24,920 research outputs found
Encoding Knowledge Graph Entity Aliases in Attentive Neural Network for Wikidata Entity Linking
The collaborative knowledge graphs such as Wikidata excessively rely on the
crowd to author the information. Since the crowd is not bound to a standard
protocol for assigning entity titles, the knowledge graph is populated by
non-standard, noisy, long or even sometimes awkward titles. The issue of long,
implicit, and nonstandard entity representations is a challenge in Entity
Linking (EL) approaches for gaining high precision and recall. Underlying KG,
in general, is the source of target entities for EL approaches, however, it
often contains other relevant information, such as aliases of entities (e.g.,
Obama and Barack Hussein Obama are aliases for the entity Barack Obama). EL
models usually ignore such readily available entity attributes. In this paper,
we examine the role of knowledge graph context on an attentive neural network
approach for entity linking on Wikidata. Our approach contributes by exploiting
the sufficient context from a KG as a source of background knowledge, which is
then fed into the neural network. This approach demonstrates merit to address
challenges associated with entity titles (multi-word, long, implicit,
case-sensitive). Our experimental study shows approx 8% improvements over the
baseline approach, and significantly outperform an end to end approach for
Wikidata entity linking.Comment: 15 page
Physician Organization in Relation to Quality and Efficiency of Care: A Synthesis of Recent Literature
Summarizes research linking cohesion, scale, and affiliation in physician groups to improved quality and efficiency. Discusses implications for promoting delivery system reform through physician group organization and changes to the payment system
Encoding Knowledge Graph Entity Aliases in Attentive Neural Network for Wikidata Entity Linking
The collaborative knowledge graphs such as Wikidata excessively rely on the crowd to author the information. Since the crowd is not bound to a standard protocol for assigning entity titles, the knowledge graph is populated by non-standard, noisy, long or even sometimes awkward titles. The issue of long, implicit, and nonstandard entity representations is a challenge in Entity Linking (EL) approaches for gaining high precision and recall. Underlying KG in general is the source of target entities for EL approaches, however, it often contains other relevant information, such as aliases of entities (e.g., Obama and Barack Hussein Obama are aliases for the entity Barack Obama). EL models usually ignore such readily available entity attributes. In this paper, we examine the role of knowledge graph context on an attentive neural network approach for entity linking on Wikidata. Our approach contributes by exploiting the sufficient context from a KG as a source of background knowledge, which is then fed into the neural network. This approach demonstrates merit to address challenges associated with entity titles (multi-word, long, implicit, case-sensitive). Our experimental study shows ≈8% improvements over the baseline approach, and significantly outperform an end to end approach for Wikidata entity linking
SenseDefs : a multilingual corpus of semantically annotated textual definitions
Definitional knowledge has proved to be essential in various Natural Language Processing tasks and applications, especially when information at the level of word senses is exploited. However, the few sense-annotated corpora of textual definitions available to date are of limited size: this is mainly due to the expensive and time-consuming process of annotating a wide variety of word senses and entity mentions at a reasonably high scale. In this paper we present SenseDefs, a large-scale high-quality corpus of disambiguated definitions (or glosses) in multiple languages, comprising sense annotations of both concepts and named entities from a wide-coverage unified sense inventory. Our approach for the construction and disambiguation of this corpus builds upon the structure of a large multilingual semantic network and a state-of-the-art disambiguation system: first, we gather complementary information of equivalent definitions across different languages to provide context for disambiguation; then we refine the disambiguation output with a distributional approach based on semantic similarity. As a result, we obtain a multilingual corpus of textual definitions featuring over 38 million definitions in 263 languages, and we publicly release it to the research community. We assess the quality of SenseDefs’s sense annotations both intrinsically and extrinsically on Open Information Extraction and Sense Clustering tasks.Peer reviewe
The Accuracy of Subhalo Detection
With the ever increasing resolution of N-body simulations, accurate subhalo
detection is becoming essential in the study of the formation of structure, the
production of merger trees and the seeding of semi-analytic models. To
investigate the state of halo finders, we compare two different approaches to
detecting subhaloes; the first based on overdensities in a halo and the second
being adaptive mesh refinement. A set of stable mock NFW dark matter haloes
were produced and a subhalo was placed at different radii within a larger halo.
SUBFIND (a Friends-of-Friends based finder) and AHF (an adaptive mesh based
finder) were employed to recover the subhalo. As expected, we found that the
mass of the subhalo recovered by SUBFIND has a strong dependence on the radial
position and that neither halo finder can accurately recover the subhalo when
it is very near the centre of the halo. This radial dependence is shown to be
related to the subhalo being truncated by the background density of the halo
and originates due to the subhalo being defined as an overdensity. If the
subhalo size is instead determined using the peak of the circular velocity
profile, a much more stable value is recovered. The downside to this is that
the maximum circular velocity is a poor measure of stripping and is affected by
resolution. For future halo finders to recover all the particles in a subhalo,
a search of phase space will need to be introduced.Comment: 9 pages, 7 figures, accepted for publication in MNRA
Large Language Models and Knowledge Graphs: Opportunities and Challenges
Large Language Models (LLMs) have taken Knowledge Representation -- and the
world -- by storm. This inflection point marks a shift from explicit knowledge
representation to a renewed focus on the hybrid representation of both explicit
knowledge and parametric knowledge. In this position paper, we will discuss
some of the common debate points within the community on LLMs (parametric
knowledge) and Knowledge Graphs (explicit knowledge) and speculate on
opportunities and visions that the renewed focus brings, as well as related
research topics and challenges.Comment: 30 page
Distant Learning for Entity Linking with Automatic Noise Detection
Accurate entity linkers have been produced for domains and languages where
annotated data (i.e., texts linked to a knowledge base) is available. However,
little progress has been made for the settings where no or very limited amounts
of labeled data are present (e.g., legal or most scientific domains). In this
work, we show how we can learn to link mentions without having any labeled
examples, only a knowledge base and a collection of unannotated texts from the
corresponding domain. In order to achieve this, we frame the task as a
multi-instance learning problem and rely on surface matching to create initial
noisy labels. As the learning signal is weak and our surrogate labels are
noisy, we introduce a noise detection component in our model: it lets the model
detect and disregard examples which are likely to be noisy. Our method, jointly
learning to detect noise and link entities, greatly outperforms the surface
matching baseline. For a subset of entity categories, it even approaches the
performance of supervised learning.Comment: ACL 201
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