1,856 research outputs found
Business Process Retrieval Based on Behavioral Semantics
This paper develops a framework for retrieving business processes considering search requirements based on behavioral semantics properties; it presents a framework called "BeMantics" for retrieving business processes based on structural, linguistics, and behavioral semantics properties. The relevance of the framework is evaluated retrieving business processes from a repository, and collecting a set of relevant business processes manually issued by human judges. The "BeMantics" framework scored high precision values (0.717) but low recall values (0.558), which implies that even when the framework avoided false negatives, it prone to false positives. The highest pre- cision value was scored in the linguistic criterion showing that using semantic inference in the tasks comparison allowed to reduce around 23.6 % the number of false positives. Using semantic inference to compare tasks of business processes can improve the precision; but if the ontologies are from narrow and specific domains, they limit the semantic expressiveness obtained with ontologies from more general domains. Regarding the perform- ance, it can be improved by using a filter phase which indexes business processes taking into account behavioral semantics propertie
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Local search: A guide for the information retrieval practitioner
There are a number of combinatorial optimisation problems in information retrieval in which the use of local search methods are worthwhile. The purpose of this paper is to show how local search can be used to solve some well known tasks in information retrieval (IR), how previous research in the field is piecemeal, bereft of a structure and methodologically flawed, and to suggest more rigorous ways of applying local search methods to solve IR problems. We provide a query based taxonomy for analysing the use of local search in IR tasks and an overview of issues such as fitness functions, statistical significance and test collections when conducting experiments on combinatorial optimisation problems. The paper gives a guide on the pitfalls and problems for IR practitioners who wish to use local search to solve their research issues, and gives practical advice on the use of such methods. The query based taxonomy is a novel structure which can be used by the IR practitioner in order to examine the use of local search in IR
Business Process Retrieval Based on Behavioral Semantics
This paper develops a framework for retrieving business processes considering search requirements based on behavioral semantics properties; it presents a framework called âBeManticsâ for retrieving business processes based on structural, linguistics, and behavioral semantics properties. The relevance of the framework is evaluated retrieving business processes from a repository, and collecting a set of relevant business processes manually issued by human judges. The âBeManticsâ framework scored high precision values (0.717) but low recall values (0.558), which implies that even when the framework avoided false negatives, it prone to false positives. The highest pre- cision value was scored in the linguistic criterion showing that using semantic inference in the tasks comparison allowed to reduce around 23.6 % the number of false positives. Using semantic inference to compare tasks of business processes can improve the precision; but if the ontologies are from narrow and specific domains, they limit the semantic expressiveness obtained with ontologies from more general domains. Regarding the perform- ance, it can be improved by using a filter phase which indexes business processes taking into account behavioral semantics properties
From Frequency to Meaning: Vector Space Models of Semantics
Computers understand very little of the meaning of human language. This
profoundly limits our ability to give instructions to computers, the ability of
computers to explain their actions to us, and the ability of computers to
analyse and process text. Vector space models (VSMs) of semantics are beginning
to address these limits. This paper surveys the use of VSMs for semantic
processing of text. We organize the literature on VSMs according to the
structure of the matrix in a VSM. There are currently three broad classes of
VSMs, based on term-document, word-context, and pair-pattern matrices, yielding
three classes of applications. We survey a broad range of applications in these
three categories and we take a detailed look at a specific open source project
in each category. Our goal in this survey is to show the breadth of
applications of VSMs for semantics, to provide a new perspective on VSMs for
those who are already familiar with the area, and to provide pointers into the
