Effectively Grouping Named Entities From Click- Through Data Into Clusters Of Generated Keywords1

Many studies show that named entities are closely related to users\u27 search behaviors, which brings increasing interest in studying named entities in search logs recently. This paper addresses the problem of forming fine grained semantic clusters of named entities within a broad domain such as “company”, and generating keywords for each cluster, which help users to interpret the embedded semantic information in the cluster. By exploring contexts, URLs and session IDs as features of named entities, a three-phase approach proposed in this paper first disambiguates named entities according to the features. Then it properly weights the features with a novel measurement, calculates the semantic similarity between named entities with the weighted feature space, and clusters named entities accordingly. After that, keywords for the clusters are generated using a text-oriented graph ranking algorithm. Each phase of the proposed approach solves problems that are not addressed in existing works, and experimental results obtained from a real click through data demonstrate the effectiveness of the proposed approach

Effectively Grouping Named Entities From Click-Through Data Into Clusters Of Generated Keywords1

Abstract Many studies show that named entities are closely related to users &apos

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Ph.DDOCTOR OF PHILOSOPH

BIM in the construction industry

En las últimas décadas, el término modelado de información de construcción (BIM) se ha mencionado en una amplia gama de esfuerzos de investigación de la construcción. BIM es una nueva solución para la recesión sin precedentes en la industria de la construcción, es decir, pérdida de productividad, escasez de mano de obra, sobrecostos y competitividad severa. La tecnología BIM proporciona muchos beneficios: detección rápida de conflictos de diseño, regulación automática de diseño algoritmo de verificación, visualización de realidad virtual/aumentada y entorno de trabajo de colaboración. BIM los expertos, así como los profesionales de la industria, enfatizan la importancia de las aplicaciones BIM en el campo de construcción. Dado el rápido desarrollo y adopción de BIM en la arquitectura, ingeniería, y construcción (AEC), están surgiendo nuevas tendencias relevantes para la investigación de BIM, siendo sumamente útil no sólo para los académicos sino también para los profesionales.In recent decades, the term building information modeling (BIM) has been mentioned in a wide range of construction research endeavors. BIM is a new solution for unprecedented recession in the construction industry, i.e., productivity loss, labor shortage, cost overrun, and severe competitiveness. BIM technology provides many benefits: prompt design clash detection, automatic deign regulatory check algorithm, augmented/virtual reality visualization, and collaboration work environment. BIM experts as well as industry practitioners are stressing the importance of BIM applications in the field of construction. Given the rapid development and adoption of BIM in the architecture, engineering, and construction (AEC) industry, new trends relevant to the research of BIM are emerging, being exceedingly helpful not only for academics but also for practitioners

Utilizing external resources for enriching information retrieval

Information retrieval (IR) seeks to support users in finding information relevant to their information needs. One obstacle for many IR algorithms to achieve better results in many IR tasks is that there is insufficient information available to enable relevant content to be identified. For example, users typically enter very short queries, in text-based image retrieval where textual annotations often describe the content of the images inadequately, or there is insufficient user log data for personalization of the search process. This thesis explores the problem of inadequate data in IR tasks. We propose methods for Enriching Information Retrieval (ENIR) which address various challenges relating to insufficient data in IR. Applying standard methods to address these problems can face unexpected challenges. For example, standard query expansion methods assume that the target collection contains sufficient data to be able to identify relevant terms to add to the original query to improve retrieval effectiveness. In the case of short documents, this assumption is not valid. One strategy to address this problem is document side expansion which has been largely overlooked in the past research. Similarly, topic modeling in personalized search often lacks the knowledge required to form adequate models leading to mismatch problems when trying to apply these models improve search. This thesis focuses on methods of ENIR for tasks affected by problems of insufficient data. To achieve ENIR, our overall solution is to include external resources for ENIR. This research focuses on developing methods for two typical ENIR tasks: text-based image retrieval and personalized web data search. In this research, the main relevant areas within existing IR research are relevance feedback and personalized modeling. ENIR is shown to be effective to augment existing knowledge in these classical areas. The areas of relevance feedback and personalized modeling are strongly correlated since user modeling and document modeling in personalized retrieval enrich the data from both sides of the query and document, which is similar to query and document expansion in relevance feedback. Enriching IR is the key challenge in these areas for IR. By addressing these two research areas, this thesis provides a prototype for an external resource based search solution. The experimental results show external resources can play a key role in enriching IR