Knowledge web: realising the semantic web... all the way to knowledge-enhanced multimedia documents

The semantic web and semantic web services are major efforts in order to spread and to integrate knowledge technology to the whole web. The Knowledge Web network of excellence aims at supporting their developments at the best and largest European level and supporting industry in adopting them. It especially investigates the solution of scalability, heterogeneity and dynamics obstacles to the full development of the semantic web. We explain how Knowledge Web results should benefit knowledge-enhanced multimedia applications

State-of-the-art on evolution and reactivity

This report starts by, in Chapter 1, outlining aspects of querying and updating resources on the Web and on the Semantic Web, including the development of query and update languages to be carried out within the Rewerse project. From this outline, it becomes clear that several existing research areas and topics are of interest for this work in Rewerse. In the remainder of this report we further present state of the art surveys in a selection of such areas and topics. More precisely: in Chapter 2 we give an overview of logics for reasoning about state change and updates; Chapter 3 is devoted to briefly describing existing update languages for the Web, and also for updating logic programs; in Chapter 4 event-condition-action rules, both in the context of active database systems and in the context of semistructured data, are surveyed; in Chapter 5 we give an overview of some relevant rule-based agents frameworks

Using contextual information to understand searching and browsing behavior

There is great imbalance in the richness of information on the web and the succinctness and poverty of search requests of web users, making their queries only a partial description of the underlying complex information needs. Finding ways to better leverage contextual information and make search context-aware holds the promise to dramatically improve the search experience of users. We conducted a series of studies to discover, model and utilize contextual information in order to understand and improve users' searching and browsing behavior on the web. Our results capture important aspects of context under the realistic conditions of different online search services, aiming to ensure that our scientific insights and solutions transfer to the operational settings of real world applications

Experiencing OptiqueVQS: A Multi-paradigm and Ontology-based Visual Query System for End Users

This is author's post-print version, published version available on http://link.springer.com/article/10.1007%2Fs10209-015-0404-5Data access in an enterprise setting is a determining factor for value creation processes, such as sense-making, decision-making, and intelligence analysis. Particularly, in an enterprise setting, intuitive data access tools that directly engage domain experts with data could substantially increase competitiveness and profitability. In this respect, the use of ontologies as a natural communication medium between end users and computers has emerged as a prominent approach. To this end, this article introduces a novel ontology-based visual query system, named OptiqueVQS, for end users. OptiqueVQS is built on a powerful and scalable data access platform and has a user-centric design supported by a widget-based flexible and extensible architecture allowing multiple coordinated representation and interaction paradigms to be employed. The results of a usability experiment performed with non-expert users suggest that OptiqueVQS provides a decent level of expressivity and high usability and hence is quite promising

A semantic and agent-based approach to support information retrieval, interoperability and multi-lateral viewpoints for heterogeneous environmental databases

PhDData stored in individual autonomous databases often needs to be combined and interrelated. For example, in the Inland Water (IW) environment monitoring domain, the spatial and temporal variation of measurements of different water quality indicators stored in different databases are of interest. Data from multiple data sources is more complex to combine when there is a lack of metadata in a computation forin and when the syntax and semantics of the stored data models are heterogeneous. The main types of information retrieval (IR) requirements are query transparency and data harmonisation for data interoperability and support for multiple user views. A combined Semantic Web based and Agent based distributed system framework has been developed to support the above IR requirements. It has been implemented using the Jena ontology and JADE agent toolkits. The semantic part supports the interoperability of autonomous data sources by merging their intensional data, using a Global-As-View or GAV approach, into a global semantic model, represented in DAML+OIL and in OWL. This is used to mediate between different local database views. The agent part provides the semantic services to import, align and parse semantic metadata instances, to support data mediation and to reason about data mappings during alignment. The framework has applied to support information retrieval, interoperability and multi-lateral viewpoints for four European environmental agency databases. An extended GAV approach has been developed and applied to handle queries that can be reformulated over multiple user views of the stored data. This allows users to retrieve data in a conceptualisation that is better suited to them rather than to have to understand the entire detailed global view conceptualisation. User viewpoints are derived from the global ontology or existing viewpoints of it. This has the advantage that it reduces the number of potential conceptualisations and their associated mappings to be more computationally manageable. Whereas an ad hoc framework based upon conventional distributed programming language and a rule framework could be used to support user views and adaptation to user views, a more formal framework has the benefit in that it can support reasoning about the consistency, equivalence, containment and conflict resolution when traversing data models. A preliminary formulation of the formal model has been undertaken and is based upon extending a Datalog type algebra with hierarchical, attribute and instance value operators. These operators can be applied to support compositional mapping and consistency checking of data views. The multiple viewpoint system was implemented as a Java-based application consisting of two sub-systems, one for viewpoint adaptation and management, the other for query processing and query result adjustment

Survey on Evaluation Methods for Dialogue Systems

In this paper we survey the methods and concepts developed for the evaluation of dialogue systems. Evaluation is a crucial part during the development process. Often, dialogue systems are evaluated by means of human evaluations and questionnaires. However, this tends to be very cost and time intensive. Thus, much work has been put into finding methods, which allow to reduce the involvement of human labour. In this survey, we present the main concepts and methods. For this, we differentiate between the various classes of dialogue systems (task-oriented dialogue systems, conversational dialogue systems, and question-answering dialogue systems). We cover each class by introducing the main technologies developed for the dialogue systems and then by presenting the evaluation methods regarding this class