literature for those who are less familiar with the field
Investigating user experience and bias mitigation of the multi-modal retrieval of historical data
Decolonisation has raised the discussion of technology having the responsibility of presenting multiple perspectives to users. This is specifically relevant to African precolonial heritage artefact data, where the data contains the bias of the curators of the artefacts and there are primary concerns surrounding the social responsibility of these systems. Historians have argued that common information retrieval algorithms may further bias results presented to users. While research for mitigating bias in information retrieval is steered in the direction of artificial intelligence and automation, an often-neglected approach is that of user-control. User-control has proven to be beneficial in other research areas and is strongly aligned with the core principles of decolonisation. Thus, the effects on user experience, bias mitigation, and retrieval effectiveness from the addition of user-control and algorithmic variation to a multimodal information retrieval system containing precolonial African heritage data was investigated in this study. This was done by conducting two experiments: 1) an experiment to provide a baseline offline evaluation of various algorithms for text and image retrieval and 2) an experiment to investigate the user experience with a retrieval system that allowed them to compare algorithms. In the first experiment, the differences in retrieval effectiveness between colour-based pre-processing algorithms, shape-based preprocessing algorithms, and pre-processing algorithms based on a combination of colour- and shape-detection, was explored. The differences in retrieval effectiveness between stemming, stopword removal and synonym query expansion was also evaluated for text retrieval. In the second experiment, the manner in which users experience bias in the context of common information retrieval algorithms for both the textual and image data that are available in typical historical archives was explored. Users were presented with the results generated by multiple algorithmic variations, in a variety of different result formats, and using a variety of different search methods, affording them the opportunity to decide what they deem provides them with a more relevant set of results. The results of the study show that algorithmic variation can lead to significantly improved retrieval performance with respect to image-based retrieval. The results also show that users potentially prefer shape-based image algorithms rather than colour-based image algorithms, and, that shape-based image algorithms can lead to significantly improved retrieval of historical data. The results also show that users have justifiable preferences for multimodal query and result formats to improve user experience and that users believe they can control bias using algorithmic variatio
Colombus: providing personalized recommendations for drifting user interests
The query formulationg process if often a problematic activity due to the cognitive load that it imposes to users. This issue is further ampliïŹed by the uncertainty of searchers with regards to their searching needs and their lack of training on eïŹective searching techniques. Also, given the tremendous growth of the world wide web, the amount of imformation users ïŹnd during their daily search episodes is often overwhelming. Unfortunatelly, web search engines do not follow the trends and advancements in this area, while real personalization features have yet to appear. As a result, keeping up-to-date with recent information about our personal interests is a time-consuming task. Also, often these information requirements change by sliding into new topics. In this case, the rate of change can be sudden and abrupt, or more gradual.
Taking into account all these aspects, we believe that an information assistant, a proïŹle-aware tool capable of adapting to usersâ evolving needs and aiding them to keep track of their personal data, can greatly help them in this endeavor. Information gathering from a combination of explicit and implicit feedback could allow such systems to detect their search requirements and present additional information, with the least possible eïŹort from them.
In this paper, we describe the design, development and evaluation of Colombus, a system aiming to meet individual needs of the searchers. The systemâs goal is to pro-actively fetch and present relevant, high quality documents on regular basis. Based entirely on implicit feedback gathering, our system concentrates on detecting drifts in user interests and accomodate them eïŹectively in their proïŹles with no additional interaction from their side.
