text: Identifying relationships between concepts is a key aspect of scientific knowledge synthesis. Finding these links often requires a researcher to laboriously search through scien- tific papers and databases, as the size of these resources grows ever larger. In this paper we describe how distributional semantics can be used to unify structured knowledge graphs with unstructured text to predict new relationships between medical concepts, using a probabilistic generative model. Our approach is also designed to ameliorate data sparsity and scarcity issues in the medical domain, which make language modelling more challenging. Specifically, we integrate the medical relational database (SemMedDB) with text from electronic health records (EHRs) to perform knowledge graph completion. We further demonstrate the ability of our model to predict relationships between tokens not appearing in the relational database.
  @cite_4: A goal of statistical language modeling is to learn the joint probability function of sequences of words in a language. This is intrinsically difficult because of the curse of dimensionality: a word sequence on which the model will be tested is likely to be different from all the word sequences seen during training. Traditional but very successful approaches based on n-grams obtain generalization by concatenating very short overlapping sequences seen in the training set. We propose to fight the curse of dimensionality by learning a distributed representation for words which allows each training sentence to inform the model about an exponential number of semantically neighboring sentences. The model learns simultaneously (1) a distributed representation for each word along with (2) the probability function for word sequences, expressed in terms of these representations. Generalization is obtained because a sequence of words that has never been seen before gets high probability if it is made of words that are similar (in the sense of having a nearby representation) to words forming an already seen sentence. Training such large models (with millions of parameters) within a reasonable time is itself a significant challenge. We report on experiments using neural networks for the probability function, showing on two text corpora that the proposed approach significantly improves on state-of-the-art n-gram models, and that the proposed approach allows to take advantage of longer contexts.
  @cite_8: Neural language models are a powerful tool to embed words into semantic vector spaces. However, learning such models generally relies on the availability of abundant and diverse training examples. In highly specialised domains this requirement may not be met due to difficulties in obtaining a large corpus, or the limited range of expression in average use. Such domains may encode prior knowledge about entities in a knowledge base or ontology. We propose a generative model which integrates evidence from diverse data sources, enabling the sharing of semantic information. We achieve this by generalising the concept of co-occurrence from distributional semantics to include other relationships between entities or words, which we model as affine transformations on the embedding space. We demonstrate the effectiveness of this approach by outperforming recent models on a link prediction task and demonstrating its ability to profit from partially or fully unobserved data training labels. We further demonstrate the usefulness of learning from different data sources with overlapping vocabularies.
  @cite_9: Distributional approaches to meaning acquisition utilize distributional properties of linguistic entities as the building blocks of semantics. In doing so, they rely fundamentally on a set of assumptions about the nature of language and meaning referred to as the distributional hypothesis. This hypothesis is often stated in terms like “words which are similar in meaning occur in similar contexts” (Rubenstein & Goodenough, 1965); “words with similar meanings will occur with similar neighbors if enough text material is available” (Schutze & Pedersen, 1995); “a representation that captures much of how words are used in natural context will capture much of what we mean by meaning” (Landauer & Dumais, 1997); and “words that occur in the same contexts tend to have similar meanings” (Pantel, 2005), just to quote a few representative examples. The general idea behind the distributional hypothesis seems clear enough: there is a correlation between distributional similarity and meaning similarity, which allows us to utilize the former in order to estimate the latter. However, one can pose two very basic questions concerning the distributional hypothesis. The first is what kind of distributional properties we should look for, and what — if any — the differences are between different kinds of distributional properties. Looking at algorithms for distributional meaning acquisition we can discern two distinct approaches. The first is to build distributional profiles for words based on which other words surround them, as exemplified by Schutze (1992) and the Hyperspace Analogue to Language (HAL) model (Lund, Burgess, & Atchley, 1995). The second is to build distributional profiles based on in which text regions words occur, as exemplified by the Latent Semantic Analysis (LSA) model (Landauer & Dumais, 1997). These approaches are often treated as functionally equivalent when it comes to representing meaning similarities, despite the fact that they are based on different types of distributional raw materials. The second question is in what sense it is meaning that is conveyed by distributional patterns. Proponents of distributional methods often seem comfortable to ascribe meaning to distributional representations without explaining
  @cite_19: We consider the problem of embedding entities and relationships of multi-relational data in low-dimensional vector spaces. Our objective is to propose a canonical model which is easy to train, contains a reduced number of parameters and can scale up to very large databases. Hence, we propose TransE, a method which models relationships by interpreting them as translations operating on the low-dimensional embeddings of the entities. Despite its simplicity, this assumption proves to be powerful since extensive experiments show that TransE significantly outperforms state-of-the-art methods in link prediction on two knowledge bases. Besides, it can be successfully trained on a large scale data set with 1M entities, 25k relationships and more than 17M training samples.
  @cite_16: This paper proposes a novel approach for relation extraction from free text which is trained to jointly use information from the text and from existing knowledge. Our model is based on scoring functions that operate by learning low-dimensional embeddings of words, entities and relationships from a knowledge base. We empirically show on New York Times articles aligned with Freebase relations that our approach is able to efficiently use the extra information provided by a large subset of Freebase data (4M entities, 23k relationships) to improve over methods that rely on text features alone.
  @cite_10: The recently introduced continuous Skip-gram model is an efficient method for learning high-quality distributed vector representations that capture a large number of precise syntactic and semantic word relationships. In this paper we present several extensions that improve both the quality of the vectors and the training speed. By subsampling of the frequent words we obtain significant speedup and also learn more regular word representations. We also describe a simple alternative to the hierarchical softmax called negative sampling. An inherent limitation of word representations is their indifference to word order and their inability to represent idiomatic phrases. For example, the meanings of "Canada" and "Air" cannot be easily combined to obtain "Air Canada". Motivated by this example, we present a simple method for finding phrases in text, and show that learning good vector representations for millions of phrases is possible.
  @cite_11: Knowledge bases are an important resource for question answering and other tasks but often suffer from incompleteness and lack of ability to reason over their discrete entities and relationships. In this paper we introduce an expressive neural tensor network suitable for reasoning over relationships between two entities. Previous work represented entities as either discrete atomic units or with a single entity vector representation. We show that performance can be improved when entities are represented as an average of their constituting word vectors. This allows sharing of statistical strength between, for instance, facts involving the "Sumatran tiger" and "Bengal tiger." Lastly, we demonstrate that all models improve when these word vectors are initialized with vectors learned from unsupervised large corpora. We assess the model by considering the problem of predicting additional true relations between entities given a subset of the knowledge base. Our model outperforms previous models and can classify unseen relationships in WordNet and FreeBase with an accuracy of 86.2 and 90.0 , respectively.
summary: Neural language models @cite_4 are an approach to learning embeddings which use a word's representation to its surrounding context. This relies on the fact that words with similar meanings have similar contexts (the distributional hypothesis of language @cite_9 ), which forces their representations to be similar. Intriguingly, it was observed @cite_10 @cite_19 that the geometry of the resulting space preserved between terms. An example is a consistent offset vector existing between Berlin' and Germany', and Dublin' and Ireland', seemingly representing the relationship capital city of country . This property has been exploited to perform knowledge-base completion, for example @cite_19 @cite_11 @cite_16 , however these approaches have restricted their attention to edge-discovery a knowledge graph. To such a graph we therefore developed a model @cite_8 which can combine structured and unstructured data sources while explicitly modelling the types of relationships present in the structured data.