pwc-archive/papers-with-abstracts huggingface dataset:
Applying Citizen Science to Gene, Drug, Disease Relationship Extraction from Biomedical Abstracts
Authors: Ginger Tsueng, Max Nanis, Jennifer T. Fouquier, Michael Mayers, Benjamin M. Good, Andrew I Su
Date: 2018-11-17 00:00:00
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER)

Biomedical literature is growing at a rate that outpaces our ability to harness the knowledge contained therein. In order to mine valuable inferences from the large volume of literature, many researchers have turned to information extraction algorithms to harvest information in biomedical texts. Information extraction is usually accomplished via a combination of manual expert curation and computational methods. Advances in computational methods usually depends on the generation of gold standards by a limited number of expert curators. This process can be time consuming and represents an area of biomedical research that is ripe for exploration with citizen science. Citizen scientists have been previously found to be willing and capable of performing named entity recognition of disease mentions in biomedical abstracts, but it was uncertain whether or not the same could be said of relationship extraction. Relationship extraction requires training on identifying named entities as well as a deeper understanding of how different entity types can relate to one another. Here, we used the web-based application Mark2Cure (https://mark2cure.org) to demonstrate that citizen scientists can perform relationship extraction and confirm the importance of accurate named entity recognition on this task. We also discuss opportunities for future improvement of this system, as well as the potential synergies between citizen science, manual biocuration, and natural language processing.
Integrating User Feedback under Identity Uncertainty in Knowledge Base Construction
Authors: Ari Kobren, Nicholas Monath, Andrew McCallum
Date: 2018-11-17 00:00:00
Proceeding: AKBC 2019
Tasks: Entity Resolution, Knowledge Base Construction

Users have tremendous potential to aid in the construction and maintenance of knowledges bases (KBs) through the contribution of feedback that identifies incorrect and missing entity attributes and relations. However, as new data is added to the KB, the KB entities, which are constructed by running entity resolution (ER), can change, rendering the intended targets of user feedback unknown–a problem we term identity uncertainty. In this work, we present a framework for integrating user feedback into KBs in the presence of identity uncertainty. Our approach is based on having user feedback participate alongside mentions in ER. We propose a specific representation of user feedback as feedback mentions and introduce a new online algorithm for integrating these mentions into an existing KB. In experiments, we demonstrate that our proposed approach outperforms the baselines in 70% of experimental conditions.
Synonym Expansion for Large Shopping Taxonomies
Authors: Adrian Boteanu, Adam Kiezun, Shay Artzi
Date: 2018-11-17 00:00:00
Proceeding: AKBC 2019
Tasks: Transfer Learning

We present an approach for expanding taxonomies with synonyms, or aliases. We target large shopping taxonomies, with thousands of nodes. A comprehensive set of entity aliases is an important component of identifying entities in unstructured text such as product reviews or search queries. Our method consists of two stages: we generate synonym candidates from WordNet and shopping search queries, then use a binary classiﬁer to ﬁlter candidates. We process taxonomies with thousands of synonyms in order to generate over 90,000 synonyms. We show that using the taxonomy to derive contextual features improves classiﬁcation performance over using features from the target node alone.We show that our approach has potential for transfer learning between diﬀerent taxonomy domains, which reduces the need to collect training data for new taxonomies.
Discriminative Candidate Generation for Medical Concept Linking
Authors: Elliot Schumacher, Mark Dredze
Date: 2018-11-17 00:00:00
Proceeding: AKBC 2019

Linking mentions of medical concepts in a clinical note to a concept in an ontology enables a variety of tasks that rely on understanding the content of a medical record, such as identifying patient populations and decision support.   Medical concept linking can be formulated as a two-step task; 1) candidate generator, which selects likely candidates from the ontology for the given mention, and 2) a ranker, which orders the candidates based on a set of features to find the best one.In this paper, we propose a candidate generation system based on the DiscK framework [Chen andVan Durme, 2017].  Our system produces a candidate list with both high coverage and a rankingthat is a useful starting point for the second step of the linking process. we integrate our candidate selection process into a current linking system, DNorm [Leaman et al., 2013]. The resulting system achieves similar accuracy paired with with a gain in efficiency due to a large reduction in the number of potential candidates considered.
Learning Numerical Attributes in Knowledge Bases
Authors: Bhushan Kotnis, Alberto García-Durán
Date: 2018-11-17 00:00:00
Proceeding: AKBC 2019
Tasks: Attribute, Link Prediction

