Learning from Ontology Streams with Semantic Concept Drift

Data stream learning has been largely studied for extracting knowledge structures from continuous and rapid data records. In the semantic Web, data is interpreted in ontologies and its ordered sequence is represented as an ontology stream. Our work exploits the semantics of such streams to tackle the problem of concept drift i.e., unexpected changes in data distribution, causing most of models to be less accurate as time passes. To this end we revisited (i) semantic inference in the context of supervised stream learning, and (ii) models with semantic embeddings. The experiments show accurate prediction with data from Dublin and Beijing

SPARQL Update for Materialised Triple Stores under DL-Lite RDFS Entailment

Abstract. Updates in RDF stores have recently been standardised in the SPARQL 1.1 Update specification. However, computing answers entailed by ontologies in triple stores is usually treated orthogonally to updates. Even W3C’s SPARQL 1.1 Update language and SPARQL 1.1 Entailment Regimes specifications explicitly exclude a standard behaviour for entailment regimes other than simple entailment in the context of updates. In this paper, we take a first step to close this gap. We define a fragment of SPARQL basic graph patterns corresponding to (the RDFS fragment of) DL-Lite and the corresponding SPARQL update language, dealing with updates both of ABox and of TBox statements. We discuss possible semantics along with potential strategies for implementing them. Particularly, we treat materialised RDF stores, which store all entailed triples explicitly, and preservation of materialisation upon ABox and TBox updates.

Rule-Based Intelligence on the Semantic Web: Implications for Military Capabilities

Rules are a key element of the Semantic Web vision, promising to provide a foundation for reasoning capabilities that underpin the intelligent manipulation and exploitation of information content. Although ontologies provide the basis for some forms of reasoning, it is unlikely that ontologies, by themselves, will support the range of knowledge-based services that are likely to be required on the Semantic Web. As such, it is important to consider the contribution that rule-based systems can make to the realization of advanced machine intelligence on the Semantic Web. This report aims to review the current state-of-the-art with respect to semantic rule-based technologies. It provides an overview of the rules, rule languages and rule engines that are currently available to support ontology-based reasoning, and it discusses some of the limitations of these technologies in terms of their inability to cope with uncertain or imprecise data and their poor performance in some reasoning contexts. This report also describes the contribution of reasoning systems to military capabilities, and suggests that current technological shortcomings pose a significant barrier to the widespread adoption of reasoning systems within the defence community. Some solutions to these shortcomings are presented and a timescale for technology adoption within the military domain is proposed. It is suggested that application areas such as semantic integration, semantic interoperability, data fusion and situation awareness provide the best opportunities for technology adoption within the 2015 timeframe. Other capabilities, such as decision support and the emulation of human-style reasoning capabilities are seen to depend on the resolution of significant challenges that may hinder attempts at technology adoption and exploitation within the 2020 timeframe

Natural Language Reasoning on ALC knowledge bases using Large Language Models

Τα προεκπαιδευμένα γλωσσικά μοντέλα έχουν κυριαρχήσει στην επεξεργασία φυσικής γλώσσας, αποτελώντας πρόκληση για τη χρήση γλωσσών αναπαράστασης γνώσης για την περιγραφή του κόσμου. Ενώ οι γλώσσες αυτές δεν είναι αρκετά εκφραστικές για να καλύψουν πλήρως τη φυσική γλώσσα, τα γλωσσικά μοντέλα έχουν ήδη δείξει σπουδαία αποτελέσματα όσον αφορά την κατανόηση και την ανάκτηση πληροφοριών απευθείας σε δεδομένα φυσικής γλώσσας. Διερευνούμε τις επιδόσεις των γλωσσικών μοντέλων για συλλογιστική φυσικής γλώσσας στη περιγραφική λογική ALC. Δημιουργούμε ένα σύνολο δεδομένων από τυχαίες βάσεις γνώσης ALC, μεταφρασμένες σε φυσική γλώσσα, ώστε να αξιολογήσουμε την ικανότητα των γλωσσικών μοντέλων να λειτουργούν ως συστήματα απάντησης ερωτήσεων πάνω σε βάσεις γνώσης φυσικής γλώσσας.Pretrained language models have dominated natural language processing, challenging the use of knowledge representation languages to describe the world. While these lan- guages are not expressive enough to fully cover natural language, language models have already shown great results in terms of understanding and information retrieval directly on natural language data. We explore language models’ performance at the downstream task of natural language reasoning in the description logic ALC. We generate a dataset of random ALC knowledge bases, translated in natural language, in order to assess the language models’ ability to function as question-answering systems over natural language knowledge bases

Explaining and Predicting Abnormal Expenses at Large Scale using Knowledge Graph based Reasoning

