Self-tuning Personalized Information Retrieval in an Ontology-Based Framework

Reliability is a well-known concern in the field of personalization technologies. We propose the extension of an ontology-based retrieval system with semantic-based personalization techniques, upon which automatic mechanisms are devised that dynamically gauge the degree of personalization, so as to benefit from adaptivity but yet reduce the risk of obtrusiveness and loss of user control. On the basis of a common domain ontology KB, the personalization framework represents, captures and exploits user preferences to bias search results towards personal user interests. Upon this, the intensity of personalization is automatically increased or decreased according to an assessment of the imprecision contained in user requests and system responses before personalization is applied

Personalized content retrieval in context using ontological knowledge

Personalized content retrieval aims at improving the retrieval process by taking into account the particular interests of individual users. However, not all user preferences are relevant in all situations. It is well known that human preferences are complex, multiple, heterogeneous, changing, even contradictory, and should be understood in context with the user goals and tasks at hand. In this paper, we propose a method to build a dynamic representation of the semantic context of ongoing retrieval tasks, which is used to activate different subsets of user interests at runtime, in a way that out-of-context preferences are discarded. Our approach is based on an ontology-driven representation of the domain of discourse, providing enriched descriptions of the semantics involved in retrieval actions and preferences, and enabling the definition of effective means to relate preferences and context

The quest for information retrieval on the semantic web

Semantic search has been one of the motivations of the Semantic Web since it was envisioned. We propose a model for the exploitation of ontology-based KBs to improve search over large document repositories. The retrieval model is based on an adaptation of the classic vector-space model, including an annotation weighting algorithm, and a ranking algorithm. Semantic search is combined with keyword-based search to achieve tolerance to KB incompleteness. Our proposal has been tested on corpora of significant size, showing promising results with respect to keyword-based search, and providing ground for further analysis and research

Natural Language based Context Modeling and Reasoning with LLMs: A Tutorial

Large language models (LLMs) have become phenomenally surging, since 2018--two decades after introducing context-awareness into computing systems. Through taking into account the situations of ubiquitous devices, users and the societies, context-aware computing has enabled a wide spectrum of innovative applications, such as assisted living, location-based social network services and so on. To recognize contexts and make decisions for actions accordingly, various artificial intelligence technologies, such as Ontology and OWL, have been adopted as representations for context modeling and reasoning. Recently, with the rise of LLMs and their improved natural language understanding and reasoning capabilities, it has become feasible to model contexts using natural language and perform context reasoning by interacting with LLMs such as ChatGPT and GPT-4. In this tutorial, we demonstrate the use of texts, prompts, and autonomous agents (AutoAgents) that enable LLMs to perform context modeling and reasoning without requiring fine-tuning of the model. We organize and introduce works in the related field, and name this computing paradigm as the LLM-driven Context-aware Computing (LCaC). In the LCaC paradigm, users' requests, sensors reading data, and the command to actuators are supposed to be represented as texts. Given the text of users' request and sensor data, the AutoAgent models the context by prompting and sends to the LLM for context reasoning. LLM generates a plan of actions and responds to the AutoAgent, which later follows the action plan to foster context-awareness. To prove the concepts, we use two showcases--(1) operating a mobile z-arm in an apartment for assisted living, and (2) planning a trip and scheduling the itinerary in a context-aware and personalized manner.Comment: Under revie

Unsupervised linking of scientific articles to food systems taxonomies

In this thesis, a novel method for linking scientific articles to taxonomy terms in the domain of food systems research is presented. With food systems being in the center of 12 of the 17 United Nations Sustainable Development goals, there has been an ever-growing amount of scientific articles in this field. These articles are vital in understanding the complex nature of food systems and their inter-dependencies. However, finding relevant literature in this field is difficult for decision makers given the interdisciplinary nature of the field and that annotation and expert feedback is expensive. In the thesis, BERT-based models (SBERT, SPECTER and SciBERT) are adapted to the food systems area and fine-tuned for tasks such as text classification and text similarity, which represents a solution to the problem of finding relevant articles in the food systems domain. The proposed search system uses several taxonomies and data augmentation to achieve the results, which are visualized in a created website. Linking food systems research articles to taxonomy terms shows good accuracy, with models finetuned on domain data achieving better performance on classification task. The best fine-tuning strategy for SPECTER and SciBERT is the combination of domain adaptation and classification. Fine-tuning for text similarity for SBERT improves SBERT performance only slightly. The proposed method can be used in other domains than food systems

An Online Framework for Supporting the Evaluation of Personalised Information Retrieval Systems

Scope - Personalised Information Retrieval (PIR) has been gaining attention because it investigates intelligent ways for enhancing content delivery. Web users can have personalised services and more accurate information. Problem - Several PIR systems have been proposed in the literature; however, they have not been properly tested or evaluated. Proposal – The authors propose a generally applicable web-based interface, which provides PIR developers and evaluators with: i) implicit recommendations on how to evaluate a specific PIR system; ii) a repository containing studies on user-centred and layered evaluation studies; iii) recommendations on how to best combine different evaluation methods, metrics and measurement criteria in order to most effectively evaluate their system; iv) a UCE methodology which details how to apply existing UCE techniques; v) a taxonomy of evaluations of adaptive systems; and vi) interface translation support (49 languages supported)