6 research outputs found
Neural-Symbolic Relational Reasoning on Graph Models: Effective Link Inference and Computation from Knowledge Bases
The recent developments and growing interest in neural-symbolic models has
shown that hybrid approaches can offer richer models for Artificial
Intelligence. The integration of effective relational learning and reasoning
methods is one of the key challenges in this direction, as neural learning and
symbolic reasoning offer complementary characteristics that can benefit the
development of AI systems. Relational labelling or link prediction on knowledge
graphs has become one of the main problems in deep learning-based natural
language processing research. Moreover, other fields which make use of
neural-symbolic techniques may also benefit from such research endeavours.
There have been several efforts towards the identification of missing facts
from existing ones in knowledge graphs. Two lines of research try and predict
knowledge relations between two entities by considering all known facts
connecting them or several paths of facts connecting them. We propose a
neural-symbolic graph neural network which applies learning over all the paths
by feeding the model with the embedding of the minimal subset of the knowledge
graph containing such paths. By learning to produce representations for
entities and facts corresponding to word embeddings, we show how the model can
be trained end-to-end to decode these representations and infer relations
between entities in a multitask approach. Our contribution is two-fold: a
neural-symbolic methodology leverages the resolution of relational inference in
large graphs, and we also demonstrate that such neural-symbolic model is shown
more effective than path-based approachesComment: Under review: ICANN 202
Recurrent One-Hop Predictions for Reasoning over Knowledge Graphs
Large scale knowledge graphs (KGs) such as Freebase are generally incomplete. Reasoning over multi-hop (mh) KG paths is thus an important capability that is needed for question answering or other NLP tasks that require knowledge about the world. mh-KG reasoning includes diverse scenarios, e.g., given a head entity and a relation path, predict the tail entity; or given two enti- ties connected by some relation paths, predict the unknown relation between them. We present ROPs, recurrent one-hop predictors, that predict entities at each step of mh-KB paths by using recurrent neural networks and vector representations of entities and relations, with two benefits: (i) modeling mh-paths of arbitrary lengths while updating the entity and relation representations by the training signal at each step; (ii) handling different types of mh-KG reasoning in a unified framework. Our models show state-of-the-art for two important multi-hop KG reasoning tasks: Knowledge Base Completion and Path Query Answerin
Recurrent One-Hop Predictions for Reasoning over Knowledge Graphs
Large scale knowledge graphs (KGs) such as Freebase are generally incomplete. Reasoning over multi-hop (mh) KG paths is thus an important capability that is needed for question answering or other NLP tasks that require knowledge about the world. mh-KG reasoning includes diverse scenarios, e.g., given a head entity and a relation path, predict the tail entity; or given two enti- ties connected by some relation paths, predict the unknown relation between them. We present ROPs, recurrent one-hop predictors, that predict entities at each step of mh-KB paths by using recurrent neural networks and vector representations of entities and relations, with two benefits: (i) modeling mh-paths of arbitrary lengths while updating the entity and relation representations by the training signal at each step; (ii) handling different types of mh-KG reasoning in a unified framework. Our models show state-of-the-art for two important multi-hop KG reasoning tasks: Knowledge Base Completion and Path Query Answerin
A Semantic Information Management Approach for Improving Bridge Maintenance based on Advanced Constraint Management
Bridge rehabilitation projects are important for transportation infrastructures. This research proposes a novel information management approach based on state-of-the-art deep learning models and ontologies. The approach can automatically extract, integrate, complete, and search for project knowledge buried in unstructured text documents. The approach on the one hand facilitates implementation of modern management approaches, i.e., advanced working packaging to delivery success bridge rehabilitation projects, on the other hand improves information management practices in the construction industry