17,957 research outputs found
SE-KGE: A Location-Aware Knowledge Graph Embedding Model for Geographic Question Answering and Spatial Semantic Lifting
Learning knowledge graph (KG) embeddings is an emerging technique for a
variety of downstream tasks such as summarization, link prediction, information
retrieval, and question answering. However, most existing KG embedding models
neglect space and, therefore, do not perform well when applied to (geo)spatial
data and tasks. For those models that consider space, most of them primarily
rely on some notions of distance. These models suffer from higher computational
complexity during training while still losing information beyond the relative
distance between entities. In this work, we propose a location-aware KG
embedding model called SE-KGE. It directly encodes spatial information such as
point coordinates or bounding boxes of geographic entities into the KG
embedding space. The resulting model is capable of handling different types of
spatial reasoning. We also construct a geographic knowledge graph as well as a
set of geographic query-answer pairs called DBGeo to evaluate the performance
of SE-KGE in comparison to multiple baselines. Evaluation results show that
SE-KGE outperforms these baselines on the DBGeo dataset for geographic logic
query answering task. This demonstrates the effectiveness of our
spatially-explicit model and the importance of considering the scale of
different geographic entities. Finally, we introduce a novel downstream task
called spatial semantic lifting which links an arbitrary location in the study
area to entities in the KG via some relations. Evaluation on DBGeo shows that
our model outperforms the baseline by a substantial margin.Comment: Accepted to Transactions in GI
Trustworthiness and Quality of Context Information
Context-aware service platforms use context information to customize their services to the current users’ situation. Due to technical limitations in sensors and context reasoning algorithms, context information does not always represent accurately the reality, and Quality of Context (QoC) models have been proposed to quantify this inaccuracy. The problems we have identified with existing QoC models is that they do not follow a standard terminology and none of them clearly differentiate quality attributes related to instances of context information (e.g. accuracy and precision) from trustworthiness, which is a quality attribute related to the context information provider. In this paper we propose a QoC model and management architecture that supports the management of QoC trustworthiness and also contributes to the terminology alignment of existing QoC models.\ud
In our QoC model, trustworthiness is a measurement of the reliability of a context information provider to provide context information about a specific entity according to a certain quality level. This trustworthiness value is used in our QoC management architecture to support context-aware service providers in the selection of trustworthy context\ud
providers. As a proof of concept to demonstrate the feasibility of our work we show a prototype implementation of our QoC model and management architecture
OTIEA:Ontology-enhanced Triple Intrinsic-Correlation for Cross-lingual Entity Alignment
Cross-lingual and cross-domain knowledge alignment without sufficient
external resources is a fundamental and crucial task for fusing irregular data.
As the element-wise fusion process aiming to discover equivalent objects from
different knowledge graphs (KGs), entity alignment (EA) has been attracting
great interest from industry and academic research recent years. Most of
existing EA methods usually explore the correlation between entities and
relations through neighbor nodes, structural information and external
resources. However, the complex intrinsic interactions among triple elements
and role information are rarely modeled in these methods, which may lead to the
inadequate illustration for triple. In addition, external resources are usually
unavailable in some scenarios especially cross-lingual and cross-domain
applications, which reflects the little scalability of these methods. To tackle
the above insufficiency, a novel universal EA framework (OTIEA) based on
ontology pair and role enhancement mechanism via triple-aware attention is
proposed in this paper without introducing external resources. Specifically, an
ontology-enhanced triple encoder is designed via mining intrinsic correlations
and ontology pair information instead of independent elements. In addition, the
EA-oriented representations can be obtained in triple-aware entity decoder by
fusing role diversity. Finally, a bidirectional iterative alignment strategy is
deployed to expand seed entity pairs. The experimental results on three
real-world datasets show that our framework achieves a competitive performance
compared with baselines
Source-Aware Embedding Training on Heterogeneous Information Networks
Heterogeneous information networks (HINs) have been extensively applied to
real-world tasks, such as recommendation systems, social networks, and citation
networks. While existing HIN representation learning methods can effectively
learn the semantic and structural features in the network, little awareness was
given to the distribution discrepancy of subgraphs within a single HIN.
However, we find that ignoring such distribution discrepancy among subgraphs
from multiple sources would hinder the effectiveness of graph embedding
learning algorithms. This motivates us to propose SUMSHINE (Scalable
Unsupervised Multi-Source Heterogeneous Information Network Embedding) -- a
scalable unsupervised framework to align the embedding distributions among
multiple sources of an HIN. Experimental results on real-world datasets in a
variety of downstream tasks validate the performance of our method over the
state-of-the-art heterogeneous information network embedding algorithms.Comment: Published in Data Intelligence 202
Type-enhanced Ensemble Triple Representation via Triple-aware Attention for Cross-lingual Entity Alignment
Entity alignment(EA) is a crucial task for integrating cross-lingual and
cross-domain knowledge graphs(KGs), which aims to discover entities referring
to the same real-world object from different KGs. Most existing methods
generate aligning entity representation by mining the relevance of triple
elements via embedding-based methods, paying little attention to triple
indivisibility and entity role diversity. In this paper, a novel framework
named TTEA -- Type-enhanced Ensemble Triple Representation via Triple-aware
Attention for Cross-lingual Entity Alignment is proposed to overcome the above
issues considering ensemble triple specificity and entity role features.
Specifically, the ensemble triple representation is derived by regarding
relation as information carrier between semantic space and type space, and
hence the noise influence during spatial transformation and information
propagation can be smoothly controlled via specificity-aware triple attention.
Moreover, our framework uses triple-ware entity enhancement to model the role
diversity of triple elements. Extensive experiments on three real-world
cross-lingual datasets demonstrate that our framework outperforms
state-of-the-art methods
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