Knowledge bases (KB) are often represented as a collection of facts in the form (HEAD, PREDICATE, TAIL), where HEAD and TAIL are entities while PREDICATE is a binary relationship that links the two. It is a well-known fact that knowledge bases are far from complete, and hence the plethora of research on KB completion methods, specifically on link prediction. However, though frequently ignored, these repositories also contain numerical facts. Numerical facts link entities to numerical values via numerical predicates; e.g., (PARIS, LATITUDE, 48.8). Likewise, numerical facts also suffer from the incompleteness problem. To address this issue, we introduce the numerical attribute prediction problem. This problem involves a new type of query where the relationship is a numerical predicate. Consequently, and contrary to link prediction, the answer to this query is a numerical value. We argue that the numerical values associated with entities explain, to some extent, the relational structure of the knowledge base. Therefore, we leverage knowledge base embedding methods to learn representations that are useful predictors for the numerical attributes. An extensive set of experiments on benchmark versions of FREEBASE and YAGO show that our approaches largely outperform sensible baselines. We make the datasets available under a permissive BSD-3 license.
Scaling Hierarchical Coreference with Homomorphic Compression
Authors: Michael Wick, Swetasudha Panda, Joseph Tassarotti, Jean-Baptiste Tristan
Date: 2018-11-17 00:00:00
Proceeding: AKBC 2019

Locality sensitive hashing schemes such as \simhash provide compact representations of multisets from which similarity can be estimated. However, in certain applications, we need to estimate the similarity of dynamically changing sets.  In this case, we need the representation to be a homomorphism so that the hash of unions and differences of sets can be computed directly from the hashes of operands.  We propose two representations that have this property for cosine similarity (an extension of \simhash and angle-preserving random projections), and make substantial progress on a third representation for Jaccard similarity (an extension of \minhash). We employ these hashes to compress the sufficient statistics of a conditional random field (CRF) coreference model and study how this compression affects our ability to compute similarities as entities are split and merged during inference. \cut{We study these hashes in a conditional random field (CRF) hierarchical coreference model in order to compute the similarity of entities as they are merged and split during inference.} We also provide novel statistical analysis of \simhash to help justify it as an estimator inside a CRF, showing that the bias and variance reduce quickly with the number of bits. On a problem of author coreference, we find that our \simhash scheme allows scaling the hierarchical coreference algorithm by an order of magnitude without degrading its statistical performance or the model's coreference accuracy, as long as we employ at least 128 or 256 bits.  Angle-preserving random projections further improve the coreference quality, potentially allowing even fewer dimensions to be used.
Learning Relation Representations from Word Representations
Authors: Huda Hakami, Danushka Bollegala
Date: 2018-11-17 00:00:00
Proceeding: AKBC 2019
Tasks: Knowledge Base Completion, Relation, Relation Prediction, Word Embeddings

Identifying the relations that connect words is an important step towards understanding human languages and is useful for various NLP tasks such as knowledge base completion and analogical reasoning. Simple unsupervised operators such as vector offset between two-word embeddings have shown to recover some specific relationships between those words, if any. Despite this, how to accurately learn generic relation representations from word representations remains unclear. We model relation representation as a supervised learning problem and learn parametrised operators that map pre-trained word embeddings to relation representations. We propose a method for learning relation representations using a feed-forward neural network that performs relation prediction. Our evaluations on two benchmark datasets reveal that the penultimate layer of the trained neural network-based relational predictor acts as a good representation for the relations between words.
Joint Learning of Hierarchical Word Embeddings from a Corpus and a Taxonomy
Authors: Mohammed Alsuhaibani, Takanori Maehara, Danushka Bollegala
Date: 2018-11-17 00:00:00
Proceeding: AKBC 2019
Tasks: Word Embeddings

Identifying the hypernym relations that hold between words is a fundamental task in NLP. Word embedding methods have recently shown some capability to encode hypernymy. However, such methods tend not to explicitly encode the hypernym hierarchy that exists between words. In this paper, we propose a method to learn a hierarchical word embedding in a speciﬁc order to capture the hypernymy. To learn the word embeddings, the proposed method considers not only the hypernym relations that exists between words on a taxonomy, but also their contextual information in a large text corpus. The experimental results on a supervised hypernymy detection and a newly-proposed hierarchical path completion tasks show the ability of the proposed method to encode the hierarchy. Moreover, the proposed method outperforms previously proposed methods for learning word and hypernym-speciﬁc word embeddings on multiple benchmarks.
Semi-supervised Ensemble Learning with Weak Supervision for Biomedical Relationship Extraction
Authors: Antonios Minas Krasakis, Evangelos Kanoulas, George Tsatsaronis
Date: 2018-11-17 00:00:00
Proceeding: AKBC 2019
Tasks: Drug Discovery, Ensemble Learning, Natural Language Understanding, Relation Extraction

