Domain-specific Vocabulary Research Articles

Linked Data for science and education

Sharing of resources, resource metadata, and data across the Web is a central principle in scientific and educational contexts. Scientific collaboration has long been striving for wider reuse and sharing of knowledge and data. Likewise, the Open Educational Resources community has promoted the widespread exploitation of public and reusable educational resources throughout the last decade. Hence, technologies to enable interoperability of shared resources and data have long been at the centre of scientific and educational information systems. However, due to the lack of a shared technology stack and joint principles, the landscape of developed and utilised standards is very fragmented and covers an increasing variety of heterogeneous technologies, such as repositories with proprietary interfaces and query mechanisms. Moreover, a broad range of largely incompatible metadata schemas and taxonomies have been developed to describe and expose educational resources, and scientific workflows and data. Due to the prevailing heterogeneity of deployed approaches and technologies, interoperability remains an open challenge. At the same time, the Linked Data (LD) approach has emerged as the most recent evolution of the Semantic Web, aiming at Web-scale data interoperability, it has produced an ever growing amount of data sets and schemas available on the Web. Given the proven capabilities of LD technologies towards realising Web scale data sharing and reuse, scientific and educational practitioners have started to adopt those principles. Results of such activities cover joint schemas used for describing scientific or educational resources, vast collections of structured data about, for instance, cultural or historic artifacts, bibliographic collections, domainspecific vocabularies capturing extensive amounts of scientific domain knowledge – where the life sciences are particularly well represented – as well as baseline technologies used to expose and integrate linked datasets. This special issue contains four papers documenting the most recent developments in Linked Data for Science and Education. Focusing on the collection and annotation of scientific data, the paper Ontology of Units of Measure and Related Concepts by Hajo Rijgersberg, Mark van Assem and Jan Top shows how to annotate quantitative research data for sharing and reuse. The design choices made during the development of this OWL ontology are explained, and the application of the ontology is demonstrated with different use cases and the tools and web services built to handle them. The paper Publishing Bibliographic Data on the Semantic Web using BibBase by Reynold S. Xin, Oktie Hassanzadeh, Christian Fritz, Shirin Sohrabi and Renee J. Miller addresses the next step in the research process, namely publishing the results and handling the publication metadata. BibBase is a system that automatically creates a number of services from standard BibTex files, providing the contents as rich Linked Data queryable through a SPARQL endpoint, as RSS feeds, and as HTML snippets, thus addressing a number of use cases for sharing and dissemination of publication metadata. While the first two papers focus on the dissemination of scientific assets, the remaining two papers ad-

Construction and Evaluation of IT Security Domain-Specific Vocabulary

Construction and Evaluation of IT Security Domain-Specific Vocabulary

Keywords Review of IT Security Literature in Recent 20 Years

The volume of published scientific literature available on Internet has been increasing exponentially. Some of them reflect the latest achievement of the specific research domain. In recent years, many projects have been funded aiming to online scientific literature mining, especially in biomedical research. Scientific literature covers most of the hot topics in the research field and has a very large domain-specific vocabulary. The exploitation of domain knowledge and specialized vocabulary can dramatically improve the result of literature text processing. The purpose of this paper is to identify the frequently used keywords in IT security literatures. The result then can be utilized to improve the performance of automatic IT security document retrieval, identification and classification. Our method is to query CiteseeX to retrieve source data of paper description information and build an artificially annotated corpus. Over the corpus, we perform words frequency statistics, word co-occurrence analysis, discrimination index computation, retrieval efficiency analysis and thus build a lexicon of IT security based on our experimental result. The lexicon can further be used in improving retrieval performance and assisting new words discovering and document classification.

