Presentation at the beginning of the workshop, given to set the tone and outline issues key to the event. [jtt]

Social tagging, as a particular type of indexing, has thrown into question the nature of indexing. Is it a democratic process? Can we all benefit from user-created tags? What about the value added by professionals? Employing an evolving framework analysis, this paper addresses the question: what is next for indexing? Comparing social tagging and subject cataloguing; this paper identifies the points of similarity and difference that obtain between these two kinds of information organization frameworks. The subsequent comparative analysis of the parts of these frameworks points to the nature of indexing as an authored, personal, situational, and referential act, where differences in discursive placement divide these two species. Furthermore, this act is contingent on implicit and explicit understanding of purpose and tools available. This analysis allows us to outline desiderata for the next steps in indexing.

Proceedings 17th Workshop of the American Society for Information Science and Technology Special Interest Group in Classification Research / Presentation at the beginning of the workshop, given to set the tone and outline issues key to the event. [jtt]

Faithful aggregated accounts of the advancement of science are invaluable for those setting scientific policy as well as scholars of the history of science. As science develops the scholarly communityiÌ s determination of the accepted knowledge undergoes shifts. Within medicine these shifts include our understanding of what can cause disease and what defines specific disease entities. Shifts in accepted medical knowledge are captured in the medical literature. The Index Catalogue of the Library of the Surgeon GeneraliÌ s Office, United States Army, published from 1880 -1961, is an extremely large index to medical literature. The newly digitized form of this index, referred to as the IndexCat, allows us a way to generate faithful accounts of the development of medical science during the late nineteenth- and early twentieth-centuries. My data looks at shifts within the IndexCat surrounding three disease entities: syphilis, Huntington's chorea, and beriberi, and their interactions with two disease causation theories: germ and hereditary, from 1880-1930. Temporal changes in the prominent subject heading words and title words within the literature of these diseases and disease theories corroborate qualitative accounts of this same literature, which reports the complex and sometimes oblique process of knowledge accretion. Although preliminary, my results indicate that the IndexCat is a valuable tool for studying the development of medical knowledge.

Artificial Intelligence Lab, Department of MIS, University of Arizona / Digital libraries of geo-spatial multimedia content are currently deficient in providing fuzzy, concept-based retrieval mechanisms to users. The main challenge is that indexing and thesaurus creation are extremely laborintensive processes for text documents and especially for images. Recently, 800,000 declassified satellite photographs were made available by the United States Geological Survey. Additionally, millions of satellite and aerial photographs are archived in national and local map libraries. Such enormous collections make human indexing and thesaurus generation methods impossible to utilize. In this article we propose a scalable method to automatically generate visual thesauri of large collections of geo-spatial media using fuzzy, unsupervised machine-learning techniques.

Pagrindinis šio darbo tikslas yra išnagrinėti judančių objektų indeksavimo duomenų bazėse problemas, siūlomus sprendimus bei palyginti keleto iš jų veiksmingumą. Įvairiais pjūviais buvo lyginami praeities duomenis indeksuojantys R ir iš jo išvesti STR bei TB medžiai. Eksperimentai atlikti naudojant sugeneruotus judančių objektų duomenis. Gauti rezultatai parodė, kad indeksų veiksmingas priklauso nuo tam tikrų sąlygų ir aplinkybių, kuriomis jie naudojami. / Over the past few years, there has been a continuous improvement in the wireless communications and the positioning technologies. As a result, tracking the changing positions of continuously moving objects is becoming increasingly feasible and necessary. Databases that deal with objects that change their location and/or shape over time are called spatio-temporal databases. Traditional database approaches for effective information retrieval cannot be used as the moving objects database is highly dynamic. A need for so called spatio-temporal indexing techniques comes to scene. Mainly, by the problem they are addressed to, indices are divided into two groups: a) indexing the past and b) indexing the current and predicted future positions. Also the have been proposed techniques covering both problems. This work is a survey for well known and used indices. Also there is a performance comparison between several past indexing methods. STR Tree, TB Tree and the predecessor of many indices, the R Tree are compared in various aspects using generated datasets of simulated objects movement.

As a new topic in text mining, novelty detection is a natural extension of information retrieval systems, or search engines. Aiming at refining raw search results by filtering out old news and saving only the novel messages, it saves modern people from the nightmare of information overload. One of the difficulties in novelty detection is the inherent ambiguity of language, which is the carrier of information. Among the sources of ambiguity, synonymy proves to be a notable factor. To address this issue, previous studies mainly employed WordNet, a lexical database which can be perceived as a thesaurus. Rather than borrowing a dictionary, we proposed a statistical approach employing Latent Semantic Indexing (LSI) to learn semantic relationship automatically with the help of language resources. To apply LSI which involves matrix factorization, an immediate problem is that the dataset in novelty detection is dynamic and changing constantly. As an imitation of real-world scenario, texts are ranked in chronological order and examined one by one. Each text is only compared with those having appeared earlier, while later ones remain unknown. As a result, the data matrix starts as a one-row vector representing the first report, and has a new row added at the bottom every time we read a new document. Such a changing dataset makes it hard to employ matrix methods directly. Although LSI has long been acknowledged as an effective text mining method when considering semantic structure, it has never been used in novelty detection, nor have other statistical treatments. We tried to change this situation by introducing external text source to build the latent semantic space, onto which the incoming news vectors were projected. We used the Reuters-21578 dataset and the TREC data as sources of latent semantic information. Topics were divided into years and types in order to take the differences between them into account. Results showed that LSI, though very effective in traditional information retrieval tasks, had only a slight improvement to the performances for some data types. The extent of improvement depended on the similarity between news data and external information. A probing into the co-occurrence matrix attributed such a limited performance to the unique features of microblogs. Their short sentence lengths and restricted dictionary made it very hard to recover and exploit latent semantic information via traditional data structure.

Digital multimedia content production and the amount of content present all over the world have exploded in the recent years. The consequences of this fact can be observed everywhere in many different forms, to exemplify, huge digital video archives of broadcasting companies, commercial image archives, virtual museums, etc. In order for these sources to be useful and accessible, this technological advance must be accompanied by the effective techniques of indexing and retrieval. The most effective way of indexing is the one providing a basis for retrieval in terms of semantic concepts, upon which ordinary users of multimedia databases base their queries. On the other hand, semantic classification of images using low-level features is a challenging problem. Combining experts with different classifier structures, trained by MPEG-7low-level color and texture descriptors, is examined as a solution alternative. For combining different classifiers and features, advanced decision mechanisms are proposed, which utilize basic expert combination strategies in different settings. Each of these decision mechanisms, namely Single Feature Combination (SFC), Multiple Feature Direct Combination (MFDC), and Multiple Feature Cascaded Combination (MFCC) enjoy significant classification performance improvements over single experts. Simulations are conducted on eight different visual semantic classes, resulting in accuracy improvements between 3.5-6.5%, when they are compared with the best performance of single expert systems.

Thesis (Ph.D.)--Drexel University, 2009. / Includes abstract and vita. Includes bibliographical references (leaves 115-121).

Thesis (Ph. D.)--Lehigh University, 2004. / Includes vita. Includes bibliographical references (leaves 94-103).