12 research outputs found
Leveraging multilingual descriptions for link prediction: Initial experiments
In most Knowledge Graphs (KGs), textual descriptions ofentities are provided in multiple natural languages. Additional informa-tion that is not explicitly represented in the structured part of the KGmight be available in these textual descriptions. Link prediction modelswhich make use of entity descriptions usually consider only one language.However, descriptions given in multiple languages may provide comple-mentary information which should be taken into consideration for thetasks such as link prediction. In this poster paper, the benefits of mul-tilingual embeddings for incorporating multilingual entity descriptionsinto the task of link prediction in KGs are investigate
Semantic entity enrichment by leveraging multilingual descriptions for link prediction
Most Knowledge Graphs (KGs) contain textual descriptions of entities in various natural languages. These descriptions of entities provide valuable information that may not be explicitly represented in the structured part of the KG. Based on this fact, some link prediction methods which make use of the information presented in the textual descriptions of entities have been proposed to learn representations of (monolingual) KGs. However, these methods use entity descriptions in only one language and ignore the fact that descriptions given in different languages may provide complementary information and thereby also additional semantics. In this position paper, the problem of effectively leveraging multilingual entity descriptions for the purpose of link prediction in KGs will be discussed along with potential solutions to the problem
ReadNet: A Hierarchical Transformer Framework for Web Article Readability Analysis
Analyzing the readability of articles has been an important sociolinguistic
task. Addressing this task is necessary to the automatic recommendation of
appropriate articles to readers with different comprehension abilities, and it
further benefits education systems, web information systems, and digital
libraries. Current methods for assessing readability employ empirical measures
or statistical learning techniques that are limited by their ability to
characterize complex patterns such as article structures and semantic meanings
of sentences. In this paper, we propose a new and comprehensive framework which
uses a hierarchical self-attention model to analyze document readability. In
this model, measurements of sentence-level difficulty are captured along with
the semantic meanings of each sentence. Additionally, the sentence-level
features are incorporated to characterize the overall readability of an article
with consideration of article structures. We evaluate our proposed approach on
three widely-used benchmark datasets against several strong baseline
approaches. Experimental results show that our proposed method achieves the
state-of-the-art performance on estimating the readability for various web
articles and literature.Comment: ECIR 202
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
Collaborative Recommendation Model Based on Multi-modal Multi-view Attention Network: Movie and literature cases
The existing collaborative recommendation models that use multi-modal
information emphasize the representation of users' preferences but easily
ignore the representation of users' dislikes. Nevertheless, modelling users'
dislikes facilitates comprehensively characterizing user profiles. Thus, the
representation of users' dislikes should be integrated into the user modelling
when we construct a collaborative recommendation model. In this paper, we
propose a novel Collaborative Recommendation Model based on Multi-modal
multi-view Attention Network (CRMMAN), in which the users are represented from
both preference and dislike views. Specifically, the users' historical
interactions are divided into positive and negative interactions, used to model
the user's preference and dislike views, respectively. Furthermore, the
semantic and structural information extracted from the scene is employed to
enrich the item representation. We validate CRMMAN by designing contrast
experiments based on two benchmark MovieLens-1M and Book-Crossing datasets.
Movielens-1m has about a million ratings, and Book-Crossing has about 300,000
ratings. Compared with the state-of-the-art knowledge-graph-based and
multi-modal recommendation methods, the AUC, NDCG@5 and NDCG@10 are improved by
2.08%, 2.20% and 2.26% on average of two datasets. We also conduct controlled
experiments to explore the effects of multi-modal information and multi-view
mechanism. The experimental results show that both of them enhance the model's
performance