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Register a new userTowards Automatic Bias Detection in Knowledge Graphs
نحو كشف التحيز التلقائي في الرسوم البيانية المعرفة
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مع زيادة الطفرة الأخيرة في التطبيقات الاجتماعية التي تعتمد على الرسوم البيانية المعرفة، أصبحت الحاجة إلى التقنيات لضمان الإنصاف في الأساليب القائمة على KG واضحة بشكل متزايد. أظهرت الأعمال السابقة أن كلية كجمها عرضة للحيوانات الاجتماعية المختلفة، وقد اقترحت طرق متعددة لدخاناتها. ومع ذلك، في مثل هذه الدراسات، كان التركيز على تقنيات deviasing، في حين يتم تحديد العلاقات التي ستكون degiased يدويا من قبل المستخدم. نظرا لأن المواصفات اليدوية هي نفسها عرضة للتحيز الإدراكي البشري، فهناك حاجة إلى نظام قادر على قياس وفضح التحيزات، التي يمكن أن تدعم قرارات أكثر استنارة بشأن ما له ديبي. لمعالجة هذه الفجوة في الأدب، وصفنا إطارا لتحديد التحيزات الموجودة في Adments Graph Admings، بناء على مقاييس BIAS الرقمية. نوضح الإطار بثلاث تدابير تحيز مختلفة حول مهمة التنبؤ بالمهنة، ويمكن امتدت بمرونة لتعريفات وتطبيقات إضافية. يمكن بعد ذلك تسليم العلاقات التي يتم تمييزها على أنها منحازة إلى صانعي القرار للحكم على الدخل اللاحق.
With the recent surge in social applications relying on knowledge graphs, the need for techniques to ensure fairness in KG based methods is becoming increasingly evident. Previous works have demonstrated that KGs are prone to various social biases, and have proposed multiple methods for debiasing them. However, in such studies, the focus has been on debiasing techniques, while the relations to be debiased are specified manually by the user. As manual specification is itself susceptible to human cognitive bias, there is a need for a system capable of quantifying and exposing biases, that can support more informed decisions on what to debias. To address this gap in the literature, we describe a framework for identifying biases present in knowledge graph embeddings, based on numerical bias metrics. We illustrate the framework with three different bias measures on the task of profession prediction, and it can be flexibly extended to further bias definitions and applications. The relations flagged as biased can then be handed to decision makers for judgement upon subsequent debiasing.
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Knowledge graph entity typing aims to infer entities' missing types in knowledge graphs which is an important but under-explored issue. This paper proposes a novel method for this task by utilizing entities' contextual information. Specifically, we d
esign two inference mechanisms: i) N2T: independently use each neighbor of an entity to infer its type; ii) Agg2T: aggregate the neighbors of an entity to infer its type. Those mechanisms will produce multiple inference results, and an exponentially weighted pooling method is used to generate the final inference result. Furthermore, we propose a novel loss function to alleviate the false-negative problem during training. Experiments on two real-world KGs demonstrate the effectiveness of our method. The source code and data of this paper can be obtained from https://github.com/CCIIPLab/CET.
The design of expressive representations of entities and relations in a knowledge graph is an important endeavor. While many of the existing approaches have primarily focused on learning from relational patterns and structural information, the intrin
sic complexity of KG entities has been more or less overlooked. More concretely, we hypothesize KG entities may be more complex than we think, i.e., an entity may wear many hats and relational triplets may form due to more than a single reason. To this end, this paper proposes to learn disentangled representations of KG entities - a new method that disentangles the inner latent properties of KG entities. Our disentangled process operates at the graph level and a neighborhood mechanism is leveraged to disentangle the hidden properties of each entity. This disentangled representation learning approach is model agnostic and compatible with canonical KG embedding approaches. We conduct extensive experiments on several benchmark datasets, equipping a variety of models (DistMult, SimplE, and QuatE) with our proposed disentangling mechanism. Experimental results demonstrate that our proposed approach substantially improves performance on key metrics.
Knowledge Graphs (KGs) have become increasingly popular in the recent years. However, as knowledge constantly grows and changes, it is inevitable to extend existing KGs with entities that emerged or became relevant to the scope of the KG after its cr
eation. Research on updating KGs typically relies on extracting named entities and relations from text. However, these approaches cannot infer entities or relations that were not explicitly stated. Alternatively, embedding models exploit implicit structural regularities to predict missing relations, but cannot predict missing entities. In this article, we introduce a novel method to enrich a KG with new entities given their textual description. Our method leverages joint embedding models, hence does not require entities or relations to be named explicitly. We show that our approach can identify new concepts in a document corpus and transfer them into the KG, and we find that the performance of our method improves substantially when extended with techniques from association rule mining, text mining, and active learning.
Interactive machine reading comprehension (iMRC) is machine comprehension tasks where knowledge sources are partially observable. An agent must interact with an environment sequentially to gather necessary knowledge in order to answer a question. We
hypothesize that graph representations are good inductive biases, which can serve as an agent's memory mechanism in iMRC tasks. We explore four different categories of graphs that can capture text information at various levels. We describe methods that dynamically build and update these graphs during information gathering, as well as neural models to encode graph representations in RL agents. Extensive experiments on iSQuAD suggest that graph representations can result in significant performance improvements for RL agents.
Knowledge graphs (KGs) are widely used to store and access information about entities and their relationships. Given a query, the task of entity retrieval from a KG aims at presenting a ranked list of entities relevant to the query. Lately, an increa
sing number of models for entity retrieval have shown a significant improvement over traditional methods. These models, however, were developed for English KGs. In this work, we build on one such system, named KEWER, to propose SERAG (Semantic Entity Retrieval from Arabic knowledge Graphs). Like KEWER, SERAG uses random walks to generate entity embeddings. DBpedia-Entity v2 is considered the standard test collection for entity retrieval. We discuss the challenges of using it for non-English languages in general and Arabic in particular. We provide an Arabic version of this standard collection, and use it to evaluate SERAG. SERAG is shown to significantly outperform the popular BM25 model thanks to its multi-hop reasoning.
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