MOTIVATION Thanks to the increasing availability of drug-drug interactions (DDI) datasets and large biomedical knowledge graphs (KGs), accurate detection of adverse DDI using machine learning models becomes possible. However, it remains largely an open problem how to effectively utilize large and noisy biomedical KG for DDI detection. Due to its sheer size and amount of noise in KGs, it is often less beneficial to directly integrate KGs with other smaller but higher quality data (e.g., experimental data). Most of existing approaches ignore KGs altogether. Some tries to directly integrate KGs with other data via graph neural networks with limited success. Furthermore most previous works focus on binary DDI prediction whereas the multi-typed DDI pharmacological effect prediction is more meaningful but harder task. RESULTS To fill the gaps, we propose a new method SumGNN: knowledge summarization graph neural network, which is enabled by a subgraph extraction module that can efficiently anchor on relevant subgraphs from a KG, a self-attention based subgraph summarization scheme to generate reasoning path within the subgraph, and a multi-channel knowledge and data integration module that utilizes massive external biomedical knowledge for significantly improved multi-typed DDI predictions. SumGNN outperforms the best baseline by up to 5.54%, and performance gain is particularly significant in low data relation types. In addition, SumGNN provides interpretable prediction via the generated reasoning paths for each prediction. AVAILABILITY The code is available in the supplementary. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

SumGNN: Multi-typed Drug Interaction Prediction via Efficient Knowledge Graph Summarization.

Active learning is an important technique for low-resource sequence labeling tasks. However, current active sequence labeling methods use the queried samples alone in each iteration, which is an inefficient way of leveraging human annotations. We propose a simple but effective data augmentation method to improve the label efficiency of active sequence labeling. Our method, SeqMix, simply augments the queried samples by generating extra labeled sequences in each iteration. The key difficulty is to generate plausible sequences along with token-level labels. In SeqMix, we address this challenge by performing mixup for both sequences and token-level labels of the queried samples. Furthermore, we design a discriminator during sequence mixup, which judges whether the generated sequences are plausible or not. Our experiments on Named Entity Recognition and Event Detection tasks show that SeqMix can improve the standard active sequence labeling method by $2.27\%$--$3.75\%$ in terms of $F_1$ scores. The code and data for SeqMix can be found at this https URL

SeqMix: Augmenting Active Sequence Labeling via Sequence Mixup

Automatic construction of a taxonomy supports many applications in e-commerce, web search, and question answering. Existing taxonomy expansion or completion methods assume that new concepts have been accurately extracted and their embedding vectors learned from the text corpus. However, one critical and fundamental challenge in fixing the incompleteness of taxonomies is the incompleteness of the extracted concepts, especially for those whose names have multiple words and consequently low frequency in the corpus. To resolve the limitations of extraction-based methods, we propose GenTaxo to enhance taxonomy completion by identifying positions in existing taxonomies that need new concepts and then generating appropriate concept names. Instead of relying on the corpus for concept embeddings, GenTaxo learns the contextual embeddings from their surrounding graph-based and language-based relational information, and leverages the corpus for pre-training a concept name generator. Experimental results demonstrate that GenTaxo improves the completeness of taxonomies over existing methods.

Enhancing Taxonomy Completion with Concept Generation via Fusing Relational Representations

Recent years have witnessed a rapid proliferation of personalized mobile Apps, which poses a pressing need for user experience improvement. A promising solution is to model App usage by learning semantic-aware App usage representations which can capture the relation among time, locations and Apps. However, it is non-trivial due to the complexity, dynamics, and heterogeneity characteristics of App usage. To smooth over these obstacles and achieve the goal, we propose SA-GCN, a novel representation learning model to map Apps, location, and time units into dense embedding vectors considering spatio-temporal characteristics and unit properties simultaneously. To handle complexity and dynamics, we build an App usage graph by regarding App, time, and location units as nodes and their co-occurrence relations as edges. For heterogeneity, we develop a Graph Convolutional Network with meta path-based objective function to combine the structure of the graph and the attribute of units into the semantic-aware representations. We evaluate the performance of SA-GCN via a large-scale real-world dataset. In-depth analysis shows that SA-GCN characterizes the complex relationships among different units and recover meaningful spatio-temporal patterns. Moreover, we make use of the learned representations in App usage prediction task without post-training and achieve 8.3% of the performance gain compared with state-of-the-art baselines.

Semantic-aware Spatio-temporal App Usage Representation via Graph Convolutional Network

Automatically constructing taxonomy finds many applications in e-commerce and web search. One critical challenge is as data and business scope grow in real applications, new concepts are emerging and needed to be added to the existing taxonomy. Previous approaches focus on the taxonomy expansion, i.e. finding an appropriate hypernym concept from the taxonomy for a new query concept. In this paper, we formulate a new task, “taxonomy completion”, by discovering both the hypernym and hyponym concepts for a query. We propose Triplet Matching Network (TMN), to find the appropriate pairs for a given query concept. TMN consists of one primal scorer and multiple auxiliary scorers. These auxiliary scorers capture various fine-grained signals (e.g., query to hypernym or query to hyponym semantics), and the primal scorer makes a holistic prediction on triplet based on the internal feature representations of all auxiliary scorers. Also, an innovative channel-wise gating mechanism that retains task-specific information in concept representations is introduced to further boost model performance. Experiments on four real-world large-scale datasets show that TMN achieves the best performance on both taxonomy completion task and the previous taxonomy expansion task, outperforming existing methods.

Taxonomy Completion via Triplet Matching Network

Taxonomies are important knowledge ontologies that underpin numerous applications on a daily basis, but many taxonomies used in practice suffer from the low coverage issue We study the taxonomy expansion problem, which aims to expand existing taxonomies with new concept terms We propose a self-supervised taxonomy expansion model named STEAM, which leverages natural supervision in the existing taxonomy for expansion To generate natural self-supervision signals, STEAM samples mini-paths from the existing taxonomy, and formulates a node attachment prediction task between anchor mini-paths and query terms To solve the node attachment task, it learns feature representations for query-anchor pairs from multiple views and performs multi-view co-training for prediction Extensive experiments show that STEAM outperforms state-of-the-art methods for taxonomy expansion by 116\% in accuracy and 70\% in mean reciprocal rank on three public benchmarks The implementation of STEAM can be found at \url{this https URL}

STEAM: Self-Supervised Taxonomy Expansion with Mini-Paths

Taxonomies are important knowledge ontologies that underpin numerous applications on a daily basis, but many taxonomies used in practice suffer from the low coverage issue. We study the taxonomy expansion problem, which aims to expand existing taxonomies with new concept terms. We propose a self-supervised taxonomy expansion model named STEAM, which leverages natural supervision in the existing taxonomy for expansion. To generate natural self-supervision signals, STEAM samples mini-paths from the existing taxonomy, and formulates a node attachment prediction task between anchor mini-paths and query terms. To solve the node attachment task, it learns feature representations for query-anchor pairs from multiple views and performs multi-view co-training for prediction. Extensive experiments show that STEAM outperforms state-of-the-art methods for taxonomy expansion by 11.6% in accuracy and 7.0% in mean reciprocal rank on three public benchmarks. The code and data for STEAM can be found at https://github.com/yueyu1030/STEAM.

pdf/steam-self-supervised-taxonomy-expansion-with-mini-paths-al36726acw.pdf

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STEAM: Self-Supervised Taxonomy Expansion with Mini-Paths

STEAM: Self-Supervised Taxonomy Expansion with Mini-Paths