3,854 research outputs found
DOLORES: Deep Contextualized Knowledge Graph Embeddings
We introduce a new method DOLORES for learning knowledge graph embeddings
that effectively captures contextual cues and dependencies among entities and
relations. First, we note that short paths on knowledge graphs comprising of
chains of entities and relations can encode valuable information regarding
their contextual usage. We operationalize this notion by representing knowledge
graphs not as a collection of triples but as a collection of entity-relation
chains, and learn embeddings for entities and relations using deep neural
models that capture such contextual usage. In particular, our model is based on
Bi-Directional LSTMs and learn deep representations of entities and relations
from constructed entity-relation chains. We show that these representations can
very easily be incorporated into existing models to significantly advance the
state of the art on several knowledge graph prediction tasks like link
prediction, triple classification, and missing relation type prediction (in
some cases by at least 9.5%).Comment: 10 pages, 6 figure
Knowledge Graph Embeddings and Explainable AI
Knowledge graph embeddings are now a widely adopted approach to knowledge
representation in which entities and relationships are embedded in vector
spaces. In this chapter, we introduce the reader to the concept of knowledge
graph embeddings by explaining what they are, how they can be generated and how
they can be evaluated. We summarize the state-of-the-art in this field by
describing the approaches that have been introduced to represent knowledge in
the vector space. In relation to knowledge representation, we consider the
problem of explainability, and discuss models and methods for explaining
predictions obtained via knowledge graph embeddings.Comment: Federico Bianchi, Gaetano Rossiello, Luca Costabello, Matteo
Plamonari, Pasquale Minervini, Knowledge Graph Embeddings and Explainable AI.
In: Ilaria Tiddi, Freddy Lecue, Pascal Hitzler (eds.), Knowledge Graphs for
eXplainable AI -- Foundations, Applications and Challenges. Studies on the
Semantic Web, IOS Press, Amsterdam, 202
Learning Better Word Embedding by Asymmetric Low-Rank Projection of Knowledge Graph
Word embedding, which refers to low-dimensional dense vector representations
of natural words, has demonstrated its power in many natural language
processing tasks. However, it may suffer from the inaccurate and incomplete
information contained in the free text corpus as training data. To tackle this
challenge, there have been quite a few works that leverage knowledge graphs as
an additional information source to improve the quality of word embedding.
Although these works have achieved certain success, they have neglected some
important facts about knowledge graphs: (i) many relationships in knowledge
graphs are \emph{many-to-one}, \emph{one-to-many} or even \emph{many-to-many},
rather than simply \emph{one-to-one}; (ii) most head entities and tail entities
in knowledge graphs come from very different semantic spaces. To address these
issues, in this paper, we propose a new algorithm named ProjectNet. ProjecNet
models the relationships between head and tail entities after transforming them
with different low-rank projection matrices. The low-rank projection can allow
non \emph{one-to-one} relationships between entities, while different
projection matrices for head and tail entities allow them to originate in
different semantic spaces. The experimental results demonstrate that ProjectNet
yields more accurate word embedding than previous works, thus leads to clear
improvements in various natural language processing tasks
Incorporating Relevant Knowledge in Context Modeling and Response Generation
To sustain engaging conversation, it is critical for chatbots to make good
use of relevant knowledge. Equipped with a knowledge base, chatbots are able to
extract conversation-related attributes and entities to facilitate context
modeling and response generation. In this work, we distinguish the uses of
attribute and entity and incorporate them into the encoder-decoder architecture
in different manners. Based on the augmented architecture, our chatbot, namely
Mike, is able to generate responses by referring to proper entities from the
collected knowledge. To validate the proposed approach, we build a movie
conversation corpus on which the proposed approach significantly outperforms
other four knowledge-grounded models
Leveraging Knowledge Bases in LSTMs for Improving Machine Reading
This paper focuses on how to take advantage of external knowledge bases (KBs)
to improve recurrent neural networks for machine reading. Traditional methods
that exploit knowledge from KBs encode knowledge as discrete indicator
features. Not only do these features generalize poorly, but they require
task-specific feature engineering to achieve good performance. We propose
KBLSTM, a novel neural model that leverages continuous representations of KBs
to enhance the learning of recurrent neural networks for machine reading. To
effectively integrate background knowledge with information from the currently
processed text, our model employs an attention mechanism with a sentinel to
adaptively decide whether to attend to background knowledge and which
information from KBs is useful. Experimental results show that our model
achieves accuracies that surpass the previous state-of-the-art results for both
entity extraction and event extraction on the widely used ACE2005 dataset.Comment: published at ACL 201
Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning
Reasoning is essential for the development of large knowledge graphs,
especially for completion, which aims to infer new triples based on existing
ones. Both rules and embeddings can be used for knowledge graph reasoning and
they have their own advantages and difficulties. Rule-based reasoning is
accurate and explainable but rule learning with searching over the graph always
suffers from efficiency due to huge search space. Embedding-based reasoning is
more scalable and efficient as the reasoning is conducted via computation
between embeddings, but it has difficulty learning good representations for
sparse entities because a good embedding relies heavily on data richness. Based