Learning to Map Natural Language to Executable Programs Over Databases

Natural language is a fundamental form of information and communication and is becoming the next frontier in computer interfaces. As the amount of data available online has increased exponentially, so has the need for Natural Language Interfaces (NLIs, which is not used for natural language inference in this thesis) to connect the data and the user by easily using natural language, significantly promoting the possibility and efficiency of information access for many users besides data experts. All consumer-facing software will one day have a dialogue interface, and this is the next vital leap in the evolution of search engines. Such intelligent dialogue systems should understand the meaning of language grounded in various contexts and generate effective language responses in different forms for information requests and human-computer communication.Developing these intelligent systems is challenging due to (1) limited benchmarks to drive advancements, (2) alignment mismatches between natural language and formal programs, (3) lack of trustworthiness and interpretability, (4) context dependencies in both human conversational interactions and the target programs, and (5) joint language understanding between dialog questions and NLI environments (e.g. databases and knowledge graphs). This dissertation presents several datasets, neural algorithms, and language models to address these challenges for developing deep learning technologies for conversational natural language interfaces (more specifically, NLIs to Databases or NLIDB). First, to drive advancements towards neural-based conversational NLIs, we design and propose several complex and cross-domain NLI benchmarks, along with introducing several datasets. These datasets enable training large, deep learning models. The evaluation is done on unseen databases. (e.g., about course arrangement). Systems must generalize well to not only new SQL queries but also to unseen database schemas to perform well on these tasks. Furthermore, in real-world applications, users often access information in a multi-turn interaction with the system by asking a sequence of related questions. The users may explicitly refer to or omit previously mentioned entities and constraints and may introduce refinements, additions, or substitutions to what has already been said. Therefore, some of them require systems to model dialog dynamics and generate natural language explanations for user verification. The full dialogue interaction with the system’s responses is also important as this supports clarifying ambiguous questions, verifying returned results, and notifying users of unanswerable or unrelated questions. A robust dialogue-based NLI system that can engage with users by forming its responses has thus become an increasingly necessary component for the query process. Moreover, this thesis presents the development of scalable algorithms designed to parse complex and sequential questions to formal programs (e.g., mapping questions to SQL queries that can execute against databases). We propose a novel neural model that utilizes type information from knowledge graphs to better understand rare entities and numbers in natural language questions. We also introduce a neural model based on syntax tree neural networks, which was the first methodology proposed for generating complex programs from language. Finally, language modeling creates contextualized vector representations of words by training a model to predict the next word given context words, which are the basis of deep learning for NLP. Recently, pre-trained language models such as BERT and RoBERTa achieve tremendous success in many natural language processing tasks such as text understanding and reading comprehension. However, most language models are pre-trained only on free-text such as Wikipedia articles and Books. Given that language in semantic parsing is usually related to some formal representations such as logic forms and SQL queries and has to be grounded in structural environments (e.g., databases), we propose better language models for NLIs by enforcing such compositional interpolation in them. To show they could better jointly understand dialog questions and NLI environments (e.g. databases and knowledge graphs), we show that these language models achieve new state-of-the-art results for seven representative tasks on semantic parsing, dialogue state tracking, and question answering. Also, our proposed pre-training method is much more effective than other prior work

An ontology system for rehabilitation robotics

Representing the available information about rehabilitation robots in a structured form, like ontologies, facilitates access to various kinds of information about the existing robots, and thus it is important both from the point of view of rehabilitation robotics and from the point of view of physical medicine. Rehabilitation robotics researchers can learn various properties of the existing robots and access to the related publications to further improve the state-of-the-art. Physical medicine experts can find information about rehabilitation robots and related publications (possibly including results of clinical studies) to better identify the right robot for a particular therapy or patient population. Therefore, considering also the advantages of ontologies and ontological reasoning, such as interoperability of various heterogenous knowledge resources (e.g., patient databases or disease ontologies), such an ontology provides the underlying mechanisms for translational physical medicine, from bench-to-bed and back, and personalized rehabilitation robotics. In this thesis, we introduce the first formal rehabilitation robotics ontology, called RehabRobo-Onto, to represent information about rehabilitation robots and their properties. We have designed and developed RehabRobo-Onto in OWL, collaborating with experts in robotics and in physical medicine. We have also built a software (called RehabRobo- Query) with an easy-to-use intelligent user-interface that allows robot designers to add/modify information about their rehabilitation robots to/from RehabRobo-Onto. With RehabRobo-Query, the experts do not need to know about the logic-based ontology languages, or have experience with the existing Semantic Web technologies or logic-based ontological reasoners. RehabRobo-Query is made available on the cloud, utilizing Amazon Web services, so that rehabilitation robot designers around the world can add/modify information about their robots in RehabRobo-Onto, and rehabilitation robot designers and physical medicine experts around the world can access the knowledge in RehabRobo-Onto by means of questions about robots, in natural language, with the guide of the intelligent userinterface of RehabRobo-Query. The ontology system consisting of RehabRobo-Onto and RehabRobo- Query is of great value to robot designers as well as physical therapists and medical doctors. On the one hand, robot designers can access various properties of the existing robots and to the related publications to further improve the state-of-the-art. On the other hand, physical therapists and medical doctors can utilize the ontology to compare rehabilitation robots and to identify the ones that serve best to cover their needs, or to evaluate the effects of various devices for targeted joint exercises on patients with specific disorders