Natural language understanding research has recently shifted towards complex Machine Learning and Deep Learning algorithms. Such models often outperform their simpler counterparts significantly. However, their performance relies on the availability of large amounts of labeled data, which are rarely available. To tackle this problem, we propose a methodology for extending training datasets to arbitrarily big sizes and training complex, data-hungry models using weak supervision. We apply this methodology on biomedical relation extraction, a task where training datasets are excessively time-consuming and expensive to create, yet has a major impact on downstream applications such as drug discovery. We demonstrate in two small-scale controlled experiments that our method consistently enhances the performance of an LSTM network, with performance improvements comparable to hand-labeled training data. Finally, we discuss the optimal setting for applying weak supervision using this methodology.
Answering Science Exam Questions Using Query Reformulation with Background Knowledge
Authors: Ryan Musa, Xiaoyan Wang, Achille Fokoue, Nicholas Mattei, Maria Chang, Pavan Kapanipathi, Bassem Makni, Kartik Talamadupula, Michael Witbrock
Date: 2018-11-17 00:00:00
Proceeding: AKBC 2019
Tasks: ARC, Information Retrieval, Multiple-choice, Natural Language Inference, Open-Domain Question Answering, Question Answering, Retrieval

Open-domain question answering (QA) is an important problem in AI and NLP that is emerging as a bellwether for progress on the generalizability of AI methods and techniques. Much of the progress in open-domain QA systems has been realized through advances in information retrieval methods and corpus construction. In this paper, we focus on the recently introduced ARC Challenge dataset, which contains 2,590 multiple choice questions authored for grade-school science exams. These questions are selected to be the most challenging for current QA systems, and current state of the art performance is only slightly better than random chance. We present a system that reformulates a given question into queries that are used to retrieve supporting text from a large corpus of science-related text. Our rewriter is able to incorporate background knowledge from ConceptNet and -- in tandem with a generic textual entailment system trained on SciTail that identifies support in the retrieved results -- outperforms several strong baselines on the end-to-end QA task despite only being trained to identify essential terms in the original source question. We use a generalizable decision methodology over the retrieved evidence and answer candidates to select the best answer. By combining query reformulation, background knowledge, and textual entailment our system is able to outperform several strong baselines on the ARC dataset.
NormCo: Deep Disease Normalization for Biomedical Knowledge Base Construction
Authors: Dustin Wright, Yannis Katsis, Raghav Mehta, Chun-Nan Hsu
Date: 2018-11-17 00:00:00
Proceeding: AKBC 2019
Tasks: Knowledge Base Construction, Word Embeddings

Biomedical knowledge bases are crucial in modern data-driven biomedical sciences, but auto-mated biomedical knowledge base construction remains challenging. In this paper, we consider the problem of disease entity normalization, an essential task in constructing a biomedical knowledge base.  We present NormCo, a deep coherence model which considers the semantics of an entity mention, as well as the topical coherence of the mentions within a single document. NormCo mod-els entity mentions using a simple semantic model which composes phrase representations from word embeddings, and treats coherence as a disease concept co-mention sequence using an RNN rather than modeling the joint probability of all concepts in a document, which requires NP-hard inference.  To overcome the issue of data sparsity, we used distantly supervised data and synthetic data generated from priors derived from the BioASQ dataset.  Our experimental results show thatNormCo outperforms state-of-the-art baseline methods on two disease normalization corpora in terms of (1) prediction quality and (2) efficiency, and is at least as performant in terms of accuracy and F1 score on tagged documents.
Scalable Rule Learning in Probabilistic Knowledge Bases
Authors: Arcchit Jain, Tal Friedman, Ondrej Kuzelka, Guy Van Den Broeck, Luc De Raedt
Date: 2018-11-17 00:00:00
Proceeding: AKBC 2019

Knowledge Bases (KBs) are becoming increasingly large, sparse and probabilistic. These KBs are typically used to perform query inferences and rule mining. But their efficacy is only as high as their completeness. Efficiently utilizing incomplete KBs remains a major challenge as the current KB completion techniques either do not take into account the inherent uncertainty associated with each KB tuple or do not scale to large KBs.

Probabilistic rule learning not only considers the probability of every KB tuple but also tackles the problem of KB completion in an explainable way. For any given probabilistic KB, it learns probabilistic first-order rules from its relations to identify interesting patterns. But, the current probabilistic rule learning techniques perform grounding to do probabilistic inference for evaluation of candidate rules. It does not scale well to large KBs as the time complexity of inference using grounding is exponential over the size of the KB. In this paper, we present SafeLearner -- a scalable solution to probabilistic KB completion that performs probabilistic rule learning using lifted probabilistic inference -- as faster approach instead of grounding. 

