Taxonomy completion via implicit concept insertion

High quality taxonomies play a critical role in various domains such as e-commerce, web search and ontology engineering. While there has been extensive work on expanding taxonomies from externally mined data, there has been less attention paid to enriching taxonomies by exploiting existing concepts and structure within the taxonomy. In this work, we show the usefulness of this kind of enrichment, and explore its viability with a new taxonomy completion system ICON (Implicit CONcept Insertion). ICON generates new concepts by identifying implicit concepts based on the existing concept structure, generating names for such concepts and inserting them in appropriate positions within the taxonomy. ICON integrates techniques from entity retrieval, text summary, and subsumption prediction; this modular architecture offers high flexibility while achieving state-of-the-art performance. We have evaluated ICON on two e-commerce taxonomies, and the results show that it offers significant advantages over strong baselines including recent taxonomy completion models and the large language model, ChatGPT

Контекстно-управляемый подход к интеллектуальной поддержке принятия решений на основе цифровых следов пользователей

A context-aware approach to intelligent decision support based on user digital traces is proposed. The concept of human digital life with regard to intelligent decision support is discussed. The aims of addressing this concept in diverse domains are clarified and approaches to modelling human digital life are identified. In the proposed approach, digital traces serve as a source of information to reveal user preferences and decision-making behaviour. Perspectives on decision support based on user digital traces are developed. The research outcomes are the specification of requirements to intelligent decision support based on user digital traces, the principles, conceptual framework and information model of such support. The principles form the basis for the conceptual framework of intelligent decision support based on user digital traces. Components of the conceptual model are user profiles; a user digital life model that structures information containing in the digital traces; group patterns that describe preferences and decision-making behavior shared by a user group; and a decision maker ontology. The information model defines information flows between the framework’s components, identifies tasks that require solutions to implement the framework and offers techniques for this. The novelties of the research are applying the concept of human digital life to intelligent decision support and context-dependent ontological inference of the type of user as a decision-maker, which determines a group of users sharing their preferences and behaviours with the active user, to predict a recommended decision. The paper contributes to the areas of modelling human digital life and intelligent decision support.Разрабатывается контекстно-управляемый подход к интеллектуальной поддержке принятия решений на основе цифровых следов пользователей. Рассматриваются вопросы использования концепции жизни человека в цифровой среде при интеллектуальной поддержке принятия решений. Исследуются цели обращения к цифровым следам человека в различных проблемных областях и выявляются подходы к моделированию жизни человека в цифровой среде. Предлагается подход к интеллектуальной поддержке принятия решений, в котором цифровые следы служат источником информации для выявления предпочтений пользователей и их поведения при принятии решений. Развиваются взгляды на поддержку принятия решений на основе учета следов пользователей в цифровой среде. Результатами исследования являются спецификация требований к интеллектуальной поддержке принятия решений на основе цифровых следов пользователя, принципы, концептуальная и информационная модели такой поддержки

The Energy Management Adviser at EDF

Abstract. The EMA (Energy Management Adviser) aims to produce personalised energy saving advice for EDF’s customers. The advice takes the form of one or more ‘tips’, and personalisation is achieved using se-mantic technologies: customers are described using RDF, an OWL on-tology provides a conceptual model of the relevant domain (housing, environment, and so on) and the different kinds of tips, and SPARQL query answering is used to identify relevant tips. The current prototype provides tips to more than 300,000 EDF customers in France at least twice a year. The main challenges for our future work include providing a timely service for all of the 35 million EDF customers in France, simpli-fying the system’s maintenance, and providing new ways for interacting with customers such as via a Web site.

Knowledge Discovery and Management within Service Centers

These days, most enterprise service centers deploy Knowledge Discovery and Management (KDM) systems to address the challenge of timely delivery of a resourceful service request resolution while efficiently utilizing the huge amount of data. These KDM systems facilitate prompt response to the critical service requests and if possible then try to prevent the service requests getting triggered in the first place. Nevertheless, in most cases, information required for a request resolution is dispersed and suppressed under the mountain of irrelevant information over the Internet in unstructured and heterogeneous formats. These heterogeneous data sources and formats complicate the access to reusable knowledge and increase the response time required to reach a resolution. Moreover, the state-of-the art methods neither support effective integration of domain knowledge with the KDM systems nor promote the assimilation of reusable knowledge or Intellectual Capital (IC). With the goal of providing an improved service request resolution within the shortest possible time, this research proposes an IC Management System. The proposed tool efficiently utilizes domain knowledge in the form of semantic web technology to extract the most valuable information from those raw unstructured data and uses that knowledge to formulate service resolution model as a combination of efficient data search, classification, clustering, and recommendation methods. Our proposed solution also handles the technology categorization of a service request which is very crucial in the request resolution process. The system has been extensively evaluated with several experiments and has been used in a real enterprise customer service center

Coupling tableau algorithms for expressive description logics with completion-based saturation procedures

Abstract. Nowadays, saturation-based reasoners for the OWL EL profile are able to handle large ontologies such as SNOMED very efficiently. However, saturation-based reasoning procedures become incomplete if the ontology is extended with axioms that use features of more expressive Description Logics, e.g., disjunctions. Tableau-based procedures, on the other hand, are not limited to a specific OWL profile, but even highly optimised reasoners might not be efficient enough to handle large ontologies such as SNOMED. In this paper, we present an approach for tightly coupling tableau-and saturation-based procedures that we implement in the OWL DL reasoner Konclude. Our detailed evaluation shows that this combination significantly improves the reasoning performance on a wide range of ontologies