Unsupervised user identity linkage via factoid embedding

National Research Foundation (NRF) Singapore under International Research Centres in Singapore Funding Initiativ

Network Alignment In Heterogeneous Social Networks

Online Social Networks (OSN) have numerous applications and an ever growing user base. This has led to users being a part of multiple social networks at the same time. Identifying a similar user from one social network on another social network will give in- formation about a user’s behavior on different platforms. It further helps in community detection and link prediction tasks. The process of identifying or aligning users in multiple networks is called Network Alignment. More the information we have about the nodes / users better the results of Network Alignment. Unlike other related work in this field that use features like location, timestamp, bag of words, our proposed solution to the Network Alignment problem primarily uses information that is easily available which is the topology of the given network. We look to improve the alignment results by using more information on users like username and profile image features. Experiments are performed to compare the proposed solution in both unsupervised and supervised setting

Find me if You Can: Aligning Users in Different Social Networks

Online Social Networks allow users to share experiences with friends and relatives, make announcements, find news and jobs, and more. Several have user bases that number in the hundred of millions and even billions. Very often many users belong to multiple social networks at the same time under possibly different user names. Identifying a user from one social network on another social network gives information about a user\u27s behavior on each platform, which in turn can help companies perform graph mining tasks, such as community detection and link prediction. The process of identifying or aligning users in multiple networks is called network alignment. These similar (or same) users on different networks are called anchor nodes and the edges between them are called anchor links. The network alignment problem aims at finding these anchor links. In this work we propose two supervised algorithms and one unsupervised algorithm using thresholds. All these algorithms use local structural graph features of users and some of them use additional information about the users. We present the performance of our models in various settings using experiments based on Foursquare-Twitter and Facebook-Twitter data (User Identity Linkage Dataset). We show that our approaches perform well even when we use the neighborhood of the users only, and the accuracy improves even more given additional information about a user, such as the username and the profile image. We further show that our best approaches perform better at the HR@1 task than unsupervised and semi-supervised factoid embedding approaches considered earlier for these datasets

AD-Link: An adaptive approach for user identity linkage

National Research Foundation (NRF) Singapore under its International Research Centres in Singapore Funding Initiativ

Neural Representations of Concepts and Texts for Biomedical Information Retrieval

Information retrieval (IR) methods are an indispensable tool in the current landscape of exponentially increasing textual data, especially on the Web. A typical IR task involves fetching and ranking a set of documents (from a large corpus) in terms of relevance to a user\u27s query, which is often expressed as a short phrase. IR methods are the backbone of modern search engines where additional system-level aspects including fault tolerance, scale, user interfaces, and session maintenance are also addressed. In addition to fetching documents, modern search systems may also identify snippets within the documents that are potentially most relevant to the input query. Furthermore, current systems may also maintain preprocessed structured knowledge derived from textual data as so called knowledge graphs, so certain types of queries that are posed as questions can be parsed as such; a response can be an output of one or more named entities instead of a ranked list of documents (e.g., what diseases are associated with EGFR mutations? ). This refined setup is often termed as question answering (QA) in the IR and natural language processing (NLP) communities. In biomedicine and healthcare, specialized corpora are often at play including research articles by scientists, clinical notes generated by healthcare professionals, consumer forums for specific conditions (e.g., cancer survivors network), and clinical trial protocols (e.g., www.clinicaltrials.gov). Biomedical IR is specialized given the types of queries and the variations in the texts are different from that of general Web documents. For example, scientific articles are more formal with longer sentences but clinical notes tend to have less grammatical conformity and are rife with abbreviations. There is also a mismatch between the vocabulary of consumers and the lingo of domain experts and professionals. Queries are also different and can range from simple phrases (e.g., COVID-19 symptoms ) to more complex implicitly fielded queries (e.g., chemotherapy regimens for stage IV lung cancer patients with ALK mutations ). Hence, developing methods for different configurations (corpus, query type, user type) needs more deliberate attention in biomedical IR. Representations of documents and queries are at the core of IR methods and retrieval methodology involves coming up with these representations and matching queries with documents based on them. Traditional IR systems follow the approach of keyword based indexing of documents (the so called inverted index) and matching query phrases against the document index. It is not difficult to see that this keyword based matching ignores the semantics of texts (synonymy at the lexeme level and entailment at phrase/clause/sentence levels) and this has lead to dimensionality reduction methods such as latent semantic indexing that generally have scale-related concerns; such methods also do not address similarity at the sentence level. Since the resurgence of neural network methods in NLP, the IR field has also moved to incorporate advances in neural networks into current IR methods. This dissertation presents four specific methodological efforts toward improving biomedical IR. Neural methods always begin with dense embeddings for words and concepts to overcome the limitations of one-hot encoding in traditional NLP/IR. In the first effort, we present a new neural pre-training approach to jointly learn word and concept embeddings for downstream use in applications. In the second study, we present a joint neural model for two essential subtasks of information extraction (IE): named entity recognition (NER) and entity normalization (EN). Our method detects biomedical concept phrases in texts and links them to the corresponding semantic types and entity codes. These first two studies provide essential tools to model textual representations as compositions of both surface forms (lexical units) and high level concepts with potential downstream use in QA. In the third effort, we present a document reranking model that can help surface documents that are likely to contain answers (e.g, factoids, lists) to a question in a QA task. The model is essentially a sentence matching neural network that learns the relevance of a candidate answer sentence to the given question parametrized with a bilinear map. In the fourth effort, we present another document reranking approach that is tailored for precision medicine use-cases. It combines neural query-document matching and faceted text summarization. The main distinction of this effort from previous efforts is to pivot from a query manipulation setup to transforming candidate documents into pseudo-queries via neural text summarization. Overall, our contributions constitute nontrivial advances in biomedical IR using neural representations of concepts and texts

A Survey on Knowledge Graphs: Representation, Acquisition and Applications

Human knowledge provides a formal understanding of the world. Knowledge graphs that represent structural relations between entities have become an increasingly popular research direction towards cognition and human-level intelligence. In this survey, we provide a comprehensive review of knowledge graph covering overall research topics about 1) knowledge graph representation learning, 2) knowledge acquisition and completion, 3) temporal knowledge graph, and 4) knowledge-aware applications, and summarize recent breakthroughs and perspective directions to facilitate future research. We propose a full-view categorization and new taxonomies on these topics. Knowledge graph embedding is organized from four aspects of representation space, scoring function, encoding models, and auxiliary information. For knowledge acquisition, especially knowledge graph completion, embedding methods, path inference, and logical rule reasoning, are reviewed. We further explore several emerging topics, including meta relational learning, commonsense reasoning, and temporal knowledge graphs. To facilitate future research on knowledge graphs, we also provide a curated collection of datasets and open-source libraries on different tasks. In the end, we have a thorough outlook on several promising research directions