1,977 research outputs found
Explainable Link Prediction for Emerging Entities in Knowledge Graphs
Despite their large-scale coverage, cross-domain knowledge graphs invariably
suffer from inherent incompleteness and sparsity. Link prediction can alleviate
this by inferring a target entity, given a source entity and a query relation.
Recent embedding-based approaches operate in an uninterpretable latent semantic
vector space of entities and relations, while path-based approaches operate in
the symbolic space, making the inference process explainable. However, these
approaches typically consider static snapshots of the knowledge graphs,
severely restricting their applicability for evolving knowledge graphs with
newly emerging entities. To overcome this issue, we propose an inductive
representation learning framework that is able to learn representations of
previously unseen entities. Our method finds reasoning paths between source and
target entities, thereby making the link prediction for unseen entities
interpretable and providing support evidence for the inferred link.Comment: To appear in the proceedings of International Semantic Web
Conference, 2020 (ISWC 2020
Dynamically Fused Graph Network for Multi-hop Reasoning
Text-based question answering (TBQA) has been studied extensively in recent
years. Most existing approaches focus on finding the answer to a question
within a single paragraph. However, many difficult questions require multiple
supporting evidence from scattered text among two or more documents. In this
paper, we propose Dynamically Fused Graph Network(DFGN), a novel method to
answer those questions requiring multiple scattered evidence and reasoning over
them. Inspired by human's step-by-step reasoning behavior, DFGN includes a
dynamic fusion layer that starts from the entities mentioned in the given
query, explores along the entity graph dynamically built from the text, and
gradually finds relevant supporting entities from the given documents. We
evaluate DFGN on HotpotQA, a public TBQA dataset requiring multi-hop reasoning.
DFGN achieves competitive results on the public board. Furthermore, our
analysis shows DFGN produces interpretable reasoning chains.Comment: Accepted by ACL 1
An Interpretable Reasoning Network for Multi-Relation Question Answering
Multi-relation Question Answering is a challenging task, due to the
requirement of elaborated analysis on questions and reasoning over multiple
fact triples in knowledge base. In this paper, we present a novel model called
Interpretable Reasoning Network that employs an interpretable, hop-by-hop
reasoning process for question answering. The model dynamically decides which
part of an input question should be analyzed at each hop; predicts a relation
that corresponds to the current parsed results; utilizes the predicted relation
to update the question representation and the state of the reasoning process;
and then drives the next-hop reasoning. Experiments show that our model yields
state-of-the-art results on two datasets. More interestingly, the model can
offer traceable and observable intermediate predictions for reasoning analysis
and failure diagnosis, thereby allowing manual manipulation in predicting the
final answer.Comment: COLING 2018, 13page
Multi-Hop Knowledge Graph Reasoning with Reward Shaping
Multi-hop reasoning is an effective approach for query answering (QA) over
incomplete knowledge graphs (KGs). The problem can be formulated in a
reinforcement learning (RL) setup, where a policy-based agent sequentially
extends its inference path until it reaches a target. However, in an incomplete
KG environment, the agent receives low-quality rewards corrupted by false
negatives in the training data, which harms generalization at test time.
Furthermore, since no golden action sequence is used for training, the agent
can be misled by spurious search trajectories that incidentally lead to the
correct answer. We propose two modeling advances to address both issues: (1) we
reduce the impact of false negative supervision by adopting a pretrained
one-hop embedding model to estimate the reward of unobserved facts; (2) we
counter the sensitivity to spurious paths of on-policy RL by forcing the agent
to explore a diverse set of paths using randomly generated edge masks. Our
approach significantly improves over existing path-based KGQA models on several
benchmark datasets and is comparable or better than embedding-based models.Comment: Accepted to EMNLP 2018, 12 page
Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning
Knowledge bases (KB), both automatically and manually constructed, are often
incomplete --- many valid facts can be inferred from the KB by synthesizing
existing information. A popular approach to KB completion is to infer new
relations by combinatory reasoning over the information found along other paths
connecting a pair of entities. Given the enormous size of KBs and the
exponential number of paths, previous path-based models have considered only
the problem of predicting a missing relation given two entities or evaluating
the truth of a proposed triple. Additionally, these methods have traditionally
used random paths between fixed entity pairs or more recently learned to pick
paths between them. We propose a new algorithm MINERVA, which addresses the
much more difficult and practical task of answering questions where the
relation is known, but only one entity. Since random walks are impractical in a
setting with combinatorially many destinations from a start node, we present a
neural reinforcement learning approach which learns how to navigate the graph
conditioned on the input query to find predictive paths. Empirically, this
approach obtains state-of-the-art results on several datasets, significantly
outperforming prior methods.Comment: ICLR 201
Exploiting Explicit Paths for Multi-hop Reading Comprehension
We propose a novel, path-based reasoning approach for the multi-hop reading
comprehension task where a system needs to combine facts from multiple passages
to answer a question. Although inspired by multi-hop reasoning over knowledge
graphs, our proposed approach operates directly over unstructured text. It
generates potential paths through passages and scores them without any direct