on this observation, in this paper we explore how embedding and rule learning
can be combined together and complement each other's difficulties with their
advantages. We propose a novel framework IterE iteratively learning embeddings
and rules, in which rules are learned from embeddings with proper pruning
strategy and embeddings are learned from existing triples and new triples
inferred by rules. Evaluations on embedding qualities of IterE show that rules
help improve the quality of sparse entity embeddings and their link prediction
results. We also evaluate the efficiency of rule learning and quality of rules
from IterE compared with AMIE+, showing that IterE is capable of generating
high quality rules more efficiently. Experiments show that iteratively learning
embeddings and rules benefit each other during learning and prediction.Comment: This paper is accepted by WWW'1
A survey of embedding models of entities and relationships for knowledge graph completion
Knowledge graphs (KGs) of real-world facts about entities and their
relationships are useful resources for a variety of natural language processing
tasks. However, because knowledge graphs are typically incomplete, it is useful
to perform knowledge graph completion or link prediction, i.e. predict whether
a relationship not in the knowledge graph is likely to be true. This paper
serves as a comprehensive survey of embedding models of entities and
relationships for knowledge graph completion, summarizing up-to-date
experimental results on standard benchmark datasets and pointing out potential
future research directions.Comment: 13 pages, 2 figures and 6 table
OpenKI: Integrating Open Information Extraction and Knowledge Bases with Relation Inference
In this paper, we consider advancing web-scale knowledge extraction and
alignment by integrating OpenIE extractions in the form of (subject, predicate,
object) triples with Knowledge Bases (KB). Traditional techniques from
universal schema and from schema mapping fall in two extremes: either they
perform instance-level inference relying on embedding for (subject, object)
pairs, thus cannot handle pairs absent in any existing triples; or they perform
predicate-level mapping and completely ignore background evidence from
individual entities, thus cannot achieve satisfying quality. We propose OpenKI
to handle sparsity of OpenIE extractions by performing instance-level
inference: for each entity, we encode the rich information in its neighborhood
in both KB and OpenIE extractions, and leverage this information in relation
inference by exploring different methods of aggregation and attention. In order
to handle unseen entities, our model is designed without creating
entity-specific parameters. Extensive experiments show that this method not
only significantly improves state-of-the-art for conventional OpenIE
extractions like ReVerb, but also boosts the performance on OpenIE from
semi-structured data, where new entity pairs are abundant and data are fairly
sparse
Exploiting Structured Knowledge in Text via Graph-Guided Representation Learning
In this work, we aim at equipping pre-trained language models with structured
knowledge. We present two self-supervised tasks learning over raw text with the
guidance from knowledge graphs. Building upon entity-level masked language
models, our first contribution is an entity masking scheme that exploits
relational knowledge underlying the text. This is fulfilled by using a linked
knowledge graph to select informative entities and then masking their mentions.
In addition we use knowledge graphs to obtain distractors for the masked
entities, and propose a novel distractor-suppressed ranking objective which is
optimized jointly with masked language model. In contrast to existing
paradigms, our approach uses knowledge graphs implicitly, only during
pre-training, to inject language models with structured knowledge via learning
from raw text. It is more efficient than retrieval-based methods that perform
entity linking and integration during finetuning and inference, and generalizes
more effectively than the methods that directly learn from concatenated graph
triples. Experiments show that our proposed model achieves improved performance
on five benchmark datasets, including question answering and knowledge base
completion tasks
MedTruth: A Semi-supervised Approach to Discovering Knowledge Condition Information from Multi-Source Medical Data
Knowledge Graph (KG) contains entities and the relations between entities.
Due to its representation ability, KG has been successfully applied to support
many medical/healthcare tasks. However, in the medical domain, knowledge holds
under certain conditions. For example, symptom \emph{runny nose} highly
indicates the existence of disease \emph{whooping cough} when the patient is a
baby rather than the people at other ages. Such conditions for medical
knowledge are crucial for decision-making in various medical applications,
which is missing in existing medical KGs. In this paper, we aim to discovery
medical knowledge conditions from texts to enrich KGs.
Electronic Medical Records (EMRs) are systematized collection of clinical
data and contain detailed information about patients, thus EMRs can be a good
resource to discover medical knowledge conditions. Unfortunately, the amount of
available EMRs is limited due to reasons such as regularization. Meanwhile, a
large amount of medical question answering (QA) data is available, which can
greatly help the studied task. However, the quality of medical QA data is quite
diverse, which may degrade the quality of the discovered medical knowledge
conditions. In the light of these challenges, we propose a new truth discovery
method, MedTruth, for medical knowledge condition discovery, which incorporates
prior source quality information into the source reliability estimation
procedure, and also utilizes the knowledge triple information for trustworthy
information computation. We conduct series of experiments on real-world medical
datasets to demonstrate that the proposed method can discover meaningful and
accurate conditions for medical knowledge by leveraging both EMR and QA data.
Further, the proposed method is tested on synthetic datasets to validate its
effectiveness under various scenarios.Comment: Accepted as CIKM2019 long pape
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