text: Knowledge base construction is crucial for summarising, understanding and inferring relationships between biomedical entities. However, for many practical applications such as drug discovery, the scarcity of relevant facts (e.g. gene X is therapeutic target for disease Y) severely limits a domain expert's ability to create a usable knowledge base, either directly or by training a relation extraction model. In this paper, we present a simple and effective method of extracting new facts with a pre-specified binary relationship type from the biomedical literature, without requiring any training data or hand-crafted rules. Our system discovers, ranks and presents the most salient patterns to domain experts in an interpretable form. By marking patterns as compatible with the desired relationship type, experts indirectly batch-annotate candidate pairs whose relationship is expressed with such patterns in the literature. Even with a complete absence of seed data, experts are able to discover thousands of high-quality pairs with the desired relationship within minutes. When a small number of relevant pairs do exist - even when their relationship is more general (e.g. gene X is biologically associated with disease Y) than the relationship of interest - our system leverages them in order to i) learn a better ranking of the patterns to be annotated or ii) generate weakly labelled pairs in a fully automated manner. We evaluate our method both intrinsically and via a downstream knowledge base completion task, and show that it is an effective way of constructing knowledge bases when few or no relevant facts are already available.
  @cite_5: 
  @cite_1: The World Wide Web is a vast resource for information. At the same time it is extremely distributed. A particular type of data such as restaurant lists may be scattered across thousands of independent information sources in many different formats. In this paper, we consider the problem of extracting a relation for such a data type from all of these sources automatically. We present a technique which exploits the duality between sets of patterns and relations to grow the target relation starting from a small sample. To test our technique we use it to extract a relation of (author,title) pairs from the World Wide Web.
  @cite_3: This paper presents a novel approach to the semi-supervised learning of Information Extraction patterns. The method makes use of more complex patterns than previous approaches and determines their similarity using a measure inspired by recent work using kernel methods (Culotta and Sorensen, 2004). Experiments show that the proposed similarity measure outperforms a previously reported measure based on cosine similarity when used to perform binary relation extraction.
  @cite_2: Text documents often contain valuable structured data that is hidden Yin regular English sentences. This data is best exploited infavailable as arelational table that we could use for answering precise queries or running data mining tasks.We explore a technique for extracting such tables from document collections that requires only a handful of training examples from users. These examples are used to generate extraction patterns, that in turn result in new tuples being extracted from the document collection.We build on this idea and present our Snowball system. Snowball introduces novel strategies for generating patterns and extracting tuples from plain-text documents.At each iteration of the extraction process, Snowball evaluates the quality of these patterns and tuples without human intervention,and keeps only the most reliable ones for the next iteration. In this paper we also develop a scalable evaluation methodology and metrics for our task, and present a thorough experimental evaluation of Snowball and comparable techniques over a collection of more than 300,000 newspaper documents.
summary: The idea of extracting entity pairs by discovering textual patterns dates back to early work on bootstrapping for relation extraction with the DIPRE system @cite_1 . This system was designed to find co-occurrences of seed entity pairs of a known relationship type inside unlabelled text, then extract simple patterns (exact string matches) from these occurrences and use them to discover new entity pairs. introduced a pattern evaluation methodology based on the precision of a pattern on the set of entity pairs which had already been discovered; they also used the dot product between word vectors instead of an exact string match to allow for slight variations in text. Later work @cite_3 @cite_2 @cite_5 has proposed more sophisticated pattern extraction methods (based on dependency graphs or kernel methods on word vectors) and different pattern evaluation frameworks (document relevance scores).