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Mostafa, J.: Bessere Suchmaschinen für das Web (2006)
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- Content
- "Seit wenigen Jahren haben Suchmaschinen die Recherche im Internet revolutioniert. Statt in Büchereien zu gehen, um dort mühsam etwas nachzuschlagen, erhalten wir die gewünschten Dokumente heute mit ein paar Tastaturanschlägen und Mausklicks. »Googeln«, nach dem Namen der weltweit dominierenden Suchmaschine, ist zum Synonym für die Online-Recherche geworden. Künftig werden verbesserte Suchmaschinen die gewünschten Informationen sogar noch zielsicherer aufspüren. Die neuen Programme dringen dazu tiefer in die Online-Materie ein. Sie sortieren und präsentieren ihre Ergebnisse besser, und zur Optimierung der Suche merken sie sich die persönlichen Präferenzen der Nutzer, die sie in vorherigen Anfragen ermittelt haben. Zudem erweitern sie den inhaltlichen Horizont, da sie mehr leisten, als nur eingetippte Schlüsselwörter zu verarbeiten. Einige der neuen Systeme berücksichtigen automatisch, an welchem Ort die Anfrage gestellt wurde. Dadurch kann beispielsweise ein PDA (Personal Digital Assistant) über seine Funknetzverbindung das nächstgelegene Restaurant ausfindig machen. Auch Bilder spüren die neuen Suchmaschinen besser auf, indem sie Vorlagen mit ähnlichen, bereits abgespeicherten Mustern vergleichen. Sie können sogar den Namen eines Musikstücks herausfinden, wenn man ihnen nur ein paar Takte daraus vorsummt. Heutige Suchmaschinen basieren auf den Erkenntnissen aus dem Bereich des information retrieval (Wiederfinden von Information), mit dem sich Computerwissenschaftler schon seit über 50 Jahren befassen. Bereits 1966 schrieb Ben Ami Lipetz im Scientific American einen Artikel über das »Speichern und Wiederfinden von Information«. Damalige Systeme konnten freilich nur einfache Routine- und Büroanfragen bewältigen. Lipetz zog den hellsichtigen Schluss, dass größere Durchbrüche im information retrieval erst dann erreichbar sind, wenn Forscher die Informationsverarbeitung im menschlichen Gehirn besser verstanden haben und diese Erkenntnisse auf Computer übertragen. Zwar können Computer dabei auch heute noch nicht mit Menschen mithalten, aber sie berücksichtigen bereits weit besser die persönlichen Interessen, Gewohnheiten und Bedürfnisse ihrer Nutzer. Bevor wir uns neuen Entwicklungen bei den Suchmaschinen zuwenden, ist es hilfreich, sich ein Bild davon zu machen, wie die bisherigen funktionieren: Was genau ist passiert, wenn »Google« auf dem Bildschirm meldet, es habe in 0,32 Sekunden einige Milliarden Dokumente durchsucht? Es würde wesentlich länger dauern, wenn dabei die Schlüsselwörter der Anfrage nacheinander mit den Inhalten all dieser Webseiten verglichen werden müssten. Um lange Suchzeiten zu vermeiden, führen die Suchmaschinen viele ihrer Kernoperationen bereits lange vor dem Zeitpunkt der Nutzeranfrage aus.
- Date
- 31.12.1996 19:29:41
22. 1.2006 18:34:49
-
Seki, K.; Mostafa, J.: Gene ontology annotation as text categorization : an empirical study (2008)
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- Abstract
- Gene ontology (GO) consists of three structured controlled vocabularies, i.e., GO domains, developed for describing attributes of gene products, and its annotation is crucial to provide a common gateway to access different model organism databases. This paper explores an effective application of text categorization methods to this highly practical problem in biology. As a first step, we attempt to tackle the automatic GO annotation task posed in the Text Retrieval Conference (TREC) 2004 Genomics Track. Given a pair of genes and an article reference where the genes appear, the task simulates assigning GO domain codes. We approach the problem with careful consideration of the specialized terminology and pay special attention to various forms of gene synonyms, so as to exhaustively locate the occurrences of the target gene. We extract the words around the spotted gene occurrences and used them to represent the gene for GO domain code annotation. We regard the task as a text categorization problem and adopt a variant of kNN with supervised term weighting schemes, making our method among the top-performing systems in the TREC official evaluation. Furthermore, we investigate different feature selection policies in conjunction with the treatment of terms associated with negative instances. Our experiments reveal that round-robin feature space allocation with eliminating negative terms substantially improves performance as GO terms become specific.
