The vision of @math -DB introduces deterministic scientific hypotheses as a kind of uncertain and probabilistic data, and opens some key technical challenges for enabling data-driven hypothesis management and analytics. The @math -DB system addresses those challenges throughout a design-by-synthesis pipeline that defines its architecture. It processes hypotheses from their XML-based extraction to encoding as uncertain and probabilistic U-relational data, and eventually to their conditioning in the presence of observations. In this demo we present a first prototype of the @math -DB system. We showcase its core innovative features by means of use case scenarios in computational science in which the hypotheses are extracted from a model repository on the web and evaluated (rated ranked) as probabilistic data.
. Finally, our framework is comparable with Bioinformatics' initiatives that address hypothesis encoding into the RDF data model @cite_18 . We point out the Robot Scientist, HyBrow and SWAN (cf. @cite_18 ), all of which consist in some ad-hoc RDF encoding of sequence and genome analysis hypotheses under varying levels of structure (viz., from gene G has function A' statements to free text). Our framework in turn consists in the U-relational encoding of hypotheses from mathematical equations, which is (to our knowledge) the first work on hypothesis relational encoding.
@cite_18
2168773024
With the rise of the Semantic Web more and more data become available encoded using the Semantic Web standard RDF. RDF is faced towards machines: designed to be easily processable by machines it is difficult to be understood by casual users. Transforming RDF data into human-comprehensible text would facilitate non-experts to assess this information. In this paper we present a languageindependent method for extracting RDF verbalization templates from a parallel corpus of text and data. Our method is based on distant-supervised simultaneous multi-relation learning and frequent maximal subgraph pattern mining. We demonstrate the feasibility of our method on a parallel corpus of Wikipedia articles and DBpedia data for English and German. The majority of knowledge on the Web is encoded in unstructured text and is not linked to formalized knowledge, such as ontologies and rules. A potential solution to this problem is to acquire this knowledge through natural language processing and text mining methods. Prior work has focused on automatically extracting RDF- or OWL-based ontologies from text; however, the type of knowledge acquired is generally restricted to simple term hierarchies. This paper presents a general-purpose framework for acquiring more complex relationships from text and then encoding this knowledge as rules. Our approach starts with existing domain knowledge in the form of OWL ontologies and Semantic Web Rule Language (SWRL) rules and applies natural language processing and text matching techniques to deduce classes and properties. It then captures deductive knowledge in the form of new rules. We have evaluated our framework by applying it to web-based text on car rental requirements. We show that our approach can automatically and accurately generate rules for requirements of car rental companies not in the knowledge base. Our framework thus rapidly acquires complex knowledge from free text sources. We are expanding it to handle richer domains, such as medical science. This paper presents a theoretical analysis of the problem of domain adaptation with multiple sources. For each source domain, the distribution over the input points as well as a hypothesis with error at most ∊ are given. The problem consists of combining these hypotheses to derive a hypothesis with small error with respect to the target domain. We present several theoretical results relating to this problem. In particular, we prove that standard convex combinations of the source hypotheses may in fact perform very poorly and that, instead, combinations weighted by the source distributions benefit from favorable theoretical guarantees. Our main result shows that, remarkably, for any fixed target function, there exists a distribution weighted combining rule that has a loss of at most ∊ with respect to any target mixture of the source distributions. We further generalize the setting from a single target function to multiple consistent target functions and show the existence of a combining rule with error at most 3∊. Finally, we report empirical results for a multiple source adaptation problem with a real-world dataset. An "Occam algorithm" learning model maintains a tentative hypothesis consistent with past observations and, when a new observation is inconsistent with the current hypothesis, updates to the next-simplest hypothesis consistent with all observations. In previous work, observations were assumed to be stochastically independent. This paper initiates study of such models under weaker Markovian assumptions on the observations, In the special case where the sequence of hypotheses satisfies a monotonicity condition, it is shown that the number of mistakes in classifying the first t observations is O(?t log 1 ?i), where ?i is the stationary probability of the initial state, i, of the Markov chain.