176,075 research outputs found
Benchmarking Diverse-Modal Entity Linking with Generative Models
Entities can be expressed in diverse formats, such as texts, images, or
column names and cell values in tables. While existing entity linking (EL)
models work well on per modality configuration, such as text-only EL, visual
grounding, or schema linking, it is more challenging to design a unified model
for diverse modality configurations. To bring various modality configurations
together, we constructed a benchmark for diverse-modal EL (DMEL) from existing
EL datasets, covering all three modalities including text, image, and table. To
approach the DMEL task, we proposed a generative diverse-modal model (GDMM)
following a multimodal-encoder-decoder paradigm. Pre-training \Model with rich
corpora builds a solid foundation for DMEL without storing the entire KB for
inference. Fine-tuning GDMM builds a stronger DMEL baseline, outperforming
state-of-the-art task-specific EL models by 8.51 F1 score on average.
Additionally, extensive error analyses are conducted to highlight the
challenges of DMEL, facilitating future research on this task.Comment: 15 pages. ACL 202
When and why does the name of the brand still matter? Developing the temporal dimension of brand name equity theory
Purpose – The purpose of this paper is to fill a current gap in the literature, through the development of theory concerned with changes that occur over time to the functions and importance of the brand name element of a branded entity.
Design/methodology/approach – An initial theoretical conceptualisation was developed from the existing literature. Study participants whose behaviour was found not to conform to this initial conceptualisation were included in subsequent research in order to obtain greater understanding. The study method employed was a series of interviews, with the obtained qualitative data analysed using template analysis. This resulted in the development of a revised theoretical conceptualisation.
Findings – Various functions of the brand name element, identified as connotation, denotation, linking and branded entity constancy are ongoing important providers of brand equity to some consumers for established branded entities. This challenges a position obtained from existing literature that the brand name element of an established branded entity becomes of minimal importance over time.
Originality/value – Value generating functions of the brand name element that persist over time were identified, leading to the development of a theoretical conceptualisation of the change in the importance of brand name equity over time
LODE: Linking Digital Humanities Content to the Web of Data
Numerous digital humanities projects maintain their data collections in the
form of text, images, and metadata. While data may be stored in many formats,
from plain text to XML to relational databases, the use of the resource
description framework (RDF) as a standardized representation has gained
considerable traction during the last five years. Almost every digital
humanities meeting has at least one session concerned with the topic of digital
humanities, RDF, and linked data. While most existing work in linked data has
focused on improving algorithms for entity matching, the aim of the
LinkedHumanities project is to build digital humanities tools that work "out of
the box," enabling their use by humanities scholars, computer scientists,
librarians, and information scientists alike. With this paper, we report on the
Linked Open Data Enhancer (LODE) framework developed as part of the
LinkedHumanities project. With LODE we support non-technical users to enrich a
local RDF repository with high-quality data from the Linked Open Data cloud.
LODE links and enhances the local RDF repository without compromising the
quality of the data. In particular, LODE supports the user in the enhancement
and linking process by providing intuitive user-interfaces and by suggesting
high-quality linking candidates using tailored matching algorithms. We hope
that the LODE framework will be useful to digital humanities scholars
complementing other digital humanities tools
Integrating public datasets using linked data: challenges and design principles
The world is moving from a state where there is paucity of data to one of surfeit. These data, and datasets, are normally in different datastores and of different formats. Connecting these datasets together will increase their value and help discover interesting relationships amongst them. This paper describes our experience of using Linked Data to inter-operate these different datasets, the challenges we faced, and the solutions we devised. The paper concludes with apposite design principles for using linked data to inter-operate disparate datasets
MAG: A Multilingual, Knowledge-base Agnostic and Deterministic Entity Linking Approach
Entity linking has recently been the subject of a significant body of
research. Currently, the best performing approaches rely on trained
mono-lingual models. Porting these approaches to other languages is
consequently a difficult endeavor as it requires corresponding training data
and retraining of the models. We address this drawback by presenting a novel
multilingual, knowledge-based agnostic and deterministic approach to entity
linking, dubbed MAG. MAG is based on a combination of context-based retrieval
on structured knowledge bases and graph algorithms. We evaluate MAG on 23 data
sets and in 7 languages. Our results show that the best approach trained on
English datasets (PBOH) achieves a micro F-measure that is up to 4 times worse
on datasets in other languages. MAG, on the other hand, achieves
state-of-the-art performance on English datasets and reaches a micro F-measure
that is up to 0.6 higher than that of PBOH on non-English languages.Comment: Accepted in K-CAP 2017: Knowledge Capture Conferenc
Topic modeling for entity linking using keyphrase
This paper proposes an Entity Linking system that applies a topic modeling ranking. We apply a novel approach in order to provide new relevant elements to the model. These elements are keyphrases related to the queries and gathered from a huge Wikipedia-based knowledge resourcePeer ReviewedPostprint (author’s final draft
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