Combining Inductive and Deductive Reasoning for Query Answering over Incomplete Knowledge Graphs

• URL: http://arxiv.org/abs/2106.14052v2
• Date: Thu, 31 Aug 2023 15:10:45 GMT
• Title: Combining Inductive and Deductive Reasoning for Query Answering over Incomplete Knowledge Graphs
• Authors: Medina Andresel, Trung-Kien Tran, Csaba Domokos, Pasquale Minervini, Daria Stepanova
• Abstract summary: Current methods for embedding-based query answering over incomplete Knowledge Graphs (KGs) only focus on inductive reasoning. We propose various integration strategies into prominent representatives of embedding models. The achieved improvements in the setting that requires both inductive and deductive reasoning are from 20% to 55% in HITS@3.
• Score: 12.852658691077643
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Current methods for embedding-based query answering over incomplete Knowledge Graphs (KGs) only focus on inductive reasoning, i.e., predicting answers by learning patterns from the data, and lack the complementary ability to do deductive reasoning, which requires the application of domain knowledge to infer further information. To address this shortcoming, we investigate the problem of incorporating ontologies into embedding-based query answering models by defining the task of embedding-based ontology-mediated query answering. We propose various integration strategies into prominent representatives of embedding models that involve (1) different ontology-driven data augmentation techniques and (2) adaptation of the loss function to enforce the ontology axioms. We design novel benchmarks for the considered task based on the LUBM and the NELL KGs and evaluate our methods on them. The achieved improvements in the setting that requires both inductive and deductive reasoning are from 20% to 55% in HITS@3.

Related papers

• Ontology Completion with Natural Language Inference and Concept Embeddings: An Analysis [26.918368764004796]
  We consider the problem of finding plausible knowledge that is missing from a given ontology, as a generalisation of the well-studied taxonomy expansion task. One line of work treats this task as a Natural Language Inference (NLI) problem, relying on the knowledge captured by language models to identify the missing knowledge. Another line of work uses concept embeddings to identify what different concepts have in common, taking inspiration from cognitive models for category based induction.
  arXiv  Detail & Related papers  (2024-03-25T21:46:35Z)
• Extending Transductive Knowledge Graph Embedding Models for Inductive Logical Relational Inference [0.5439020425819]
  This work bridges the gap between traditional transductive knowledge graph embedding approaches and more recent inductive relation prediction models. We introduce a generalized form of harmonic extension which leverages representations learned through transductive embedding methods to infer representations of new entities introduced at inference time as in the inductive setting. In experiments on a number of large-scale knowledge graph embedding benchmarks, we find that this approach for extending the functionality of transductive knowledge graph embedding models is competitive with--and in some scenarios outperforms--several state-of-the-art models derived explicitly for such inductive tasks.
  arXiv  Detail & Related papers  (2023-09-07T15:24:18Z)
• Explaining Explainability: Towards Deeper Actionable Insights into Deep Learning through Second-order Explainability [70.60433013657693]
  Second-order explainable AI (SOXAI) was recently proposed to extend explainable AI (XAI) from the instance level to the dataset level. We demonstrate for the first time, via example classification and segmentation cases, that eliminating irrelevant concepts from the training set based on actionable insights from SOXAI can enhance a model's performance.
  arXiv  Detail & Related papers  (2023-06-14T23:24:01Z)
• A Study of Situational Reasoning for Traffic Understanding [63.45021731775964]
  We devise three novel text-based tasks for situational reasoning in the traffic domain. We adopt four knowledge-enhanced methods that have shown generalization capability across language reasoning tasks in prior work. We provide in-depth analyses of model performance on data partitions and examine model predictions categorically.
  arXiv  Detail & Related papers  (2023-06-05T01:01:12Z)
• Rethinking Complex Queries on Knowledge Graphs with Neural Link Predictors [58.340159346749964]
  We propose a new neural-symbolic method to support end-to-end learning using complex queries with provable reasoning capability. We develop a new dataset containing ten new types of queries with features that have never been considered. Our method outperforms previous methods significantly in the new dataset and also surpasses previous methods in the existing dataset at the same time.
  arXiv  Detail & Related papers  (2023-04-14T11:35:35Z)
• Deep Manifold Learning for Reading Comprehension and Logical Reasoning Tasks with Polytuplet Loss [0.0]
  The current trend in developing machine learning models for reading comprehension and logical reasoning tasks is focused on improving the models' abilities to understand and utilize logical rules. This work focuses on providing a novel loss function and accompanying model architecture that has more interpretable components than some other models. Our strategy involves emphasizing relative accuracy over absolute accuracy and can theoretically produce the correct answer with incomplete knowledge.
  arXiv  Detail & Related papers  (2023-04-03T14:48:34Z)
• Neural Causal Models for Counterfactual Identification and Estimation [62.30444687707919]
  We study the evaluation of counterfactual statements through neural models. First, we show that neural causal models (NCMs) are expressive enough. Second, we develop an algorithm for simultaneously identifying and estimating counterfactual distributions.
  arXiv  Detail & Related papers  (2022-09-30T18:29:09Z)
• MERIt: Meta-Path Guided Contrastive Learning for Logical Reasoning [63.50909998372667]
  We propose MERIt, a MEta-path guided contrastive learning method for logical ReasonIng of text. Two novel strategies serve as indispensable components of our method.
  arXiv  Detail & Related papers  (2022-03-01T11:13:00Z)
• Explaining, Evaluating and Enhancing Neural Networks' Learned Representations [2.1485350418225244]
  We show how explainability can be an aid, rather than an obstacle, towards better and more efficient representations. We employ such attributions to define two novel scores for evaluating the informativeness and the disentanglement of latent embeddings. We show that adopting our proposed scores as constraints during the training of a representation learning task improves the downstream performance of the model.
  arXiv  Detail & Related papers  (2022-02-18T19:00:01Z)
• Decision-Theoretic Question Generation for Situated Reference Resolution: An Empirical Study and Computational Model [11.543386846947554]
  We analyzed dialogue data from an interactive study in which participants controlled a virtual robot tasked with organizing a set of tools while engaging in dialogue with a live, remote experimenter. We discovered a number of novel results, including the distribution of question types used to resolve ambiguity and the influence of dialogue-level factors on the reference resolution process.
  arXiv  Detail & Related papers  (2021-10-12T19:23:25Z)
• AR-LSAT: Investigating Analytical Reasoning of Text [57.1542673852013]
  We study the challenge of analytical reasoning of text and introduce a new dataset consisting of questions from the Law School Admission Test from 1991 to 2016. We analyze what knowledge understanding and reasoning abilities are required to do well on this task.
  arXiv  Detail & Related papers  (2021-04-14T02:53:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.