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Mostafa, J.: Digital image representation and access (1994)
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- Source
- Annual review of information science and technology. 29(1994), S.91-135
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Lam, W.; Mostafa, J.: Modeling user interest shift using a Baysian approach (2001)
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- Date
- 29. 9.2001 13:58:28
-
Mostafa, J.; Dillon, A.: Design and evaluation of a user interface supporting multiple image query models (1996)
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- Abstract
- For effective access to images, the design of the database interface must be based on principles that match the actual querying needs of users. Analysis of this design problem reveals that the query language must support utilization of both visual and verbal clues. The ViewFinder interface, designed as a client to a database server, supports querying based on both types of clues. Presents details of ViewFinder design. Describes results of usability analysis performed on ViweFinder with a group of 18 users. High search success rates were achieved (greater than 80%) through both types of querying means (visual and verbal). Users generally used more verbal clues than visual clues in searches
-
Zhang, Y.; Wu, D.; Hagen, L.; Song, I.-Y.; Mostafa, J.; Oh, S.; Anderson, T.; Shah, C.; Bishop, B.W.; Hopfgartner, F.; Eckert, K.; Federer, L.; Saltz, J.S.: Data science curriculum in the iField (2023)
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- Date
- 12. 5.2023 14:29:42
-
Zhang, J.; Mostafa, J.; Tripathy, H.: Information retrieval by semantic analysis and visualization of the concept space of D-Lib® magazine (2002)
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- Abstract
- In this article we present a method for retrieving documents from a digital library through a visual interface based on automatically generated concepts. We used a vocabulary generation algorithm to generate a set of concepts for the digital library and a technique called the max-min distance technique to cluster them. Additionally, the concepts were visualized in a spring embedding graph layout to depict the semantic relationship among them. The resulting graph layout serves as an aid to users for retrieving documents. An online archive containing the contents of D-Lib Magazine from July 1995 to May 2002 was used to test the utility of an implemented retrieval and visualization system. We believe that the method developed and tested can be applied to many different domains to help users get a better understanding of online document collections and to minimize users' cognitive load during execution of search tasks. Over the past few years, the volume of information available through the World Wide Web has been expanding exponentially. Never has so much information been so readily available and shared among so many people. Unfortunately, the unstructured nature and huge volume of information accessible over networks have made it hard for users to sift through and find relevant information. To deal with this problem, information retrieval (IR) techniques have gained more intensive attention from both industrial and academic researchers. Numerous IR techniques have been developed to help deal with the information overload problem. These techniques concentrate on mathematical models and algorithms for retrieval. Popular IR models such as the Boolean model, the vector-space model, the probabilistic model and their variants are well established.
From the user's perspective, however, it is still difficult to use current information retrieval systems. Users frequently have problems expressing their information needs and translating those needs into queries. This is partly due to the fact that information needs cannot be expressed appropriately in systems terms. It is not unusual for users to input search terms that are different from the index terms information systems use. Various methods have been proposed to help users choose search terms and articulate queries. One widely used approach is to incorporate into the information system a thesaurus-like component that represents both the important concepts in a particular subject area and the semantic relationships among those concepts. Unfortunately, the development and use of thesauri is not without its own problems. The thesaurus employed in a specific information system has often been developed for a general subject area and needs significant enhancement to be tailored to the information system where it is to be used. This thesaurus development process, if done manually, is both time consuming and labor intensive. Usage of a thesaurus in searching is complex and may raise barriers for the user. For illustration purposes, let us consider two scenarios of thesaurus usage. In the first scenario the user inputs a search term and the thesaurus then displays a matching set of related terms. Without an overview of the thesaurus - and without the ability to see the matching terms in the context of other terms - it may be difficult to assess the quality of the related terms in order to select the correct term. In the second scenario the user browses the whole thesaurus, which is organized as in an alphabetically ordered list. The problem with this approach is that the list may be long, and neither does it show users the global semantic relationship among all the listed terms.
