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Register a new userA Divide-And-Conquer Approach for Multi-label Multi-hop Relation Detection in Knowledge Base Question Answering
مقاربة تقسيم وقهر للكشف عن العلاقة متعددة الملصقات المتعددة في قاعدة المعرفة
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الكشف عن العلاقة في أسئلة المعرفة الأساسية الإجابة، تهدف إلى تحديد مسار (ق) العلاقات بدءا من عقدة كيان الموضوع المرتبطة بعقدة الإجابة في الرسم البياني للمعرفة. قد يتكون هذا المسار من علاقات متعددة، نسميه متعدد القفز. علاوة على ذلك، للحصول على سؤال واحد، قد توجد مسارات علاقة متعددة للإجابة الصحيحة، والتي نسميها متعددة الملصقات. ومع ذلك، فإن معظم الأساليب الموجودة لا تكتشف مسارا واحدا فقط للحصول على الإجابة دون النظر في المسارات الصحيحة الأخرى، والتي قد تؤثر على الأداء النهائي. لذلك، في هذه الورقة، نقترح نهجا جديدا للقسمة والانقسام للكشف عن العلاقة متعددة الملصقات متعددة العلامات (DC-MLMH) من خلال تحللها في الكشف عن العلاقة بين الرأس وتوليد مسار العلاقة الشرطية. في محددة، يقترح آلية أخذ العينات على مسار رواية لتوليد مسارات علاقة متنوعة لمرحلة الاستدلال. يتم استخدام سياسة الأصوات للأغلبية للكشف عن إجابة KB النهائية. أجريت تجارب شاملة على مجموعة بيانات Freebaseqa القياسية. تشير النتائج التجريبية إلى أن النهج المقترح لا يتفوق فقط على خطوط خطوط الأساس التنافسية المتعددة التسمية، ولكن أيضا تفوق على بعض أساليب KBQA الحديثة.
Relation detection in knowledge base question answering, aims to identify the path(s) of relations starting from the topic entity node that is linked to the answer node in knowledge graph. Such path might consist of multiple relations, which we call multi-hop. Moreover, for a single question, there may exist multiple relation paths to the correct answer, which we call multi-label. However, most of existing approaches only detect one single path to obtain the answer without considering other correct paths, which might affect the final performance. Therefore, in this paper, we propose a novel divide-and-conquer approach for multi-label multi-hop relation detection (DC-MLMH) by decomposing it into head relation detection and conditional relation path generation. In specific, a novel path sampling mechanism is proposed to generate diverse relation paths for the inference stage. A majority-vote policy is employed to detect final KB answer. Comprehensive experiments were conducted on the FreebaseQA benchmark dataset. Experimental results show that the proposed approach not only outperforms other competitive multi-label baselines, but also has superiority over some state-of-art KBQA methods.
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Multi-hop relation detection in Knowledge Base Question Answering (KBQA) aims at retrieving the relation path starting from the topic entity to the answer node based on a given question, where the relation path may comprise multiple relations. Most o
f the existing methods treat it as a single-label learning problem while ignoring the fact that for some complex questions, there exist multiple correct relation paths in knowledge bases. Therefore, in this paper, multi-hop relation detection is considered as a multi-label learning problem. However, performing multi-label multi-hop relation detection is challenging since the numbers of both the labels and the hops are unknown. To tackle this challenge, multi-label multi-hop relation detection is formulated as a sequence generation task. A relation-aware sequence relation generation model is proposed to solve the problem in an end-to-end manner. Experimental results show the effectiveness of the proposed method for relation detection and KBQA.
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