Current methodologies in information retrieval do not support the evaluation of such systems and techniques. Lab-based experiments can be carried out in large batches but their accuracy often questione. On the other hand, user studies are much more accurate, but setting up a user base for large-scale experiments is often not feasible. We have designed a hybrid evaluation methodology that combines large sets of lab experiments based on searcher simulations together with user experiments, where ïŹfteen searchers used the system regularly for 15 days. At the ïŹrst stage, the simulation experiments were aiming attuning Colombus, while the various component evaluation and results gathering was carried out at the second stage, throughout the user study. A baseline system was also employed in order to make a direct comparison of Colombus against a current web search engine. The evaluation results illustrate that the Personalized Information Assistant is eïŹective in capturing and satisfying usersâ evolving information needs and providing additional information on their behalf
Creation Account and Scriptures: A Linguistic Meta-Function Analysis of Genesis Chapter One
The study is a linguistically-based analysis of the biblical account of creation. It examines the trilogy of power, forceful use of words, and transcendence in Genesis 1. By drawing on critical discourse analysis and meta-function from Halliday's systemic functional linguistics, the article analyses transitivity patterns and discursive strategies that articulate power and the responsiveness of words to their environment especially when deliberately and forcefully deployed. It also examines the peculiar use of cohesion, semantic opposition, ordinal adjectives, and symbolism as discourse markers. The article further illustrates how the intentional use of words impacted nature and the transcendental history of human existence. In other words, language is captured as a life inherent organism and a force that births the universe. More specifically, the study demonstrates that God's creative power is His Word and that existence is an expression of Godâs deliberate use of words
Evaluation Methodologies for Visual Information Retrieval and Annotation
Die automatisierte Evaluation von Informations-Retrieval-Systemen erlaubt
Performanz und QualitÀt der Informationsgewinnung zu bewerten. Bereits in
den 60er Jahren wurden erste Methodologien fĂŒr die system-basierte
Evaluation aufgestellt und in den Cranfield Experimenten ĂŒberprĂŒft.
Heutzutage gehören Evaluation, Test und QualitÀtsbewertung zu einem aktiven
Forschungsfeld mit erfolgreichen Evaluationskampagnen und etablierten
Methoden. Evaluationsmethoden fanden zunÀchst in der Bewertung von
Textanalyse-Systemen Anwendung. Mit dem rasanten Voranschreiten der
Digitalisierung wurden diese Methoden sukzessive auf die Evaluation von
Multimediaanalyse-Systeme ĂŒbertragen. Dies geschah hĂ€ufig, ohne die
Evaluationsmethoden in Frage zu stellen oder sie an die verÀnderten
Gegebenheiten der Multimediaanalyse anzupassen. Diese Arbeit beschÀftigt
sich mit der system-basierten Evaluation von Indizierungssystemen fĂŒr
Bildkollektionen. Sie adressiert drei Problemstellungen der Evaluation von
Annotationen: Nutzeranforderungen fĂŒr das Suchen und Verschlagworten von
Bildern, EvaluationsmaĂe fĂŒr die QualitĂ€tsbewertung von
Indizierungssystemen und Anforderungen an die Erstellung visueller
Testkollektionen. Am Beispiel der Evaluation automatisierter
Photo-Annotationsverfahren werden relevante Konzepte mit Bezug zu
Nutzeranforderungen diskutiert, Möglichkeiten zur Erstellung einer
zuverlÀssigen Ground Truth bei geringem Kosten- und Zeitaufwand vorgestellt
und EvaluationsmaĂe zur QualitĂ€tsbewertung eingefĂŒhrt, analysiert und
experimentell verglichen. Traditionelle MaĂe zur Ermittlung der Performanz
werden in vier Dimensionen klassifiziert. EvaluationsmaĂe vergeben
ĂŒblicherweise binĂ€re Kosten fĂŒr korrekte und falsche Annotationen. Diese
Annahme steht im Widerspruch zu der Natur von Bildkonzepten. Das gemeinsame
Auftreten von Bildkonzepten bestimmt ihren semantischen Zusammenhang und
von daher sollten diese auch im Zusammenhang auf ihre Richtigkeit hin
ĂŒberprĂŒft werden. In dieser Arbeit wird aufgezeigt, wie semantische
Ăhnlichkeiten visueller Konzepte automatisiert abgeschĂ€tzt und in den
Evaluationsprozess eingebracht werden können. Die Ergebnisse der Arbeit
inkludieren ein Nutzermodell fĂŒr die konzeptbasierte Suche von Bildern,
eine vollstĂ€ndig bewertete Testkollektion und neue EvaluationsmaĂe fĂŒr die
anforderungsgerechte QualitÀtsbeurteilung von Bildanalysesystemen.Performance assessment plays a major role in the research on Information
Retrieval (IR) systems. Starting with the Cranfield experiments in the
early 60ies, methodologies for the system-based performance assessment
emerged and established themselves, resulting in an active research field
with a number of successful benchmarking activities. With the rise of the
digital age, procedures of text retrieval evaluation were often transferred
to multimedia retrieval evaluation without questioning their direct
applicability. This thesis investigates the problem of system-based
performance assessment of annotation approaches in generic image
collections. It addresses three important parts of annotation evaluation,
namely user requirements for the retrieval of annotated visual media,
performance measures for multi-label evaluation, and visual test
collections. Using the example of multi-label image annotation evaluation,
I discuss which concepts to employ for indexing, how to obtain a reliable
ground truth to moderate costs, and which evaluation measures are
appropriate. This is accompanied by a thorough analysis of related work on
system-based performance assessment in Visual Information Retrieval (VIR).