Personal name vocabularies as linked open data: A case study of jazz artist names

This article describes the process of constructing a vocabulary of personal names of jazz artists in the form of Linked Open Data (LOD). Created as a name directory to support the development of the Linked Jazz project, it provides a case study that demonstrates the value and the challenges of developing a domain-specific vocabulary tool that draws upon the semantics of DBpedia, a major LOD dataset. The article also addresses possible strategies for enhancing the directory to make it a more robust personal name vocabulary.

An Academic Curriculum Will Close the Academic Achievement Gap

America's unyielding academic achievement gap has been a national priority for a long time; yet, some schools have succeeded with academically disadvantaged youth. Usually, these institutions embrace a culture of success and follow an academic curriculum that is grounded in core knowledge and scholastic vocabulary. Academically disadvantaged students need school-wide programs that meet their distinct needs. These programs emphasize basic knowledge, grounded in contextual references. Students are taught to decode and comprehend domain specific and academic vocabulary. They learn skills and content that will help them achieve as successful adults. These schools are the antithesis of “mean streets.” They welcome disadvantaged students and provide the intellectual and emotional tools they need to thrive, while instilling self-discipline and hope in their pupils.

A psycholinguistic approach to oral language assessment

This paper presents a framework for incorporating the assessment of psycholinguistic constructs into spoken language proficiency testing. Although response time and automaticity of language processing are vital factors in language acquisition, as constructs they are rarely explicitly assessed in language tests. It is proposed that efficiency of processing, although it does not appear in most models of communicative language competence, might be viewed as a language competence in its own right. In contrast to communicative approaches to testing which evaluate mastery of complex language structures or domain-specific vocabulary, an approach which emphasizes efficiency of processing evaluates the speed and accuracy with which a learner processes familiar language, as this provides equally useful information about their L2 proficiency. In a study to demonstrate this, the speech of 147 test takers was evaluated as they performed on conceptually simple and straightforward performance tasks such as elicited imitation. It was found that sentence repetition and sentence building tasks reliably separated test takers according to proficiency in a way that accounts for the trade-off between fluency, accuracy and complexity. While these tasks would not be considered communicative, they are psycholinguistically valid and also link to good practice in classroom language learning activities.

The Man behind the Curtain: What Cognitive Science Reveals about Drawing

tasks, such as decision making, problem solving, and analogic thinking, might be disrupted by verbalization. In one study people were asked to read groups of stories. Some were asked to verbally discuss each story as soon as they finished it. Others read all the stories without verbally discussing them. Those who waited were better able to uncover the deeper structural similarities and relationships between the stories. Those who verbalized as they read seemed to have been distracted by incidental detail at the expense of global and analogic reasoning. Over time, through both training and experience, the effects of verbal overshadowing do tend to fade; however, meta-analysis has demonstrated the overall reliability of results showing verbal overshadowing.31 One possibility may be that a novice in a particular domain will use words to focus on specific elements, or parts of the whole, rather than the relationships between the parts.32 Domain-specific vocabulary may help to overcome these kinds of biases as deeper levels of conceptual understanding are gained through experience. While it’s easy to say someone has a large nose, it’s more difficult to describe in words the position of the nose in relation to other facial features. This might make sense of the surprising result that verbalization can sometimes impair analogic thinking. Situations are analogous when the relations between the elements, rather than the elements themselves, are similar. Configural, rather than featural, awareness is the kind of thinking needed for analogic reasoning and successful transfer of knowledge from one domain to another. Cognitive Error and Metacognition The good news about verbal overshadowing is that it can be overcome through reflective practice.33 We can learn to think smarter. Developing metacognitive skills—the ability to detect and analyze our own thinking processes—can help us overcome many common cognitive errors.34 Intelligence can also be cultivated through “distributed cognition,” in other words, through the use of external supports, which can be social, symbolic, or physical.35

L.A. meta (data): Exploring vocabulary interoperability in libraries, Archives and Museums