path supervision. The proposed model, named PathNet, attempts to extract
implicit relations from text through entity pair representations, and compose
them to encode each path. To capture additional context, PathNet also composes
the passage representations along each path to compute a passage-based
representation. Unlike previous approaches, our model is then able to explain
its reasoning via these explicit paths through the passages. We show that our
approach outperforms prior models on the multi-hop Wikihop dataset, and also
can be generalized to apply to the OpenBookQA dataset, matching
state-of-the-art performance
UHop: An Unrestricted-Hop Relation Extraction Framework for Knowledge-Based Question Answering
In relation extraction for knowledge-based question answering, searching from
one entity to another entity via a single relation is called "one hop". In
related work, an exhaustive search from all one-hop relations, two-hop
relations, and so on to the max-hop relations in the knowledge graph is
necessary but expensive. Therefore, the number of hops is generally restricted
to two or three. In this paper, we propose UHop, an unrestricted-hop framework
which relaxes this restriction by use of a transition-based search framework to
replace the relation-chain-based search one. We conduct experiments on
conventional 1- and 2-hop questions as well as lengthy questions, including
datasets such as WebQSP, PathQuestion, and Grid World. Results show that the
proposed framework enables the ability to halt, works well with
state-of-the-art models, achieves competitive performance without exhaustive
searches, and opens the performance gap for long relation paths.Comment: To appear in NAACL-HLT 201
Reasoning over RDF Knowledge Bases using Deep Learning
Semantic Web knowledge representation standards, and in particular RDF and
OWL, often come endowed with a formal semantics which is considered to be of
fundamental importance for the field. Reasoning, i.e., the drawing of logical
inferences from knowledge expressed in such standards, is traditionally based
on logical deductive methods and algorithms which can be proven to be sound and
complete and terminating, i.e. correct in a very strong sense. For various
reasons, though, in particular, the scalability issues arising from the
ever-increasing amounts of Semantic Web data available and the inability of
deductive algorithms to deal with noise in the data, it has been argued that
alternative means of reasoning should be investigated which bear high promise
for high scalability and better robustness. From this perspective, deductive
algorithms can be considered the gold standard regarding correctness against
which alternative methods need to be tested. In this paper, we show that it is
possible to train a Deep Learning system on RDF knowledge graphs, such that it
is able to perform reasoning over new RDF knowledge graphs, with high precision
and recall compared to the deductive gold standard
Multi-hop Reading Comprehension via Deep Reinforcement Learning based Document Traversal
Reading Comprehension has received significant attention in recent years as
high quality Question Answering (QA) datasets have become available. Despite
state-of-the-art methods achieving strong overall accuracy, Multi-Hop (MH)
reasoning remains particularly challenging. To address MH-QA specifically, we
propose a Deep Reinforcement Learning based method capable of learning
sequential reasoning across large collections of documents so as to pass a
query-aware, fixed-size context subset to existing models for answer
extraction. Our method is comprised of two stages: a linker, which decomposes
the provided support documents into a graph of sentences, and an extractor,
which learns where to look based on the current question and already-visited
sentences. The result of the linker is a novel graph structure at the sentence
level that preserves logical flow while still allowing rapid movement between
documents. Importantly, we demonstrate that the sparsity of the resultant graph
is invariant to context size. This translates to fewer decisions required from
the Deep-RL trained extractor, allowing the system to scale effectively to
large collections of documents.
The importance of sequential decision making in the document traversal step
is demonstrated by comparison to standard IE methods, and we additionally
introduce a BM25-based IR baseline that retrieves documents relevant to the
query only. We examine the integration of our method with existing models on
the recently proposed QAngaroo benchmark and achieve consistent increases in
accuracy across the board, as well as a 2-3x reduction in training time
PullNet: Open Domain Question Answering with Iterative Retrieval on Knowledge Bases and Text
We consider open-domain queston answering (QA) where answers are drawn from
either a corpus, a knowledge base (KB), or a combination of both of these. We
focus on a setting in which a corpus is supplemented with a large but
incomplete KB, and on questions that require non-trivial (e.g., ``multi-hop'')
reasoning. We describe PullNet, an integrated framework for (1) learning what
to retrieve (from the KB and/or corpus) and (2) reasoning with this
heterogeneous information to find the best answer. PullNet uses an {iterative}
process to construct a question-specific subgraph that contains information
relevant to the question. In each iteration, a graph convolutional network
(graph CNN) is used to identify subgraph nodes that should be expanded using
retrieval (or ``pull'') operations on the corpus and/or KB. After the subgraph
is complete, a similar graph CNN is used to extract the answer from the
subgraph. This retrieve-and-reason process allows us to answer multi-hop
questions using large KBs and corpora. PullNet is weakly supervised, requiring
question-answer pairs but not gold inference paths. Experimentally PullNet
improves over the prior state-of-the art, and in the setting where a corpus is
used with incomplete KB these improvements are often dramatic. PullNet is also
often superior to prior systems in a KB-only setting or a text-only setting
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