Traditional performance measures are classified into four dimensions and
investigated according to their appropriateness for visual annotation
evaluation. One of the main ideas in this thesis adheres to the common
assumption on the binary nature of the score prediction dimension in
annotation evaluation. However, the predicted concepts and the set of true
indexed concepts interrelate with each other. This work will show how to
utilise these semantic relationships for a fine-grained evaluation
scenario. Outcomes of this thesis result in a user model for concept-based
image retrieval, a fully assessed image annotation test collection, and a
number of novel performance measures for image annotation evaluation
Linked Data Supported Information Retrieval
Um Inhalte im World Wide Web ausfindig zu machen, sind Suchmaschienen nicht mehr wegzudenken. Semantic Web und Linked Data Technologien ermöglichen ein detaillierteres und eindeutiges Strukturieren der Inhalte und erlauben vollkommen neue Herangehensweisen an die Lösung von Information Retrieval Problemen. Diese Arbeit befasst sich mit den Möglichkeiten, wie Information Retrieval Anwendungen von der Einbeziehung von Linked Data profitieren können. Neue Methoden der computer-gestĂŒtzten semantischen Textanalyse, semantischen Suche, Informationspriorisierung und -visualisierung werden vorgestellt und umfassend evaluiert. Dabei werden Linked Data Ressourcen und ihre Beziehungen in die Verfahren integriert, um eine Steigerung der EffektivitĂ€t der Verfahren bzw. ihrer Benutzerfreundlichkeit zu erzielen. ZunĂ€chst wird eine EinfĂŒhrung in die Grundlagen des Information Retrieval und Linked Data gegeben. AnschlieĂend werden neue manuelle und automatisierte Verfahren zum semantischen Annotieren von Dokumenten durch deren VerknĂŒpfung mit Linked Data Ressourcen vorgestellt (Entity Linking). Eine umfassende Evaluation der Verfahren wird durchgefĂŒhrt und das zu Grunde liegende Evaluationssystem umfangreich verbessert. Aufbauend auf den Annotationsverfahren werden zwei neue Retrievalmodelle zur semantischen Suche vorgestellt und evaluiert. Die Verfahren basieren auf dem generalisierten Vektorraummodell und beziehen die semantische Ăhnlichkeit anhand von taxonomie-basierten Beziehungen der Linked Data Ressourcen in Dokumenten und Suchanfragen in die Berechnung der Suchergebnisrangfolge ein. Mit dem Ziel die Berechnung von semantischer Ăhnlichkeit weiter zu verfeinern, wird ein Verfahren zur Priorisierung von Linked Data Ressourcen vorgestellt und evaluiert. Darauf aufbauend werden Visualisierungstechniken aufgezeigt mit dem Ziel, die Explorierbarkeit und Navigierbarkeit innerhalb eines semantisch annotierten Dokumentenkorpus zu verbessern. HierfĂŒr werden zwei Anwendungen prĂ€sentiert. Zum einen eine Linked Data basierte explorative Erweiterung als ErgĂ€nzung zu einer traditionellen schlĂŒsselwort-basierten Suchmaschine, zum anderen ein Linked Data basiertes Empfehlungssystem
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