AbstractThis poster session reports the examination of user tasks in the context of domain specific vocabulary interoperability. Two vocabularies, Library of Congress Subject Headings and the Art and Architecture thesaurus, were selected for comparison using a single user task (Cross‐vocabulary discovery). Analyzing the structure of the vocabularies in the context of this task found challenges associated with the differences in concept representation, differences in vocabulary structures and varying levels of specificity. The exploration found that the similarities in structure and semantics for these two vocabularies provide a solid foundation for both user‐determined on demand and automatically derived interoperability in the context of the cross‐vocabulary discovery task. The exploration of AAT and LCSH vocabularies also found that considering the impact on stakeholders (e.g., end users, vocabulary creators, vocabulary implementers) is important when considering how to design and implement vocabulary interoperability applications.

L.A. meta (data): Exploring vocabulary interoperability in libraries, Archives and Museums

AbstractThis poster session reports the examination of user tasks in the context of domain specific vocabulary interoperability. Two vocabularies, Library of Congress Subject Headings and the Art and Architecture thesaurus, were selected for comparison using a single user task (Cross‐vocabulary discovery). Analyzing the structure of the vocabularies in the context of this task found challenges associated with the differences in concept representation, differences in vocabulary structures and varying levels of specificity. The exploration found that the similarities in structure and semantics for these two vocabularies provide a solid foundation for both user‐determined on demand and automatically derived interoperability in the context of the cross‐vocabulary discovery task. The exploration of AAT and LCSH vocabularies also found that considering the impact on stakeholders (e.g., end users, vocabulary creators, vocabulary implementers) is important when considering how to design and implement vocabulary interoperability applications.

Automatic extraction of angiogenesis bioprocess from text

Motivation: Understanding key biological processes (bioprocesses) and their relationships with constituent biological entities and pharmaceutical agents is crucial for drug design and discovery. One way to harvest such information is searching the literature. However, bioprocesses are difficult to capture because they may occur in text in a variety of textual expressions. Moreover, a bioprocess is often composed of a series of bioevents, where a bioevent denotes changes to one or a group of cells involved in the bioprocess. Such bioevents are often used to refer to bioprocesses in text, which current techniques, relying solely on specialized lexicons, struggle to find.Results: This article presents a range of methods for finding bioprocess terms and events. To facilitate the study, we built a gold standard corpus in which terms and events related to angiogenesis, a key biological process of the growth of new blood vessels, were annotated. Statistics of the annotated corpus revealed that over 36% of the text expressions that referred to angiogenesis appeared as events. The proposed methods respectively employed domain-specific vocabularies, a manually annotated corpus and unstructured domain-specific documents. Evaluation results showed that, while a supervised machine-learning model yielded the best precision, recall and F1 scores, the other methods achieved reasonable performance and less cost to develop.Availability: The angiogenesis vocabularies, gold standard corpus, annotation guidelines and software described in this article are available at http://text0.mib.man.ac.uk/~mbassxw2/angiogenesis/Contact: xinglong.wang@gmail.com

Design and use of the Simple Event Model (SEM)

Events have become central elements in the representation of data from domains such as history, cultural heritage, multimedia and geography. The Simple Event Model (SEM) is created to model events in these various domains, without making assumptions about the domain-specific vocabularies used. SEM is designed with a minimum of semantic commitment to guarantee maximal interoperability. In this paper, we discuss the general requirements of an event model for Web data and give examples from two use cases: historic events and events in the maritime safety and security domain. The advantages and disadvantages of several existing event models are discussed in the context of the historic example. We discuss the design decisions underlying SEM. SEM is coupled with a Prolog API that enables users to create instances of events without going into the details of the implementation of the model. By a tight coupling to existing Prolog packages, the API facilitates easy integration of event instances to Linked Open Data. We illustrate use of the API with examples from the maritime domain.

