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Classifications and phylogenies of perceived natural entities change in the light of new evidence. Taxonomic changes, translated into Code-compliant names, frequently lead to name:meaning dissociations across succeeding treatments. Classification standards such as the Mammal Species of the World (MSW) may experience significant levels of taxonomic change from one edition to the next, with potential costs to long-term, large-scale information integration. This circumstance challenges the biodiversity and phylogenetic data communities to express taxonomic congruence and incongruence in ways that both humans and machines can process, that is, to logically represent taxonomic alignments across multiple classifications. We demonstrate that such alignments are feasible for two classifications of primates corresponding to the second and third MSW editions. Our approach has three main components: (i) use of taxonomic concept labels, that is name sec. author (where sec. means according to), to assemble each concept hierarchy separately via parent/child relationships; (ii) articulation of select concepts across the two hierarchies with user-provided Region Connection Calculus (RCC-5) relationships; and (iii) the use of an Answer Set Programming toolkit to infer and visualize logically consistent alignments of these input constraints. Our use case entails the Primates sec. Groves (1993; MSW2-317 taxonomic concepts; 233 at the species level) and Primates sec. Groves (2005; MSW3-483 taxonomic concepts; 376 at the species level). Using 402 RCC-5 input articulations, the reasoning process yields a single, consistent alignment and 153,111 Maximally Informative Relations that constitute a comprehensive meaning resolution map for every concept pair in the Primates sec. MSW2/MSW3. The complete alignment, and various partitions thereof, facilitate quantitative analyses of name:meaning dissociation, revealing that nearly one in three taxonomic names are not reliable across treatments-in the sense of the same name identifying congruent taxonomic meanings. The RCC-5 alignment approach is potentially widely applicable in systematics and can achieve scalable, precise resolution of semantically evolving name usages in synthetic, next-generation biodiversity, and phylogeny data platforms.

An advantage of scientific workflow systems is their ability to collect runtime provenance information as an execution trace. Traces include the computation steps invoked as part of the workflow run along with the corresponding data consumed and produced by each workflow step. The information captured by a trace is used to infer “lineage” relationships among data items, which can help answer provenance queries to find workflow inputs that were involved in producing specific workflow outputs. Determining lineage relationships, however, requires an understanding of the dependency patterns that exist between each workflow step’s inputs and outputs, and this information is often under-specified or generally assumed by workflow systems. For instance, most approaches assume all outputs depend on all inputs, which can lead to lineage “false positives”. In prior work, we defined annotations for specifying detailed dependency relationships between inputs and outputs of computation steps. These annotations are used to define corresponding rules for inferring fine-grained data dependencies from a trace. In this paper, we extend our previous work by considering the impact of dependency annotations on workflow specifications. In particular, we provide a reasoning framework to ensure the set of dependency annotations on a workflow specification is consistent. The framework can also infer a complete set of annotations given a partially annotated workflow. Finally, we describe an implementation of the reasoning framework using answer-set programming.

Derivations and proofs are a form of provenance in automated deduction that can assist users in understanding how reasoners derive logical consequences from premises. However, system-generated proofs are often overly complex or detailed, and making sense of them is non-trivial. Conversely, without any form of provenance, it is just as hard to know why a certain fact was derived.

We describe a framework called the Uni-Level Description (ULD) for accurately representing information from a broad range of data models. The ULD extends previous meta-data-model approaches by: (a) providing uniform representation and access to data model, schema, and data, and (b) supporting data models with non-traditional schema arrangements, including those that allow optional and multiple levels of schema. Because the ULD is a flat, first-order representation, we show how Datalog over the ULD can provide a flexible mechanism to query, extract, and transform reformation from data sources that exhibit various types of structural heterogeneity.

We consider the task of merging datasets that have been organized using different, but aligned taxonomies. We assume such a merge is intended to create a single dataset that unambiguously describes the information in the source datasets using the alignment. We also assume that the merged result should reflect the observations of the datasets as specifically as possible. Typically, there will be no single merge result that is both unambiguous and maximally specific. In this case, a user may be provided with a set of possible merged datasets. If the user requires a single dataset, that dataset loses specificity. Here we examine whether the data exchange setting can provide a way to derive a “best-effort” merge. We find that the data exchange setting might be a good candidate for providing the merge, but further research is needed.

