Linn Marks Collins

ScienceSifter: facilitating activity awareness in collaborative research groups through focused information feeds

As the amount of scientific information available to researchers increases, the challenge of sifting through the information to find what is truly important to their work increases, as well. In this paper we describe ScienceSifter, a tool that addresses this challenge by enabling groups of researchers and channel editors to create and customize information feeds. Using ScienceSifter, users can combine several information feeds, then filter them by keywords to create a focused information feed. They can view the feed in a shared information space in the form of a list, a list with descriptions, or a hyberbolic tree visualization, and they can save items to a shared list. Thus ScienceSifter can reduce the amount of time researchers spend finding and sharing information. It can facilitate shared intellectual activity and activity awareness among the members of the group

A Semantic Registry for Digital Library Collections and Services

The Semantic Web has to date promised far more than it has been able to deliver. Libraries have been understandably cautious about investing extensively in ontology creation, mapping data to semantic representations, or tackling the hard problems of semantically describing collections and services. We look at the evolution of network addressable services, to service oriented architectures, and now toward semantically enhanced service oriented architectures. We also investigate various library technology standards that might influence semantic service and collection descriptions, as well as semantic registries. Next, we describe our own efforts to build a semantic registry of services and collections, for semantic metadata collections. Finally, we speculate about how these standards may evolve in the future.

“At scale” author name matching with Hadoop/MapReduce

Purpose – The purpose of this paper is to describe a process for extracting and matching author names from large collections of bibliographic metadata using the Hadoop implementation of MapReduce. It considers the challenges and risks associated with name matching on such a large‐scale and proposes simple matching heuristics for the reduce process. The resulting semantic graphs of authors link names to publications, and include additional features such as phonetic representations of author last names. The authors believe that this achieves an appropriate level of matching at scale, and enables further matching to be performed with graph analysis tools.Design/methodology/approach – A topically‐focused collection of metadata records describing peer‐reviewed papers was generated based upon a search. The matching records were harvested and stored in the Hadoop Distributed File System (HDFS) for processing by hadoop. A MapReduce job was written to perform coarse‐grain author name matching, and multiple papers ...

Entity disambiguation using semantic networks

A major stumbling block preventing machines from understanding text is the problem of entity disambiguation. While humans find it easy to determine that a person named in one story is the same person referenced in a second story, machines rely heavily on crude heuristics such as string matching and stemming to make guesses as to whether nouns are coreferent. A key advantage that humans have over machines is the ability to mentally make connections between ideas and, based on these connections, reason how likely two entities are to be the same. Mirroring this natural thought process, we have created a prototype framework for disambiguating entities that is based on connectedness. In this article, we demonstrate it in the practical application of disambiguating authors across a large set of bibliographic records. By representing knowledge from the records as edges in a graph between a subject and an object, we believe that the problem of disambiguating entities reduces to the problem of discovering the most strongly connected nodes in a graph. The knowledge from the records comes in many different forms, such as names of people, date of publication, and themes extracted from the text of the abstract. These different types of knowledge are fused to create the graph required for disambiguation. Furthermore, the resulting graph and framework can be used for more complex operations.

The Geographic Awareness Tool: techniques for geo‐encoding digital library content

Purpose – The purpose of this paper is to explore motivations for libraries to build location aware services.Design/methodology/approach – The paper examines various techniques for generating geo‐referenced metadata, including converting placenames to coordinates and using entity extraction to discover places in unstructured text, such as abstracts. It describes several prototype services developed, which deliver geo‐referenced data in different ways – as search results overlaid onto a map, as location specific data delivered to location aware mobile devices just in time, and as raw structured metadata supplied by web services, which could be combined with other data sets in support of e‐science.Findings – Although library metadata standards can accommodate location, catalogers rarely provide location information related to the content of the intellectual product. Entity extraction services can find location information in free text contents, such as abstracts, and even provide the appropriate coordinates...

Collaborative eScience libraries

In the context of collaborative eScience, digital libraries are one of many distributed, interoperable resources available to scientists that facilitate both human and machine collaboration: machine collaboration in the form of standards such as the Open Archives Initiative Protocol for Metadata Harvesting and human collaboration in the form of collaborative workspaces. This paper describes a set of collaborative workspaces created at the Los Alamos National Laboratory Research Library, initial patterns of use, and additional user requirements determined based on these initial patterns of use.

Semantically Enhancing Collections of Library and Non-Library Content

Many digital libraries have not made the transition to semantic digital libraries, and often with good reason. Librarians and information technologists may not yet grasp the value of semantic mappings of bibliographic metadata, they may not have the resources to make the transition and, even if they do, semantic web tools and standards have varied in terms of maturity and performance. Selecting appropriate or reasonable classes and properties from ontologies, linking and augmenting bibliographic metadata as it is mapped to triples, data fusion and re-use, and considerations about what it means to represent this data as a graph, are all challenges librarians and information technologists face as they transition their various collections to the semantic web. This paper presents some lessons we have learned building small, focused semantic digital library collections that combine bibliographic and non-bibliographic data, based on specific topics. The tools map and augment the metadata to produce a collection of triples. We have also developed some prototype tools atop these collections which allow users to explore the content in ways that were either not possible or not easy to do with other library systems.

Information space models for data integration, and entity resolution

Geospatial information systems provide a unique frame of reference to bring together a large and diverse set of data from a variety of sources. However, automating this process remains a challenge since: 1) data (particularly from sensors) is error prone and ambiguous, 2) analysis and visualization tools typically expect clean (or exact) data, and 3) it is difficult to describe how different data types and modalities relate to each other. In this paper we describe a data integration approach that can help address some of these challenges. Specifically we propose a light weight ontology for an Information Space Model (ISM). The ISM is designed to support functionality that lies between data catalogues and domain ontologies. Similar to data catalogues, the ISM provides metadata for data discovery across multiple, heterogeneous (often legacy) data sources e.g. maps servers, satellite images, social networks, geospatial blogs. Similar to domain ontologies, the ISM describes the functional relationship between these systems with respect to entities relevant to an application e.g. venues, actors and activities. We suggest a minimal set of ISM objects, and attributes for describing data sources and sensors relevant to data integration. We present a number of statistical relational learning techniques to represent and leverage the combination of deterministic and probabilistic dependencies found within the ISM. We demonstrate how the ISM provides a flexible language for data integration where unknown or ambiguous relationships can be mitigated.

The karst collaborative workspace for analyzing and annotating scientific datasets

Scientific fields of study such as astrobiology, nanotechnology, and cave and karst science involve the study of images and associated biological, physiochemical, and geological data. In order to ensure interdisciplinary analysis, it is important to make these kinds of datasets available for analysis and curation by the scientific community. The goal of this project is to design and develop an online workspace that enables scientists to collaboratively view, analyze, and annotate such datasets. The prototype contains scanning electron micrographs of karst and cave samples. The target users are the interdisciplinary community of scientists who study karst samples to learn more about critical biological and geological processes and the microbial communities often found in karst terrain. The prototype can inform the design and development of collaborative workspaces in other interdisciplinary fields.

Structural issues in multimedia design

This tutorial addresses the structural issues that emerge in the context of designing and developing a range of interactive multimedia applications, from those with basic navigational structures, such as branching and elaboration, to those with complex discourse structures, such as interactive narratives and interactive essays. Topics include basic interactive structures; complex interactive discourse structures; and the kinds of global representations of content, or conceptual macrostructures [1], that are appropriate for various kinds of content and applications. Concepts are illustrated with examples from the World Wide Web, commercial products, and research prototypes.