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Register a new userxER: An Explainable Model for Entity Resolution using an Efficient Solution for the Clique Partitioning Problem
XER: نموذج قابل للتفسير لتحليل الكيان باستخدام حل فعال لمشكلة تقسيم الزمرة
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في هذه الورقة، نقترح حل عالمي قابل للتفسير لحل مشكلة NLP البارزة: قرار الكيان (ER). نحن فوركون في وقت متأخر من مشكلة تقسيم الرسم البياني. يتم تمثيل كل إشارة إلى كيان عالمي حقيقي بواسطة عقدة في الرسم البياني، وتستخدم درجات SIM الزوجية بين التفسير في ربط هذه العقد إلى زمرة واحدة بالضبط، والتي تمثل كيان عالمي حقيقي في مجال ER. في هذه الورقة، نستخدم مشكلة تقسيم Clique (CPP)، وهو ما يعد عددا صحيحا (IP) لصياغة ER كقسم رسم بياني، ثم قم بتسلط الضوء على الطبيعة القابلة للتفسير لهذه الطريقة. نظرا لأن CPP هو NP-Hard، نقدم إجراءات حل فعالة، خوارزمية XER، لحل CPP كملكة كيميائية لإيجاد أقصى حد من الزمرات في الرسم البياني ثم أداء التعبئة المعممة المعممة باستخدام صياغة جديدة. نناقش مزايا استخدام XER على الأساليب التقليدية وتوفير الخيارات الحسابية ونتائج تطبيق هذه الطريقة إلى مجموعات بيانات ER.
In this paper, we propose a global, self- explainable solution to solve a prominent NLP problem: Entity Resolution (ER). We formu- late ER as a graph partitioning problem. Every mention of a real-world entity is represented by a node in the graph, and the pairwise sim- ilarity scores between the mentions are used to associate these nodes to exactly one clique, which represents a real-world entity in the ER domain. In this paper, we use Clique Partition- ing Problem (CPP), which is an Integer Pro- gram (IP) to formulate ER as a graph partition- ing problem and then highlight the explainable nature of this method. Since CPP is NP-Hard, we introduce an efficient solution procedure, the xER algorithm, to solve CPP as a combi- nation of finding maximal cliques in the graph and then performing generalized set packing using a novel formulation. We discuss the advantages of using xER over the traditional methods and provide the computational exper- iments and results of applying this method to ER data sets.
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