We compared SafeLearner to the state-of-the-art probabilistic rule learner ProbFOIL+ and to its deterministic contemporary AMIE+ on standard probabilistic KBs of NELL (Never-Ending Language Learner) and Yago. Our results demonstrate that SafeLearner scales as good as AMIE+ when learning simple rules and is also significantly faster than ProbFOIL+.
Alexandria: Unsupervised High-Precision Knowledge Base Construction using a Probabilistic Program
Authors: John Winn, John Guiver, Sam Webster, Yordan Zaykov, Martin Kukla, Dany Fabian
Date: 2018-11-17 00:00:00
Proceeding: AKBC 2019
Tasks: Knowledge Base Construction, Vocal Bursts Intensity Prediction

Creating a knowledge base that is accurate, up-to-date and complete remains a significant challenge despite substantial efforts in automated knowledge base construction.  In this paper, we present Alexandria -- a system for unsupervised, high-precision knowledge base construction. Alexandria uses a probabilistic program to define a process of converting knowledge base facts into unstructured text.  Using probabilistic inference, we can invert this program and so retrieve facts, schemas and entities from web text. The use of a probabilistic program allows uncertainty in the text to be propagated through to the retrieved facts, which increases accuracy and helps merge facts from multiple sources. Because Alexandria does not require labelled training data, knowledge bases can be constructed with the minimum of manual input. We demonstrate this by constructing a high precision (typically 97\%+) knowledge base for people from a single seed fact.
OxKBC: Outcome Explanation for Factorization Based Knowledge Base Completion
Authors: Yatin Nandwani, Ankesh Gupta, Aman Agrawal, Mayank Singh Chauhan, Parag Singla, Mausam
Date: 2020-02-14 00:00:00
Proceeding: AKBC 2020 6
Tasks: Automated Theorem Proving, Knowledge Base Completion

State-of-the-art models for Knowledge Base Completion (KBC) are based on tensor factorization (TF), e.g, DistMult, ComplEx. While they produce good results, they cannot expose any rationale behind their predictions, potentially reducing the trust of a user in the model. Previous works have explored creating an inherently explainable model, e.g. Neural Theorem Proving (NTP), DeepPath, MINERVA, but explainability comes at the cost of performance. Others have tried to create an auxiliary explainable model having high fidelity with the underlying TF model, but unfortunately, they do not scale on large KBs such as FB15k and YAGO. In this work, we propose OxKBC -- an Outcome eXplanation engine for KBC, which provides a post-hoc explanation for every triple inferred by an (uninterpretable) factorization based model. It first augments the underlying Knowledge Graph by introducing weighted edges between entities based on their similarity given by the underlying model. In the augmented graph, it defines a notion of human-understandable explanation paths along with a language to generate them. Depending on the edges, the paths are aggregated into second-order templates for further selection. The best template with its grounding is then selected by a neural selection module that is trained with minimal supervision by a novel loss function. Experiments over Mechanical Turk demonstrate that users find our explanations more trustworthy compared to rule mining.
Predicting Institution Hierarchies with Set-based Models
Authors: Derek Tam, Nicholas Monath, Ari Kobren, Andrew McCallum
Date: 2020-02-14 00:00:00
Proceeding: AKBC 2020 6

The hierarchical structure of research organizations plays a pivotal role in science of science research as well as in tools that track the research achievements and output. However, this structure is not consistently documented for all institutions in the world, motivating the need for automated construction methods. In this paper, we present a new task and model for predicting sub-institution/super-institution relationships based on their string names. The crux of our model is that it leverages learned, permutation invariant representations of various token subsets of institution name strings. Our model outperforms or matches non-set-based models and baselines. We also create a dataset for training and evaluating models for this task based on the publicly available relationships in the Global Research Identifier Database.
Contrastive Entity Linkage: Mining Variational Attributes from Large Catalogs for Entity Linkage
Authors: Varun Embar, Bunyamin Sisman, Hao Wei, Xin Luna Dong, Christos Faloutsos, Lise Getoor
Date: 2020-02-14 00:00:00
Proceeding: AKBC 2020 6
Tasks: Data Integration

Presence of near identical, but distinct, entities called entity variations makes the task of data integration challenging. For example, in the domain of grocery products, variations share the same value for attributes such as brand, manufacturer and product line, but differ in other attributes, called variational attributes, such as package size and color. Identifying variations across data sources is an important task in itself and is crucial for identifying duplicates. However, this task is challenging as the variational attributes are often present as a part of unstructured text and are domain dependent. In this work, we propose our approach, Contrastive entity linkage, to identify both entity pairs that are the same and pairs that are variations of each other. We propose a novel unsupervised approach, VarSpot, to mine domain-dependent variational attributes present in unstructured text. The proposed approach reasons about both similarities and differences between entities and can easily scale to large sources containing millions of entities. We show the generality of our approach by performing experimental evaluation on three different domains. Our approach significantly outperforms state-of-the-art learning-based and rule-based entity linkage systems by up to 4% F1 score when identifying duplicates, and up to 41% when identifying entity variations.