121 research outputs found
Inductive Entity Representations from Text via Link Prediction
Knowledge Graphs (KG) are of vital importance for multiple applications on
the web, including information retrieval, recommender systems, and metadata
annotation. Regardless of whether they are built manually by domain experts or
with automatic pipelines, KGs are often incomplete. Recent work has begun to
explore the use of textual descriptions available in knowledge graphs to learn
vector representations of entities in order to preform link prediction.
However, the extent to which these representations learned for link prediction
generalize to other tasks is unclear. This is important given the cost of
learning such representations. Ideally, we would prefer representations that do
not need to be trained again when transferring to a different task, while
retaining reasonable performance.
In this work, we propose a holistic evaluation protocol for entity
representations learned via a link prediction objective. We consider the
inductive link prediction and entity classification tasks, which involve
entities not seen during training. We also consider an information retrieval
task for entity-oriented search. We evaluate an architecture based on a
pretrained language model, that exhibits strong generalization to entities not
observed during training, and outperforms related state-of-the-art methods (22%
MRR improvement in link prediction on average). We further provide evidence
that the learned representations transfer well to other tasks without
fine-tuning. In the entity classification task we obtain an average improvement
of 16% in accuracy compared with baselines that also employ pre-trained models.
In the information retrieval task, we obtain significant improvements of up to
8.8% in NDCG@10 for natural language queries. We thus show that the learned
representations are not limited KG-specific tasks, and have greater
generalization properties than evaluated in previous work
Query Embedding on Hyper-relational Knowledge Graphs
Multi-hop logical reasoning is an established problem in the field of
representation learning on knowledge graphs (KGs). It subsumes both one-hop
link prediction as well as other more complex types of logical queries.
Existing algorithms operate only on classical, triple-based graphs, whereas
modern KGs often employ a hyper-relational modeling paradigm. In this paradigm,
typed edges may have several key-value pairs known as qualifiers that provide
fine-grained context for facts. In queries, this context modifies the meaning
of relations, and usually reduces the answer set. Hyper-relational queries are
often observed in real-world KG applications, and existing approaches for
approximate query answering cannot make use of qualifier pairs. In this work,
we bridge this gap and extend the multi-hop reasoning problem to
hyper-relational KGs allowing to tackle this new type of complex queries.
Building upon recent advancements in Graph Neural Networks and query embedding
techniques, we study how to embed and answer hyper-relational conjunctive
queries. Besides that, we propose a method to answer such queries and
demonstrate in our experiments that qualifiers improve query answering on a
diverse set of query patterns
QAGCN: A Graph Convolutional Network-based Multi-Relation Question Answering System
Answering multi-relation questions over knowledge graphs is a challenging task as it requires multi-step reasoning over a huge number of possible paths. Reasoning-based methods with complex reasoning mechanisms, such as reinforcement learning-based sequential decision making, have been regarded as the default pathway for this task. However, these mechanisms are difficult to implement and train, which hampers their reproducibility and transferability to new domains. In this paper, we propose QAGCN - a simple but effective and novel model that leverages attentional graph convolutional networks that can perform multi-step reasoning during the encoding of knowledge graphs. As a consequence, complex reasoning mechanisms are avoided. In addition, to improve efficiency, we retrieve answers using highly-efficient embedding computations and, for better interpretability, we extract interpretable paths for returned answers. On widely adopted benchmark datasets, the proposed model has been demonstrated competitive against state-of-the-art methods that rely on complex reasoning mechanisms. We also conducted extensive experiments to scrutinize the efficiency and contribution of each component of our model
Adapting Neural Link Predictors for Data-Efficient Complex Query Answering
Answering complex queries on incomplete knowledge graphs is a challenging
task where a model needs to answer complex logical queries in the presence of
missing knowledge. Prior work in the literature has proposed to address this
problem by designing architectures trained end-to-end for the complex query
answering task with a reasoning process that is hard to interpret while
requiring data and resource-intensive training. Other lines of research have
proposed re-using simple neural link predictors to answer complex queries,
reducing the amount of training data by orders of magnitude while providing
interpretable answers. The neural link predictor used in such approaches is not
explicitly optimised for the complex query answering task, implying that its
scores are not calibrated to interact together. We propose to address these
problems via CQD, a parameter-efficient score \emph{adaptation}
model optimised to re-calibrate neural link prediction scores for the complex
query answering task. While the neural link predictor is frozen, the adaptation
component -- which only increases the number of model parameters by --
is trained on the downstream complex query answering task. Furthermore, the
calibration component enables us to support reasoning over queries that include
atomic negations, which was previously impossible with link predictors. In our
experiments, CQD produces significantly more accurate results
than current state-of-the-art methods, improving from to Mean
Reciprocal Rank values averaged across all datasets and query types while using
of the available training query types. We further show that
CQD is data-efficient, achieving competitive results with only
of the training complex queries, and robust in out-of-domain evaluations
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