Extracting semantic knowledge from web context for multimedia IR: a taxonomy, survey and challenges

Bracamonte, Teresa; Bustos, Benjamín; Poblete, Bárbara; Schreck, Tobias

doi:10.1007/s11042-017-4997-y

Cited by 7 publications

(2 citation statements)

References 52 publications

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“…However, in the process of applying context knowledge, several challenges and considerations need attention [28]. The accuracy and reliability of the application's responses or recommendations are crucial, necessitating rigorous validation and testing processes.…”

Section: Inference Applicationmentioning

confidence: 99%

Towards Semantic Smart Cities: A Study on the Conceptualization and Implementation of Semantic Context Inference Systems

Lee,

Song

2023

Sensors

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Smart cities provide integrated management and operation of urban data emerging within a city, supplying the infrastructure for smart city services and resolving various urban challenges. Nevertheless, cities continue to grapple with substantial issues, such as contagious diseases and terrorism, that pose severe financial and human risks. These problems sporadically arise in various locales, and current smart city frameworks lack the capability to autonomously identify and address these issues. The challenge intensifies especially when trying to recognize and respond to unprecedented problems. The primary objective of this research is to predict potential urban issues and support their resolution proactively. To achieve this, our system makes use of semantic reasoning to understand the ongoing situations within the city. In this process, the 5W1H principles serve as inference rules, guiding the extraction and consolidation of context. Firstly, utilizing domain-specific annotation templates, we craft a semantic graph by amalgamating information from various sources available in the city, such as municipal public data and IoT platforms. Subsequently, the system autonomously infers and accumulates contexts of situations occurring in the city using 5W1H-based reasoning. As a result, the accumulated contexts allow for inferring potential urban problems by identifying repeated disruptions in city services at specific times or locations and establishing connections among them. The main contribution of this paper lies in proposing a comprehensive conceptual model for the suggested system and presenting actual implementation cases and applicable use cases. These contributions facilitate awareness among city administrators and citizens within a smart city regarding potential problem-prone areas or times, thereby aiding in the preemptive identification and mitigation of urban challenges.

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Section: Inference Applicationmentioning

confidence: 99%

Towards Semantic Smart Cities: A Study on the Conceptualization and Implementation of Semantic Context Inference Systems

Lee,

Song

2023

Sensors

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“…IR is an essential research area that pushes the development of information technologies and applications in many domains across the industry. IR-based systems are at the core of many search engines, supporting tasks such as query routing [2], spam filtering [3], multimedia retrieval [4], and user interest mining [5]. IR is also a fundamental building block of many content-based recommender systems [6].…”

Section: Introductionmentioning

confidence: 99%

Topic Models Ensembles for AD-HOC Information Retrieval

2021

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Ad hoc information retrieval (ad hoc IR) is a challenging task consisting of ranking text documents for bag-of-words (BOW) queries. Classic approaches based on query and document text vectors use term-weighting functions to rank the documents. Some of these methods’ limitations consist of their inability to work with polysemic concepts. In addition, these methods introduce fake orthogonalities between semantically related words. To address these limitations, model-based IR approaches based on topics have been explored. Specifically, topic models based on Latent Dirichlet Allocation (LDA) allow building representations of text documents in the latent space of topics, the better modeling of polysemy and avoiding the generation of orthogonal representations between related terms. We extend LDA-based IR strategies using different ensemble strategies. Model selection obeys the ensemble learning paradigm, for which we test two successful approaches widely used in supervised learning. We study Boosting and Bagging techniques for topic models, using each model as a weak IR expert. Then, we merge the ranking lists obtained from each model using a simple but effective top-k list fusion approach. We show that our proposal strengthens the results in precision and recall, outperforming classic IR models and strong baselines based on topic models.

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