Rebecca Green

Bio: Rebecca Green is an academic researcher from OCLC. The author has contributed to research in topics: WordNet & Semantic similarity. The author has an hindex of 15, co-authored 34 publications receiving 1080 citations. Previous affiliations of Rebecca Green include University of Maryland, College Park.

The semantics of relationships : an interdisciplinary perspective

Abstract: Introduction * List of Contributors * Part I: Types of Relationships. 1. Hyponymy and Its Varieties. 2. On the Semantics of Troponymy * 3. Meronymic Relationships: From Classical Mereology to Complex Part-Whole Relations. 4. The Many Facets of the Cause-Effect Relation * Part II: Relationships in Knowledge Representation and Reasoning. 5. Internally-Structured Conceptual Models in Cognitive Semantics.6. Comparing Sets of Semantic Relations in Ontologies. 7. Identity and Subsumption. 8. Logic of Relationships * Part III: Applications of Relationships. 9. Thesaural Relations in Information Retrieval. 10. Identifying Semantic Relations in Text for Information Retrieval and Information Extraction. 11. A Conceptual Framework for the Biomedical Domain. 12. Visual Analysis and Exploration of Relationships * Index.

Relationships in the Organization of Knowledge

Abstract: Introduction. Part I: Relationships in the Organization of Knowledge: Theoretical Background. 1. Relationships in the Organization of Knowledge: An Overview R. Green. 2. Bibliographic Relationships B.B. Tillett. 3. Thesaural Relationships S.G. Dextre Clarke. 4. Standards for Relationships between Subject Indexing Terms J.L. Milstead. 5. Relationships in Multilingual Thesauri M. Hudon. 6. Relationships among Knowledge Structures: Vocabulary Integration within a Subject Domain O. Bodenreider, C.A. Bean. 7. Relationships in Classificatory Structure and Meaning C. Beghtol. 8. Relevance Relationships C.A. Bean, R. Green. Part II: Relationships in the Organization of Knowledge: Systems. 9. Relationships in Library of Congress Subject Headings L.M. El-Hoshy. 10. The Art and Architecture Thesaurus: Controlling Relationships through Rules and Structure P. Molholt. 11. Relationships in Medical Subject Headings (MeSH) S.J. Nelson, et al. 12. Lateral Relationships in Multicultural, Multilingual Databases in the Spiritual and Religious Domains: The OM Information Service A. Neelameghan. 13. Relationships in Ranganathan's Colon Classification M.P. Satija. 14. Relationships in the Dewey Decimal Classification System J.S. Mitchell. Index.

Topical relevance relationships. I: why topic matching fails

Abstract: This is the first in a two-part series on topical relevance relationships. Part I presents conceptual background; Part II reports on a related empirical study. Since topicality is a major factor in relevance, it is crucial to identify the range of relationship types that occur between the topics of user needs and the topics of user needs and the topics of texts relevant to those needs. We have generally assumed—without particular warrant—that a single relationship type obtains, i.e., that the two topics match. Evidence from the analysis of recall failures, citation analysis, and knowledge synthesis suggests otherwise: topical relevance relationships are not limited to topic matching relationships; to the contrary, in certain circumstances they are quite likely not to be matching relationships. Relationships are one of the two fundamental components of human conceptual systems. Attempts to classify them usually accept a distinction between relationships that occur by virtue of the combination of component units (syntagmatic relationships) and relationships that are built into the language system (paradigmatic relationships). Given the variety of relationship types previously identified, empirical research is needed to determine the subset that actually account for topical relevance. © 1995 John Wiley & Sons, Inc.

Locating Sources in Humanities Scholarship: The Efficacy of following Bibliographic References

Abstract: Humanities scholars often locate literature sources by following bibliographic references from documents already known to them or to their colleagues. In contrast, they tend not to make regular use of formal bibliographic (for example, abstracting and indexing) tools. The soundness of this approach resides in the scholars' familiarity with the literature of their specialization, the role of the literature--especially primary sources--in humanities scholarship, the importance of peer influence, and vocabulary softness in the humanities. An evaluation of the humanist's preferred approach, based on following bibliographic references from documents known to be relevant to twelve scholarly humanities questions, revealed that the approach complements, and in some ways surpasses, more formal approaches by identifying relevant literature not covered by standard bibliographic tools and by providing a more appropriate level of analytical access than the formal bibliographic apparatus. The following of bibliographic...

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Handbook on Ontologies

Abstract: An ontology is a formal description of concepts and relationships that can exist for a community of human and/or machine agents. The notion of ontologies is crucial for the purpose of enabling knowledge sharing and reuse. The Handbook on Ontologies provides a comprehensive overview of the current status and future prospectives of the field of ontologies considering ontology languages, ontology engineering methods, example ontologies, infrastructures and technologies for ontologies, and how to bring this all into ontology-based infrastructures and applications that are among the best of their kind. The field of ontologies has tremendously developed and grown in the five years since the first edition of the "Handbook on Ontologies". Therefore, its revision includes 21 completely new chapters as well as a major re-working of 15 chapters transferred to this second edition.

IR evaluation methods for retrieving highly relevant documents

Abstract: This paper proposes evaluation methods based on the use of non-dichotomous relevance judgements in IR experiments It is argued that evaluation methods should credit IR methods for their ability to retrieve highly relevant documents This is desirable from the user point of view in modem large IR environments The proposed methods are (1) a novel application of P-R curves and average precision computations based on separate recall bases for documents of different degrees of relevance, and (2) two novel measures computing the cumulative gain the user obtains by examining the retrieval result up to a given ranked position We then demonstrate the use of these evaluation methods in a case study on the effectiveness of query types, based on combinations of query structures and expansion, in retrieving documents of various degrees of relevance The test was run with a best match retrieval system (In- Query I) in a text database consisting of newspaper articles The results indicate that the tested strong query structures are most effective in retrieving highly relevant documents The differences between the query types are practically essential and statistically significant More generally, the novel evaluation methods and the case demonstrate that non-dichotomous relevance assessments are applicable in IR experiments, may reveal interesting phenomena, and allow harder testing of IR methods

Content Analysis: A Flexible Methodology

Abstract: Content analysis is a highly fl exible research method that has been widely used in library and information science (LIS) studies with varying research goals and objectives. The research method is applied in qualitative, quantitative, and sometimes mixed modes of research frameworks and employs a wide range of analytical techniques to generate fi ndings and put them into context. This article characterizes content analysis as a systematic, rigorous approach to analyzing documents obtained or generated in the course of research. It briefl y describes the steps involved in content analysis, differentiates between quantitative and qualitative content analysis, and shows that content analysis serves the purposes of both quantitative research and qualitative research. The authors draw on selected LIS studies that have used content analysis to illustrate the concepts addressed in the article. The article also serves as a gateway to methodological books and articles that provide more detail about aspects of content analysis discussed only briefl y in the article.

Sweetening Ontologies with DOLCE

Abstract: In this paper we introduce the DOLCE upper level ontology, the first module of a Foundational Ontologies Library being developed within the WonderWeb project. DOLCE is presented here in an intuitive way; the reader should refer to the project deliverable for a detailed axiomatization. A comparison with WordNet's top-level taxonomy of nouns is also provided, which shows how DOLCE, used in addition to the OntoClean methodology, helps isolating and understanding some major WordNet?s semantic limitations. We suggest that such analysis could hopefully lead to an ?ontologically sweetened? WordNet, meant to be conceptually more rigorous, cognitively transparent, and efficiently exploitable in several applications.