131 research outputs found
Entity Type Prediction in Knowledge Graphs using Embeddings
Open Knowledge Graphs (such as DBpedia, Wikidata, YAGO) have been recognized
as the backbone of diverse applications in the field of data mining and
information retrieval. Hence, the completeness and correctness of the Knowledge
Graphs (KGs) are vital. Most of these KGs are mostly created either via an
automated information extraction from Wikipedia snapshots or information
accumulation provided by the users or using heuristics. However, it has been
observed that the type information of these KGs is often noisy, incomplete, and
incorrect. To deal with this problem a multi-label classification approach is
proposed in this work for entity typing using KG embeddings. We compare our
approach with the current state-of-the-art type prediction method and report on
experiments with the KGs
Amnestic Forgery: an Ontology of Conceptual Metaphors
This paper presents Amnestic Forgery, an ontology for metaphor semantics,
based on MetaNet, which is inspired by the theory of Conceptual Metaphor.
Amnestic Forgery reuses and extends the Framester schema, as an ideal ontology
design framework to deal with both semiotic and referential aspects of frames,
roles, mappings, and eventually blending. The description of the resource is
supplied by a discussion of its applications, with examples taken from metaphor
generation, and the referential problems of metaphoric mappings. Both schema
and data are available from the Framester SPARQL endpoint
Analyzing Social Media for Measuring Public Attitudes towards Controversies and their Driving Factors - A Case Study of Migration
Among other ways of expressing opinions on media such as blogs, and forums, social media (such as Twitter) has become one of the most widely used channels by populations for expressing their opinions. With an increasing interest in the topic of migration in Europe, it is important to process and analyze these opinions. To this end, this study aims at measuring the public attitudes toward migration in terms of sentiments and hate speech from a large number of tweets crawled on the decisive topic of migration. This study introduces a knowledge base (KB) of anonymized migration-related annotated tweets termed as MigrationsKB (MGKB). The tweets from 2013 to July 2021 in the European countries that are hosts of immigrants are collected, pre-processed, and filtered using advanced topic modeling techniques. BERT-based entity linking and sentiment analysis, complemented by attention-based hate speech detection, are performed to annotate the curated tweets. Moreover, external databases are used to identify the potential social and economic factors causing negative public attitudes toward migration. The analysis aligns with the hypothesis that the countries with more migrants have fewer negative and hateful tweets. To further promote research in the interdisciplinary fields of social sciences and computer science, the outcomes are integrated into MGKB, which significantly extends the existing ontology to consider the public attitudes toward migrations and economic indicators. This study further discusses the use-cases and exploitation of MGKB. Finally, MGKB is made publicly available, fully supporting the FAIR principles
Analyzing social media for measuring public attitudes toward controversies and their driving factors: a case study of migration
Among other ways of expressing opinions on media such as blogs, and forums, social media (such as Twitter) has become one of the most widely used channels by populations for expressing their opinions. With an increasing interest in the topic of migration in Europe, it is important to process and analyze these opinions. To this end, this study aims at measuring the public attitudes toward migration in terms of sentiments and hate speech from a large number of tweets crawled on the decisive topic of migration. This study introduces a knowledge base (KB) of anonymized migration-related annotated tweets termed as MigrationsKB (MGKB). The tweets from 2013 to July 2021 in the European countries that are hosts of immigrants are collected, pre-processed, and filtered using advanced topic modeling techniques. BERT-based entity linking and sentiment analysis, complemented by attention-based hate speech detection, are performed to annotate the curated tweets. Moreover, external databases are used to identify the potential social and economic factors causing negative public attitudes toward migration. The analysis aligns with the hypothesis that the countries with more migrants have fewer negative and hateful tweets. To further promote research in the interdisciplinary fields of social sciences and computer science, the outcomes are integrated into MGKB, which significantly extends the existing ontology to consider the public attitudes toward migrations and economic indicators. This study further discusses the use-cases and exploitation of MGKB. Finally, MGKB is made publicly available, fully supporting the FAIR principles
Linked Metaphors
International audienceThe poster summarizes Amnestic Forgery, an ontology for metaphor semantics, based on MetaNet and Framester factual-linguistic linked data. An example of metaphor generation based on linked metaphors is shown
Is Aligning Embedding Spaces a Challenging Task? A Study on Heterogeneous Embedding Alignment Methods
Representation Learning of words and Knowledge Graphs (KG) into low
dimensional vector spaces along with its applications to many real-world
scenarios have recently gained momentum. In order to make use of multiple KG
embeddings for knowledge-driven applications such as question answering, named
entity disambiguation, knowledge graph completion, etc., alignment of different
KG embedding spaces is necessary. In addition to multilinguality and
domain-specific information, different KGs pose the problem of structural
differences making the alignment of the KG embeddings more challenging. This
paper provides a theoretical analysis and comparison of the state-of-the-art
alignment methods between two embedding spaces representing entity-entity and
entity-word. This paper also aims at assessing the capability and short-comings
of the existing alignment methods on the pretext of different applications
Towards Semantically Enriched Embeddings for Knowledge Graph Completion
Embedding based Knowledge Graph (KG) Completion has gained much attention
over the past few years. Most of the current algorithms consider a KG as a
multidirectional labeled graph and lack the ability to capture the semantics
underlying the schematic information. In a separate development, a vast amount
of information has been captured within the Large Language Models (LLMs) which
has revolutionized the field of Artificial Intelligence. KGs could benefit from
these LLMs and vice versa. This vision paper discusses the existing algorithms
for KG completion based on the variations for generating KG embeddings. It
starts with discussing various KG completion algorithms such as transductive
and inductive link prediction and entity type prediction algorithms. It then
moves on to the algorithms utilizing type information within the KGs, LLMs, and
finally to algorithms capturing the semantics represented in different
description logic axioms. We conclude the paper with a critical reflection on
the current state of work in the community and give recommendations for future
directions
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
RAILD: Towards Leveraging Relation Features for Inductive Link Prediction In Knowledge Graphs
Due to the open world assumption, Knowledge Graphs (KGs) are never complete.
In order to address this issue, various Link Prediction (LP) methods are
proposed so far. Some of these methods are inductive LP models which are
capable of learning representations for entities not seen during training.
However, to the best of our knowledge, none of the existing inductive LP models
focus on learning representations for unseen relations. In this work, a novel
Relation Aware Inductive Link preDiction (RAILD) is proposed for KG completion
which learns representations for both unseen entities and unseen relations. In
addition to leveraging textual literals associated with both entities and
relations by employing language models, RAILD also introduces a novel
graph-based approach to generate features for relations. Experiments are
conducted with different existing and newly created challenging benchmark
datasets and the results indicate that RAILD leads to performance improvement
over the state-of-the-art models. Moreover, since there are no existing
inductive LP models which learn representations for unseen relations, we have
created our own baselines and the results obtained with RAILD also outperform
these baselines
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