Christopher Condit

Federated Access to Heterogeneous Information Resources in the Neuroscience Information Framework (NIF)

The overarching goal of the NIF (Neuroscience Information Framework) project is to be a one-stop-shop for Neuroscience. This paper provides a technical overview of how the system is designed. The technical goal of the first version of the NIF system was to develop an information system that a neuroscientist can use to locate relevant information from a wide variety of information sources by simple keyword queries. Although the user would provide only keywords to retrieve information, the NIF system is designed to treat them as concepts whose meanings are interpreted by the system. Thus, a search for term should find a record containing synonyms of the term. The system is targeted to find information from web pages, publications, databases, web sites built upon databases, XML documents and any other modality in which such information may be published. We have designed a system to achieve this functionality. A central element in the system is an ontology called NIFSTD (for NIF Standard) constructed by amalgamating a number of known and newly developed ontologies. NIFSTD is used by our ontology management module, called OntoQuest to perform ontology-based search over data sources. The NIF architecture currently provides three different mechanisms for searching heterogeneous data sources including relational databases, web sites, XML documents and full text of publications. Version 1.0 of the NIF system is currently in beta test and may be accessed through http://nif.nih.gov.

Use of model organism and disease databases to support matchmaking for human disease gene discovery.

The Matchmaker Exchange application programming interface (API) allows searching a patients genotypic or phenotypic profiles across clinical sites, for the purposes of cohort discovery and variant disease causal validation. This API can be used not only to search for matching patients, but also to match against public disease and model organism data. This public disease data enable matching known diseases and variant–phenotype associations using phenotype semantic similarity algorithms developed by the Monarch Initiative. The model data can provide additional evidence to aid diagnosis, suggest relevant models for disease mechanism and treatment exploration, and identify collaborators across the translational divide. The Monarch Initiative provides an implementation of this API for searching multiple integrated sources of data that contextualize the knowledge about any given patient or patient family into the greater biomedical knowledge landscape. While this corpus of data can aid diagnosis, it is also the beginning of research to improve understanding of rare human diseases.

Response of Commercial Ships to a Voluntary Speed Reduction Measure: Are Voluntary Strategies Adequate for Mitigating Ship-Strike Risk?

Collisions between ships and whales are an increasing concern for endangered large whale species. After an unusually high number of blue whales (Balaenoptera musculus) were fatally struck in 2007 off the coast of southern California, federal agencies implemented a voluntary conservation program to reduce the likelihood of ship-strikes in the region. This initiative involved seasonal advisory broadcasts requesting vessel operators to voluntarily slow to 10 knots or less when transiting a 75 nm stretch of designated shipping lanes. We monitored ship adherence with those speed advisories using Automatic Identification System data. Daily average speed of cargo and tanker ships and the average speed of individual ship transits before, during, and after the notices were statistically analyzed for changes related to the notices. Whereas a small number of individual ships (1%) traveled significantly slower during the requested periods, speeds were not at or below the recommended 10 knots, nor were daily average speeds reduced during the notices. Voluntary conservation measures are established in a variety of contexts, and may be preferable to regulatory action; in this case, a request to make voluntary changes appeared largely ineffective. Reducing collision risks for whales in this area will require consideration of the various factors that likely explain the lack of adherence when developing an alternative strategy.

A formal ontology of subcellular neuroanatomy.

The complexity of the nervous system requires high-resolution microscopy to resolve the detailed 3D structure of nerve cells and supracellular domains. The analysis of such imaging data to extract cellular surfaces and cell components often requires the combination of expert human knowledge with carefully engineered software tools. In an effort to make better tools to assist humans in this endeavor, create a more accessible and permanent record of their data, and to aid the process of constructing complex and detailed computational models, we have created a core of formalized knowledge about the structure of the nervous system and have integrated that core into several software applications. In this paper, we describe the structure and content of a formal ontology whose scope is the subcellular anatomy of the nervous system (SAO), covering nerve cells, their parts, and interactions between these parts. Many applications of this ontology to image annotation, content-based retrieval of structural data, and integration of shared data across scales and researchers are also described.

Editorial: BioDB: An ontology-enhanced information system for heterogeneous biological information

This paper presents BIODB, an ontology-enhanced information system to manage heterogeneous data. An ontology-enhanced system is a system where ad hoc data is imported into the system by a user, annotated by the user to connect the data to an ontology or other data sources, and then all data connected through the ontology can be queried in a federated manner. The BIODB system enables multi-model data federation, i.e., it federate data that can be in different data models including, relational, XML and RDF, sequence data and so on. It uses an ontologically enhanced system catalog, an ontological data index, an association index to facilitate cross-model data mapping, and a new algorithm for ontology-assisted keyword queries with ranking. The paper describes these components in detail, and presents an evaluation of the architecture in the context of an actual application.

Toward an ontological database for subcellular neuroanatomy

We present the semantic data model for an ontological database for subcellular anatomy for Neurosciences. The data model builds upon the foundations of OWL and the Basic Formal Ontology, but extends them to include novel constructs that address several unresolved challenges encountered by biologists in using ontological models in their databases. The model addresses the interplay between models of space and objects located in the space, objects that are defined by constrained spatial arrangements of other objects, the interactions among multiple transitive relationships over the same set of concepts and so on. We propose the notion of parametric relationships to denote different multiple ways of parcellating the same space. We also introduce the notion of phantom instances to address the mismatches between the ontological properties of a conceptual object and the actual recorded instance of that object in cases where the observed object is partially visible.

Graphitti: An Annotation Management System for Heterogeneous Objects

Annotation is the process of supplementing data with additional information that was not part of the actual observation, but reflects post-facto comments and associations made by a user who analyzes the data. While annotation management systems are emerging in the field of relational data, such systems for scientific applications, where there is a wide heterogeneity in the types of annotable data, are almost nonexistent. In this demonstration paper, we describe Graphitti, a tool that (i) allows a user to annotate a wide variety of scientific data, and (ii) allows a user to query data and their annotations in a seamless manner.

Semantic query reformulation: the NIF experience

The NIF system is a semantic search engine that uses an ontology to improve search quality. In this experience paper we present SKEYQL, our semantic keyword query language and describe a number of ontology-based query reformulation strategies that go beyond standard query expansion techniques. We also present a set of lessons learnt and strategies that did not work. We reaffirm the importance of pre-annotating data to ensure quality query results.

Processing semantic keyword queries for scientific literature

In this short paper, we present early results from an ongoing research on creating a new graph-based representation from NLP analysis of scientific documents so that the graph can be utilized for answering structured queries on NL-processed data. We present a sketch of the data model and the query language to show how scientifically meaningful queries can be posed against this graph structure.