Comparing Attributional and Relational Similarity as a Means to Identify Clinically Relevant Drug-gene Relationships

In emerging domains, such as precision oncology, knowledge extracted from explicit assertions may be insufficient to identify relationships of interest. One solution to this problem involves drawing inference on the basis of similarity. Computational methods have been developed to estimate the semantic similarity and relatedness between terms and relationships that are distributed across corpora of literature such as Medline abstracts and other forms of human readable text. Most research on distributional similarity has focused on the notion of attributional similarity, which estimates the similarity between entities based on the contexts in which they occur across a large corpus. A relatively under-researched area concerns relational similarity, in which the similarity between pairs of entities is estimated from the contexts in which these entity pairs occur together. While it seems intuitive that models capturing the structure of the relationships between entities might mediate the identification of biologically important relationships, there is to date no comparison of the relative utility of attributional and relational models for this purpose. In this research, I compare the performance of a range of relational and attributional similarity methods, on the task of identifying drugs that may be therapeutically useful in the context of particular aberrant genes, as identified by a team of human experts. My hypothesis is that relational similarity will be of greater utility than attributional similarity as a means to identify biological relationships that may provide answers to clinical questions, (such as “which drugs INHIBIT gene x”?) in the context of rapidly evolving domains. My results show that models based on relational similarity outperformed models based on attributional similarity on this task. As the methods explained in this research can be applied to identify any sort of relationship for which cue pairs exist, my results suggest that relational similarity may be a suitable approach to apply to other biomedical problems. Furthermore, I found models based on neural word embeddings (NWE) to be particularly useful for this task, given their higher performance than Random Indexing-based models, and significantly less computational effort needed to create them. NWE methods (such as those produced by the popular word2vec tool) are a relatively recent development in the domain of distributional semantics, and are considered by many as the state-of-the-art when it comes to semantic language modeling. However, their application in identifying biologically important relationships from Medline in general, and specifically, in the domain of precision oncology has not been well studied. The results of this research can guide the design and implementation of biomedical question answering and other relationship extraction applications for precision medicine, precision oncology and other similar domains, where there is rapid emergence of novel knowledge. The methods developed and evaluated in this project can help NLP applications provide more accurate results by leveraging corpus based methods that are by design scalable and robust

Legal Reasoning

For more than a century, lawyers have written about legal reasoning, and the flow of books and articles describing, analyzing, and reformulating the topic continues unabated. The volume and persistence of this unrelenting discussion (Simon, 1998, p. 4) suggests that there is no solid consensus about what legal reasoning is. Legal scholars have a tenacious intuition - or at least a strong hope - that legal reasoning is distinctive, that it is not the same as logic, or scientific reasoning, or ordinary decision making, and there have been dozens of attempts to describe what it is that sets it apart from these other forms of thinking. These attempts generate criticism, the critics devise new formulations that generate further criticism, and the process continues. In this chapter, I describe the primary forms of legal reasoning, the most important schools of thought about legal reasoning, and some of the major differences between legal reasoning and scientific reasoning

Legal Reasoning

For more than a century, lawyers have written about legal reasoning, and the flow of books and articles describing, analyzing, and reformulating the topic continues unabated. The volume and persistence of this unrelenting discussion (Simon, 1998, p. 4) suggests that there is no solid consensus about what legal reasoning is. Legal scholars have a tenacious intuition - or at least a strong hope - that legal reasoning is distinctive, that it is not the same as logic, or scientific reasoning, or ordinary decision making, and there have been dozens of attempts to describe what it is that sets it apart from these other forms of thinking. These attempts generate criticism, the critics devise new formulations that generate further criticism, and the process continues. In this chapter, I describe the primary forms of legal reasoning, the most important schools of thought about legal reasoning, and some of the major differences between legal reasoning and scientific reasoning

A Study on Learning Representations for Relations Between Words

Reasoning about relations between words or entities plays an important role in human cognition. It is thus essential for a computational system which processes human languages to be able to understand the semantics of relations to simulate human intelligence. Automatic relation learning provides valuable information for many natural language processing tasks including ontology creation, question answering and machine translation, to name a few. This need brings us to the topic of this thesis where the main goal is to explore multiple resources and methodologies to effectively represent relations between words. How to effectively represent semantic relations between words remains a problem that is underexplored. A line of research makes use of relational patterns, which are the linguistic contexts in which two words co-occur in a corpus to infer a relation between them (e.g., X leads to Y). This approach suffers from data sparseness because not every related word-pair co-occurs even in a large corpus. In contrast, prior work on learning word embeddings have found that certain relations between words could be captured by applying linear arithmetic operators on the corresponding pre-trained word embeddings. Specifically, it has been shown that the vector offset (expressed as PairDiff) from one word to the other in a pair encodes the relation that holds between them, if any. Such a compositional method addresses the data sparseness by inferring a relation from constituent words in a word-pair and obviates the need of relational patterns. This thesis investigates the best way to compose word embeddings to represent relational instances. A systematic comparison is carried out for unsupervised operators, which in general reveals the superiority of the PairDiff operator on multiple word embedding models and benchmark datasets. Despite the empirical success, no theoretical analysis has been conducted so far explaining why and under what conditions PairDiff is optimal. To this end, a theoretical analysis is conducted for the generalised bilinear operators that can be used to measure the relational distance between two word-pairs. The main conclusion is that, under certain assumptions, the bilinear operator can be simplified to a linear form, where the widely used PairDiff operator is a special case. Multiple recent works raised concerns about existing unsupervised operators for inferring relations from pre-trained word embeddings. Thus, the question of whether it is possible to learn better parametrised relational compositional operators is addressed in this thesis. A supervised relation representation operator is proposed using a non-linear neural network that performs relation prediction. The evaluation on two benchmark datasets reveals that the penultimate layer of the trained neural network-based relational predictor acts as a good representation for the relations between words. Because we believe that both relational patterns and word embeddings provide complementary information to learn relations, a self-supervised context-guided relation embedding method that is trained on the two sources of information has been proposed. Experimentally, incorporating relational contexts shows improvement in the performance of a compositional operator for representing unseen word-pairs. Besides unstructured text corpora, knowledge graphs provide another source for relational facts in the form of nodes (i.e., entities) connected by edges (i.e., relations). Knowledge graphs are employed widely in natural language processing applications such as question answering and dialogue systems. Embedding entities and relations in a graph have shown impressive results for inferring previously unseen relations between entities. This thesis contributes to developing a theoretical model to infer a relationship between the connections in the graph and the embeddings of entities and relations. Learning graph embeddings that satisfy the proven theorem demonstrates efficient performance compared to existing heuristically derived graph embedding methods. As graph embedding methods generate representations for only existing relation types, a relation composition task is proposed in the thesis to tackle this limitation