We present a method for using aligned ontologies to merge taxonomically organized data sets that have apparently compatible schemas, but potentially different semantics for corresponding domains. We restrict the relationships involved in the alignment to basic set relations and disjunctions of these relations. A merged data set combines the domains of the source data set attributes, conforms to the observations reported in both data sets, and minimizes uncertainty introduced by ontology alignments. We find that even in very simple cases, merging data sets under this scenario is non-trivial. Reducing uncertainty introduced by the ontology alignments in combination with the data set observations often results in many possible merged data sets, which are managed using a possible worlds semantics. The primary contributions of this paper are a framework for representing aligned data sets and algorithms for merging data sets that report the presence and absence of taxonomically organized entities, including an efficient algorithm for a common data set merging scenario.

Scientific workflow systems are problem-solving environments that allow scientists to automate and reproduce data management and analysis tasks. Workflow components include actors (e.g., queries, transformations, analyses, simulations, visualizations), and datasets which are produced and consumed by actors. The increasing number of such components creates the problem of discovering suitable components and of composing them to form the desired scientific workflow. In previous work we proposed the use of semantic types (annotations relative to an ontology) to solve these problems. Since creating semantic types can be complex and time-consuming, scalability of the approach becomes an issue. In this paper we propose a framework to automatically derive semantic types from a (possibly small) number of initial types. Our approach propagates the given semantic types through workflow steps whose input and output data structures are related via query expressions. By propagating semantic types, we can significantly reduce the effort required to annotate datasets and components and even derive new “candidate axioms” for inclusion in annotation ontologies.

We present an approach to support incremental navigation of struc- tured information, where the structure is introduced by the data model and schema (if present) of a data source. Simple browsing through data values and their connections is an effective way for a user or an automated system to access and explore information. We use our previously defined Uni-Level Description (ULD) to represent an information source explicitly by capturing the source's data model, schema (if present), and data values. We define generic operators for incremental navigation that use the ULD directly along with techniques for specifying how a given representation scheme can be navigated. Because our nav- igation is based on the ULD, the operations can easily move from data to schema to data model and back, supporting a wide range of applications for exploring and integrating data. Further, because the ULD can express a broad range of data models, our navigation operators are applicable, without modification, across the corresponding model or schema. In general, we believe that information sources may usefully support various styles of navigation, depending on the type of user and the user's desired task.

Ecologists spend considerable effort integrating heterogeneous data for statistical analyses and simulations, for example, to run and test predictive models. Our research is focused on reducing this effort by providing data integration and transformation tools, allowing researchers to focus on “real science,” that is, discovering new knowledge through analysis and modeling. This paper defines a generic framework for transforming heterogeneous data within scientific workflows. Our approach relies on a formalized ontology, which serves as a simple, unstructured global schema. In the framework, inputs and outputs of services within scientific workflows can have structural types and separate semantic types (expressions of the target ontology). In addition, a registration mapping can be defined to relate input and output structural types to their corresponding semantic types. Using registration mappings, appropriate data transformations can then be generated for each desired service composition. Here, we describe our proposed framework and an initial implementation for services that consume and produce XML data.

One advantage of having several different representation schemes and data models is that users can select the right representation and associated tools for their particular need. However, multiple representations introduce structural, model-based heterogeneity, making it difficult to combine information from different sources and exploit information using generic tools (e.g., for querying or browsing). In this work, we define a uniform representation based on a meta-data-model called the Uni-Level Description (ULD) that can accommodate and accurately store information in a broad range of data models. The ULD defines three distinct instance-of relationships plus a relationship for modeling conformance, which is used to connect (data) constructs to other (schema) constructs and can be constrained to reflect the requirements of the data model. The ULD has been shown to enable powerful, generic transformation rules and simple generic browsing capability over information represented in diverse data models and representation schemes.

Considerable research exists on providing structured access to unstructured information sources, primarily for search and query. Little attention has been placed on (1) keeping the by-products of the process (e.g., connections between structured and unstructured data are typically forgotten and context is lost), (2) developing a rich, conceptual structure for this new layer of information (e.g., the structured data is often represented in simple relational tables), and (3) further elaborating and linking the new, structured information. We propose superimposed schematics to address these issues. Superimposed schematics offer ER modeling constructs integrated with marks, where a mark holds an address to an information element in an underlying source. A superimposed schematic enables a conceptual addressing scheme for the information it contains (directly) and for the information it references (through marks). This addressing scheme can then be used for marking from additional layers of superimposed information. We consider schematics as a useful model for superimposed information and discuss how it fits into our general research on superimposed information.