Design and use of the Simple Event Model (SEM)

Events have become central elements in the representation of data from domains such as history, cultural heritage, multimedia and geography. The Simple Event Model (SEM) is created to model events in these various domains, without making assumptions about the domain-specific vocabularies used. SEM is designed with a minimum of semantic commitment to guarantee maximal interoperability. In this paper, we discuss the general requirements of an event model for web data and give examples from two use cases: historic events and events in the maritime safety and security domain. The advantages and disadvantages of several existing event models are discussed in the context of the historic example. We discuss the design decisions underlying SEM. SEM is coupled with a Prolog API that enables users to create instances of events without going into the details of the implementation of the model. By a tight coupling to existing Prolog packages, the API facilitates easy integration of event instances to Linked Open Data. We illustrate use of the API with examples from the maritime domain.

How to Extract Domain-Specific Vocabulary in ESP

The purpose of current paper is to demonstrate how to extract domain-specific vocabulary (DSV) in English for Specific Purposes (ESP) fields such as...

A Method for the Automated, Reliable Retrieval of Publication-Citation Records

BackgroundPublication records and citation indices often are used to evaluate academic performance. For this reason, obtaining or computing them accurately is important. This can be difficult, largely due to a lack of complete knowledge of an individual's publication list and/or lack of time available to manually obtain or construct the publication-citation record. While online publication search engines have somewhat addressed these problems, using raw search results can yield inaccurate estimates of publication-citation records and citation indices.MethodologyIn this paper, we present a new, automated method that produces estimates of an individual's publication-citation record from an individual's name and a set of domain-specific vocabulary that may occur in the individual's publication titles. Because this vocabulary can be harvested directly from a research web page or online (partial) publication list, our method delivers an easy way to obtain estimates of a publication-citation record and the relevant citation indices. Our method works by applying a series of stringent name and content filters to the raw publication search results returned by an online publication search engine. In this paper, our method is run using Google Scholar, but the underlying filters can be easily applied to any existing publication search engine. When compared against a manually constructed data set of individuals and their publication-citation records, our method provides significant improvements over raw search results. The estimated publication-citation records returned by our method have an average sensitivity of and specificity of (in contrast to raw search result specificity of less than 10%). When citation indices are computed using these records, the estimated indices are within of the true value, compared to raw search results which have overestimates of, on average, .ConclusionsThese results confirm that our method provides significantly improved estimates over raw search results, and these can either be used directly for large-scale (departmental or university) analysis or further refined manually to quickly give accurate publication-citation records.

The Dynamics of Collaborative Tagging: An Analysis of Tag Vocabulary Application in Knowledge Representation, Discovery and Retrieval

This study investigates the contribution of collaborative tagging to the design of user-driven vocabularies in knowledge management systems (KMS). Three metrics, tag growth, tag reuse, and tag discrimination, were used to examine the evolution of the tagging vocabulary of the knowledge management community of interest in CiteULike over a three-year period. Results indicate a steady decrease in the number of unique tags over the four years, suggesting an increasing stability in the community vocabulary over time and the establishment of domain-specific vocabulary. Members reused each others' tags over time and exhibited increasingly collaborative tagging behaviour. Tag discrimination was high, with 4.11 distinct articles per tag. The stable and discriminatory nature of the community's tags suggests that collaborative tagging may serve as a useful resource for vocabulary choice or maintenance by KMS managers.

Classifier Calibration for Multi-Domain Sentiment Classification

Textual sentiment classifiers classify texts into a fixed number of affective classes, such as positive, negative or neutral sentiment, or subjective versus objective information. It has been observed that sentiment classifiers suffer from a lack of generalization capability: a classifier trained on a certain domain generally performs worse on data from another domain. This phenomenon has been attributed to domain-specific affective vocabulary. In this paper, we propose a voting-based thresholding approach, which calibrates a number of existing single-domain classifiers with respect to sentiment data from a new domain. The approach presupposes only a small amount of annotated data from the new domain. We evaluate three criteria for estimating thresholds, and discuss the ramifications of these criteria for the trade-off between classifier performance and manual annotation effort.

