Recent knowledge graph (KG) embedding methods explore parameter-efficient representations for large-scale KGs. These techniques learn entity representation using a fixed size vocabulary. Such a vocabulary consists of all the relations and a small subset of the full entity set, referred to as anchors. An entity is hence expressed as a function of reachable anchors and immediate relations. The performance of these methods is, therefore, largely dependent on the entity tokenization strategy. Especially in inductive settings, the representation capacity of these embeddings is limited due to the absence of anchor entities, as unseen entities have no connection with training graph entities. In this work, we propose a novel entity tokenization strategy that tokenizes an entity into a set of anchors based on relation similarity and relational paths. Our model MH-RARe overcomes the challenge of unseen entities not being directly connected to the anchors by selecting informative anchors from the training graph using relation similarity. Experiment results show that our model outperforms the baselines on multiple datasets for inductive knowledge graph completion task, attaining upto 5% improvement, while maintaining parameter efficiency.

Mobile devices, with their rapidly growing usage, have turned into rich sources of user information, holding critical insights for betterment of user experience and personalization. Creating, receiving and storing important information in the form of unstructured text has become a part and parcel of daily routine of users. From purchase deliveries in Short Message Service (SMS) or Notifications, to event booking details in Calendar applications, mobile devices serve as a portal for understanding user interests, behaviours and activities through information extraction. In this paper, we address the challenge of on-device extraction of user information from unstructured data in natural language from heterogeneous sources like messages, notification, calendar etc. The issue of privacy concern is effectively eliminated by the on-device nature of the proposed solution. Our proposed solution consists of 3 components – A Na ̈ıve-Bayes based classifier for domain identification, a Dual Character andWord based Bidirectional Long Short Term Memory (Bi-LSTM) and Conditional Random Field (CRF) model for attribute extraction and a rule-based Entity Linker. Our solution achieved a 93.29% F1 score on five domains (shopping, travel, event, service and personal). Since on-device deployment has memory and latency constraints, we ensure minimal model size and optimal inference latency. To demonstrate the efficacy of our approach, we have experimented on CoNLL- 2003 dataset and achieved comparable performance to existing benchmark results.