft Logic (NeuPSL), a novel neuro-symbolic (NeSy) framework that unites state-of-the-art symbolic reasoning with the low-level perception of deep neural networks. To explicitly model the boundary between neural and symbolic representations, we introduce NeSy Energy-Based Models, a general family of energy-based models that combine neural and symbolic reasoning. Using this framework, we show how to seamlessly integrate neural and symbolic parameter learning and inference. We perform an extensive empirical evaluation and show that NeuPSL outperforms existing methods on joint inference and has significantly lower variance in almost all settings. 

   more » « less