Analyzing architectural styles

The backbone of most software architectures and component integration frameworks is one or more architectural styles that provide a domain-specific design vocabulary and a set of constraints on how that vocabulary is used. Today’s architectural styles are increasingly complex, involving rich vocabularies and numerous constraints. Hence, designing a sound and appropriate style becomes an intellectually challenging activity. Unfortunately, although there are numerous tools to help in the analysis of architectures for individual systems, relatively less work has been done on tools to help in the design of architectural styles. In this paper we address this gap by showing how to map an architectural style, expressed formally in an architectural description language, into a relational model that can then be checked for properties, such as whether a style is consistent, whether a style satisfies some predicates over its architectural structure, and whether two styles are compatible for composition.

Towards automated processing of clinical Finnish: Sublanguage analysis and a rule-based parser

Introduction In this paper, we present steps taken towards more efficient automated processing of clinical Finnish, focusing on daily nursing notes in a Finnish Intensive Care Unit (ICU). First, we analyze ICU Finnish as a sublanguage, identifying its specific features facilitating, for example, the development of a specialized syntactic analyser. The identified features include frequent omission of finite verbs, limitations in allowed syntactic structures, and domain-specific vocabulary. Second, we develop a formal grammar and a parser for ICU Finnish, thus providing better tools for the development of further applications in the clinical domain. Methods The grammar is implemented in the LKB system in a typed feature structure formalism. The lexicon is automatically generated based on the output of the FinTWOL morphological analyzer adapted to the clinical domain. As an additional experiment, we study the effect of using Finnish constraint grammar to reduce the size of the lexicon. The parser construction thus makes efficient use of existing resources for Finnish. Results The grammar currently covers 76.6% of ICU Finnish sentences, producing highly accurate best-parse analyzes with F-score of 91.1%. We find that building a parser for the highly specialized domain sublanguage is not only feasible, but also surprisingly efficient, given an existing morphological analyzer with broad vocabulary coverage. The resulting parser enables a deeper analysis of the text than was previously possible.

Terminologies for text-mining; an experiment in the lipoprotein metabolism domain.

BackgroundThe engineering of ontologies, especially with a view to a text-mining use, is still a new research field. There does not yet exist a well-defined theory and technology for ontology construction. Many of the ontology design steps remain manual and are based on personal experience and intuition. However, there exist a few efforts on automatic construction of ontologies in the form of extracted lists of terms and relations between them.ResultsWe share experience acquired during the manual development of a lipoprotein metabolism ontology (LMO) to be used for text-mining. We compare the manually created ontology terms with the automatically derived terminology from four different automatic term recognition (ATR) methods. The top 50 predicted terms contain up to 89% relevant terms. For the top 1000 terms the best method still generates 51% relevant terms. In a corpus of 3066 documents 53% of LMO terms are contained and 38% can be generated with one of the methods.ConclusionsGiven high precision, automatic methods can help decrease development time and provide significant support for the identification of domain-specific vocabulary. The coverage of the domain vocabulary depends strongly on the underlying documents. Ontology development for text mining should be performed in a semi-automatic way; taking ATR results as input and following the guidelines we described.AvailabilityThe TFIDF term recognition is available as Web Service, described at

Knowledge networks in the age of the Semantic Web

The Web has become the major medium for various communities to share their knowledge. To this end, it provides an optimal environment for knowledge networks. The web offers global connectivity that is virtually instantaneous, and whose resources and documents can easily be indexed for easy searching. In the coupled realms of biomedical research and healthcare, this has become especially important where today many thousands of communities already exist that connect across academia, hospitals and industry. These communities also rely on several forms of knowledge assets, including publications, experimental data, domain-specific vocabularies and policies. Web-based communities will be one of the earlier beneficiaries of the emerging Semantic Web. With the new standards and technologies of the Semantic Web, effective utilization of knowledge networks will expand profoundly, fostering new levels of innovation and knowledge.