International audienceGlobal business travel spend topped record-breaking $1.2 Trillion USD in 2015, and will reach$1.6 Trillion by 2020 according to the Global Business Travel Association, the world's premier business travel and meetings trade organization. Existing expenses systems are designed for reporting expenses, their type and amount over pre-defined views such as time period, service or employee group. However such systems do not aim at systematically detecting abnormal expenses, and more importantly explaining their causes. Therefore deriving any actionable insight for optimising spending and saving from their analysis is time-consuming, cumbersome and often impossible. Towards this challenge we present AIFS, a system designed for expenses business owner and auditors. Our system is manipulating and combining semantic web and machine learning technologies for (i) identifying, (ii) explaining and (iii) predicting abnormal expenses claim by employees of large organisations. Our prototype of semantics-aware employee expenses analytics and reasoning, experimented with 191, 346 unique Accenture employees in 2015, has demonstrated scalability and accuracy for the tasks of explaining and predicting abnormal expenses

Scalable Reasoning for Knowledge Bases Subject to Changes

ScienceWeb is a semantic web system that collects information about a research community and allows users to ask qualitative and quantitative questions related to that information using a reasoning engine. The more complete the knowledge base is, the more helpful answers the system will provide. As the size of knowledge base increases, scalability becomes a challenge for the reasoning system. As users make changes to the knowledge base and/or new information is collected, providing fast enough response time (ranging from seconds to a few minutes) is one of the core challenges for the reasoning system. There are two basic inference methods commonly used in first order logic: forward chaining and backward chaining. As a general rule, forward chaining is a good method for a static knowledge base and backward chaining is good for the more dynamic cases. The goal of this thesis was to design a hybrid reasoning architecture and develop a scalable reasoning system whose efficiency is able to meet the interaction requirements in a ScienceWeb system when facing a large and evolving knowledge base. Interposing a backward chaining reasoner between an evolving knowledge base and a query manager with support of trust yields an architecture that can support reasoning in the face of frequent changes. An optimized query-answering algorithm, an optimized backward chaining algorithm and a trust-based hybrid reasoning algorithm are three key algorithms in such an architecture. Collectively, these three algorithms are significant contributions to the field of backward chaining reasoners over ontologies. I explored the idea of trust in the trust-based hybrid reasoning algorithm, where each change to the knowledge base is analyzed as to what subset of the knowledge base is impacted by the change and could therefore contribute to incorrect inferences. I adopted greedy ordering and deferring joins in optimized query-answering algorithm. I introduced four optimizations in the algorithm for backward chaining. These optimizations are: 1) the implementation of the selection function, 2) the upgraded substitute function, 3) the application of OLDT and 4) solving of the owl: sameAs problem. I evaluated our optimization techniques by comparing the results with and without optimization techniques. I evaluated our optimized query answering algorithm by comparing to a traditional backward-chaining reasoner. I evaluated our trust-based hybrid reasoning algorithm by comparing the performance of a forward chaining algorithm to that of a pure backward chaining algorithm. The evaluation results have shown that the hybrid reasoning architecture with the scalable reasoning system is able to support scalable reasoning of ScienceWeb to answer qualitative questions effectively when facing both a fixed knowledge base and an evolving knowledge base

Persuasive Explanation of Reasoning Inferences on Dietary Data

Explainable AI aims at building intelligent systems that are able to provide a clear, and human understandable, justification of their decisions. This holds for both rule-based and data-driven methods. In management of chronic diseases, the users of such systems are patients that follow strict dietary rules to manage such diseases. After receiving the input of the intake food, the system performs reasoning to understand whether the users follow an unhealthy behaviour. Successively, the system has to communicate the results in a clear and effective way, that is, the output message has to persuade users to follow the right dietary rules. In this paper, we address the main challenges to build such systems: i) the natural language generation of messages that explain the reasoner inconsistency; ii) the effectiveness of such messages at persuading the users. Results prove that the persuasive explanations are able to reduce the unhealthy users’ behaviours

Towards a satisfactory conversion of messages among agent-based information systems

Over the last years, there has been a change of perspective concerning the management of information systems, since they are no longer isolated and need to communicate with others. However, from a semantic point of view, real communication is difficult to achieve due to the heterogeneity of the systems. We present a proposal which, considering information systems are represented by software agents, provides a framework that favors a semantic communication among them, overcoming the heterogeneity of their agent communication languages. The main components of the framework are a suite of ontologies – conceptualizing communication acts – that will be used for generating the communication conversion, and an Event Calculus interpretation of the communications, which will be used for formalizing the notion of a satisfactory conversion. Moreover, we present a motivating example in order to complete the explanation of the whole picture.The work of Idoia Berges was supported by a grant of the Basque Government (Programa de Formación de Investigadores del Departamento de Educación, Universidades e Investigación). This work is also supported the Spanish Ministry of Education and Science TIN2010–21387-C02–01