Recently, there has been much effort in making databases for molecular biology more accessible and interoperable. However, information in text form, such as MEDLINE records, remains a greatly underutilized source of biological information. We have begun a research effort aimed at automatically mapping information from text sources into structured representations, such as knowledge bases. Our approach to this task is to use machine-learning methods to induce routines for extracting facts from text. We describe two learning methods that we have applied to this task --- a statistical text classification method, and a relational learning method --- and our initial experiments in learning such information-extraction routines. We also present an approach to decreasing the cost of learning information-extraction routines by learning from "weakly" labeled training data.

We survey ve mathematical discovery programs by looking in detail at the discovery processes they illustrate and the success they've had. We focus on how they estimate the interestingness of concepts and conjectures and extract some common notions about interestingness in automated mathematical discovery. We detail how empirical evidence is used to give plausibility to conjectures, and the dierent ways in which a result can be thought of as novel. We also look at the ways in which the programs assess how surprising and complex a conjecture statement is, and the dierent ways in which the applicability of a concept or conjecture is used. Finally, we note how a user can set tasks for the program to achieve and how this aects the calculation of interestingness. We conclude with some hints on the use of interestingness measures for future developers of discovery programs in mathematics.

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Hypothesis generation, a crucial initial step for making scientific discoveries, relies on prior knowledge, experience and intuition. Chance connections made between seemingly distinct subareas sometimes turn out to be fruitful. The goal in text mining is to assist in this process by automatically discovering a small set of interesting hypotheses from a suitable text collection.

Relevance is a, if not even the, key notion in information science in general and information retrieval in particular. This two-part critical review traces and synthesizes the scholarship on relevance over the past 30 years or so and provides an updated framework within which the still widely dissonant ideas and works about relevance might be interpreted and related. It is a continuation and update of a similar review that appeared in 1975 under the same title, considered here as being Part I. The present review is organized in two parts: Part II addresses the questions related to nature and manifestations of relevance, and Part III addresses questions related to relevance behavior and effects. In Part II, the nature of relevance is discussed in terms of meaning ascribed to relevance, theories used or proposed, and models that have been developed. The

We report experiments that use lexical statistics, such as word frequency counts, to discover hidden connections in the medical literature. Hidden connections are those that are unlikely to be found by examination of bibliographic citations or the use of standard indexing methods and yet establish a relationship between topics that might profitably be explored by scientific research. Our experiments were conducted with the MEDLINE medical literature database and follow and extend the work of Swanson.

Humans can make hasty, but generally robust judgements about what a text fragment is, or is not, about. Such judgements are termed information inference. By drawing on theories from non-classical logic and applied cognition, an information inference mechanism is proposed which makes inferences via computations of information flow through a high dimensional conceptual space. Within a conceptual space information is represented geometrically. In this article, an approximation of a conceptual space is employed whereby geometric representations of words are realized as vectors in a high dimensional semantic space, which is automatically constructed from a text corpus. Two approaches were presented for priming vector representations according to context. The first approach uses a concept combination heuristic to adjust the vector representation of a concept in the light of the representation of another concept. The second approach computes a prototypical concept on the basis of exemplar trace texts and moves it in the dimensional space according to the context. Information inference is evaluated by measuring the effectiveness of query models derived by information flow computations. Results show that information flow contributes significantly to query model effectiveness, particularly with respect to precision. Moreover, retrieval effectiveness compares favourably with two probabilistic query models, and another based on semantic association. More generally, this article can be seen as a contribution towards realizing operational systems which mimic human text-based reasoning.

Literature-based discovery has resulted in new knowledge.

An important problem in computational scientific discovery is to identify, among the diversity of discovery programs written in various sciences, a commonality that will take a next step beyond the acknowledged general -- but weak -- framework of heuristic search. We characterize discovery in science as the generation of novel, interesting, plausible, and intelligible knowledge about the objects of study. We then analyze four current machine discovery programs in chemistry, medicine, mathematics, and linguistics according to how their design, or the circumstances of their application, heighten the chances of finding knowledge that has all four properties. Some general patterns emerge, although some strategies seem idiosyncratic. Our candidate for a commonality, which focuses on human factors, can be used pragmatically to evaluate and compare the designs of discovery programs that are intended to be used as collaborators by scientists. 1 1 Introduction Early work on machine scienti...

In this paper, we review AI research on computational discovery and its recent application to the discovery of new scientific knowledge. We characterize five historical stages of the scientific discovery process, which we use as an organizational framework in describing applications. We also identify five distinct steps during which developers or users can influence the behavior of a computational discovery system. Rather than criticizing such intervention, as done in the past, we recommend it as the preferred approach to using discovery software. As evidence for the advantages of such human-computer cooperation, we report seven examples of novel, computer-aided discoveries that have appeared in the scientific literature. We consider briefly the role that humans played in each case, then examine one such interaction in more detail. We close by recommending that future systems provide more explicit support for human intervention in the discovery process. Running head: Computational Sci...