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Tiansi Dong

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3 papers

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AAAI Conference 2025 Conference Paper

Neural Reasoning for Sure Through Constructing Explainable Models

Tiansi Dong
Mateja Jamnik
Pietro Liò

Neural networks remain black-box systems, unsure about their outputs, and their performance may drop unpredictably in real applications. An open question is how to qualitatively extend neural networks, so that they are sure about their reasoning results, or reasoning-for-sure. Here, we introduce set-theoretic relations explicitly and seamlessly into neural networks by extending vector embedding into sphere embedding, so that part-whole relations can explicitly encode set-theoretic relations through sphere boundaries in the vector space. A reasoning-for-sure neural network successfully constructs, within a constant number M of epochs, a sphere configuration as its semantic model for any consistent set-theoretic relation. We implement Hyperbolic Sphere Neural Network (HSphNN), the first reasoning-for-sure neural network for all types of Aristotelian syllogistic reasoning. Its construction process is realised as a sequence of neighbourhood transitions from the current towards the target configuration. We prove M=1 for HSphNN. In experiments, HSphNN achieves the symbolic level rigour of syllogistic reasoning and successfully checks both decisions and explanations of ChatGPT (gpt-3.5-turbo and gpt-4o) without errors. Through prompts, HSphNN improves the performance of gpt-3.5-turbo from 46.875% to 58.98%, and of gpt-4o from 82.42% to 84.76%. We show ways to extend HSphNN for various kinds of logical and Bayesian reasoning, and to integrate it with traditional neural networks seamlessly.

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AAAI Conference 2019 Conference Paper

Triple Classification Using Regions and Fine-Grained Entity Typing

Tiansi Dong
Zhigang Wang
Juanzi Li
Christian Bauckhage
Armin B. Cremers

A Triple in knowledge-graph takes a form that consists of head, relation, tail. Triple Classification is used to determine the truth value of an unknown Triple. This is a hard task for 1-to-N relations using the vector-based embedding approach. We propose a new region-based embedding approach using fine-grained type chains. A novel geometric process is presented to extend the vectors of pre-trained entities into n-balls (n-dimensional balls) under the condition that head balls shall contain their tail balls. Our algorithm achieves zero energy cost, therefore, serves as a case study of perfectly imposing tree structures into vector space. An unknown Triple (h, r, x) will be predicted as true, when x’s n-ball is located in the r-subspace of h’s n-ball, following the same construction of known tails of h. The experiments are based on large datasets derived from the benchmark datasets WN11, FB13, and WN18. Our results show that the performance of the new method is related to the length of the type chain and the quality of pre-trained entityembeddings, and that performances of long chains with welltrained entity-embeddings outperform other methods in the literature. Source codes and datasets are located at https: //github. com/GnodIsNait/mushroom.

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IJCAI Conference 2011 Conference Paper

A Natural Language Question Answering System as a Participant in Human Q&A Portals

Tiansi Dong
Ulrich Furbach
Ingo Gl
ouml; ckner
Bj
ouml; rn Pelzer

LogAnswer is a question answering (QA) system for the German language, aimed at providing concise and correct answers to arbitrary questions. For this purpose LogAnswer is designed as an embedded artificial intelligence system which integrates methods from several fields of AI, namely natural language processing, machine learning, knowledge representation and automated theorem proving. We intend to employ LogAnswer as a virtual user within Internet-based QA forums, where it must be able to identify the questions that it cannot answer correctly, a task that normally receives little attention in QA research compared to the actual answer derivation. The paper presents a machine learning solution to the wrong answer avoidance (WAA) problem, applying a meta classifier to the output of simple term-based classifiers and a rich set of other WAA features. Experiments with a large set of real-world questions from a QA forum show that the proposed method significantly improves the WAA characteristics of our system.

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