347 research outputs found
Relphormer: Relational Graph Transformer for Knowledge Graph Representations
Transformers have achieved remarkable performance in widespread fields,
including natural language processing, computer vision and graph mining.
However, vanilla Transformer architectures have not yielded promising
improvements in the Knowledge Graph (KG) representations, where the
translational distance paradigm dominates this area. Note that vanilla
Transformer architectures struggle to capture the intrinsically heterogeneous
structural and semantic information of knowledge graphs. To this end, we
propose a new variant of Transformer for knowledge graph representations dubbed
Relphormer. Specifically, we introduce Triple2Seq which can dynamically sample
contextualized sub-graph sequences as the input to alleviate the heterogeneity
issue. We propose a novel structure-enhanced self-attention mechanism to encode
the relational information and keep the semantic information within entities
and relations. Moreover, we utilize masked knowledge modeling for general
knowledge graph representation learning, which can be applied to various
KG-based tasks including knowledge graph completion, question answering, and
recommendation. Experimental results on six datasets show that Relphormer can
obtain better performance compared with baselines. Code is available in
https://github.com/zjunlp/Relphormer.Comment: Work in progres
NEMISA Digital Skills Conference (Colloquium) 2023
The purpose of the colloquium and events centred around the central role that data plays
today as a desirable commodity that must become an important part of massifying digital
skilling efforts. Governments amass even more critical data that, if leveraged, could
change the way public services are delivered, and even change the social and economic
fortunes of any country. Therefore, smart governments and organisations increasingly
require data skills to gain insights and foresight, to secure themselves, and for improved
decision making and efficiency. However, data skills are scarce, and even more
challenging is the inconsistency of the associated training programs with most curated for
the Science, Technology, Engineering, and Mathematics (STEM) disciplines.
Nonetheless, the interdisciplinary yet agnostic nature of data means that there is
opportunity to expand data skills into the non-STEM disciplines as well.College of Engineering, Science and Technolog
Detection of Groups with Biased Representation in Ranking
Real-life tools for decision-making in many critical domains are based on
ranking results. With the increasing awareness of algorithmic fairness, recent
works have presented measures for fairness in ranking. Many of those
definitions consider the representation of different ``protected groups'', in
the top- ranked items, for any reasonable . Given the protected groups,
confirming algorithmic fairness is a simple task. However, the groups'
definitions may be unknown in advance. In this paper, we study the problem of
detecting groups with biased representation in the top- ranked items,
eliminating the need to pre-define protected groups. The number of such groups
possible can be exponential, making the problem hard. We propose efficient
search algorithms for two different fairness measures: global representation
bounds, and proportional representation. Then we propose a method to explain
the bias in the representations of groups utilizing the notion of Shapley
values. We conclude with an experimental study, showing the scalability of our
approach and demonstrating the usefulness of the proposed algorithms
A Closer Look into Recent Video-based Learning Research: A Comprehensive Review of Video Characteristics, Tools, Technologies, and Learning Effectiveness
People increasingly use videos on the Web as a source for learning. To
support this way of learning, researchers and developers are continuously
developing tools, proposing guidelines, analyzing data, and conducting
experiments. However, it is still not clear what characteristics a video should
have to be an effective learning medium. In this paper, we present a
comprehensive review of 257 articles on video-based learning for the period
from 2016 to 2021. One of the aims of the review is to identify the video
characteristics that have been explored by previous work. Based on our
analysis, we suggest a taxonomy which organizes the video characteristics and
contextual aspects into eight categories: (1) audio features, (2) visual
features, (3) textual features, (4) instructor behavior, (5) learners
activities, (6) interactive features (quizzes, etc.), (7) production style, and
(8) instructional design. Also, we identify four representative research
directions: (1) proposals of tools to support video-based learning, (2) studies
with controlled experiments, (3) data analysis studies, and (4) proposals of
design guidelines for learning videos. We find that the most explored
characteristics are textual features followed by visual features, learner
activities, and interactive features. Text of transcripts, video frames, and
images (figures and illustrations) are most frequently used by tools that
support learning through videos. The learner activity is heavily explored
through log files in data analysis studies, and interactive features have been
frequently scrutinized in controlled experiments. We complement our review by
contrasting research findings that investigate the impact of video
characteristics on the learning effectiveness, report on tasks and technologies
used to develop tools that support learning, and summarize trends of design
guidelines to produce learning video
Towards a Transparent and an Environmental-Friendly Approach for Short Text Topic Detection : A Comparison of Methods for Performance, Transparency, and Carbon Footprint
Peer reviewedPublisher PD
Combating Misinformation in the Age of LLMs: Opportunities and Challenges
Misinformation such as fake news and rumors is a serious threat on
information ecosystems and public trust. The emergence of Large Language Models
(LLMs) has great potential to reshape the landscape of combating
misinformation. Generally, LLMs can be a double-edged sword in the fight. On
the one hand, LLMs bring promising opportunities for combating misinformation
due to their profound world knowledge and strong reasoning abilities. Thus, one
emergent question is: how to utilize LLMs to combat misinformation? On the
other hand, the critical challenge is that LLMs can be easily leveraged to
generate deceptive misinformation at scale. Then, another important question
is: how to combat LLM-generated misinformation? In this paper, we first
systematically review the history of combating misinformation before the advent
of LLMs. Then we illustrate the current efforts and present an outlook for
these two fundamental questions respectively. The goal of this survey paper is
to facilitate the progress of utilizing LLMs for fighting misinformation and
call for interdisciplinary efforts from different stakeholders for combating
LLM-generated misinformation.Comment: 9 pages for the main paper, 35 pages including 656 references, more
resources on "LLMs Meet Misinformation" are on the website:
https://llm-misinformation.github.io
LIPIcs, Volume 261, ICALP 2023, Complete Volume
LIPIcs, Volume 261, ICALP 2023, Complete Volum
Beyond Quantity: Research with Subsymbolic AI
How do artificial neural networks and other forms of artificial intelligence interfere with methods and practices in the sciences? Which interdisciplinary epistemological challenges arise when we think about the use of AI beyond its dependency on big data? Not only the natural sciences, but also the social sciences and the humanities seem to be increasingly affected by current approaches of subsymbolic AI, which master problems of quality (fuzziness, uncertainty) in a hitherto unknown way. But what are the conditions, implications, and effects of these (potential) epistemic transformations and how must research on AI be configured to address them adequately
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