Enhancing online knowledge graph population with semantic knowledge

Knowledge Graphs (KG) are becoming essential to organize, represent and store the world’s knowledge, but they still rely heavily on humanly-curated structured data. Information Extraction (IE) tasks, like disambiguating entities and relations from unstructured text, are key to automate KG population. However, Natural Language Processing (NLP) methods alone can not guarantee the validity of the facts extracted and may introduce erroneous information into the KG. This work presents an end-to-end system that combines Semantic Knowledge and Validation techniques with NLP methods, to provide KG population of novel facts from clustered news events. The contributions of this paper are two-fold: First, we present a novel method for including entity-type knowledge into a Relation Extraction model, improving F1-Score over the baseline with TACRED and TypeRE datasets. Second, we increase the precision by adding data validation on top of the Relation Extraction method. These two contributions are combined in an industrial pipeline for automatic KG population over aggregated news, demonstrating increased data validity when performing online learning from unstructured web data. Finally, the TypeRE and AggregatedNewsRE datasets build to benchmark these results are also published to foster future research in this field.This work was partially supported by the Government of Catalonia under the industrial doctorate 2017 DI 011.Peer ReviewedPostprint (author's final draft

A merging data tool for knowledge based photogrammetry: the case study of the castle of shawbak,Jordan

International audienceThe present paper addresses an approach for merging heritage survey and archaeological knowledge. The theoretical framework is the integration between photogrammetric survey and documentation process, practically used in different archaeological excavation. Merging surveyed geometries and knowledge is a complex task. Many variables have to be considered during the process of merging. Photogrammetric survey results and knowledge can be actually seen as information. Information is sorted by source. A source is a set of information provided by the operators involved in the excavation process. Such operators can be archaeologists, photogrammetrists, or any other researcher (i.e. a topographist) involved in the study. The merging process involves the verification of the consistency of different sources and the aggregation of all the information from the sources into a global result. Each source, respectively each operator, owns a personal representation of his knowledge domain, a photogrammetrist uses geometrical primitive and 3D representations of the object surveyed, an archaeologist has a textual and semantic representation of the objects. Merging together all these sets of information needs a tool which can be easily operated by most of the participants in the research and which can furthermore manage the ‘multiple knowledge' on the surveyed object. This tool, called Ametist, an acronym standing for Arpenteur ManagEment Tool for Interactive Survey Treatment, uses a simple interface for displaying results and knowledge in various form (textual, 2D map, 3D scene, XML). This tool can make an automatic merging of the “multiple knowledge” and its merge engine can solve conflicts (object identification mismatch, measure of an object taken several times, spatial collisions etc.). When conflicts cannot automatically be solved the application can report about inconsistency errors and ask a user to manually correct the information involved. As inconsistency can be present in any information, all operators have to be able to use the interface. The tool provides a simple easy to use interface. This document will first address the concept of knowledge based photogrammetry (with ARPENTEUR) and then deal with a presentation of ‘Ametist'. Finally, a real case study will be considered to highlight the first results of such a system in the frame of a French Italian scientific partnership with the “Dipartimento di Studi storici e Geografici” of the University of Florence, in charge of the archaeological research. The selected case study is the Castle of Shawbak, in Jordan, known in medieval written sources as the “Crac de Montréal”

Fusing Automatically Extracted Annotations for the Semantic Web

This research focuses on the problem of semantic data fusion. Although various solutions have been developed in the research communities focusing on databases and formal logic, the choice of an appropriate algorithm is non-trivial because the performance of each algorithm and its optimal configuration parameters depend on the type of data, to which the algorithm is applied. In order to be reusable, the fusion system must be able to select appropriate techniques and use them in combination. Moreover, because of the varying reliability of data sources and algorithms performing fusion subtasks, uncertainty is an inherent feature of semantically annotated data and has to be taken into account by the fusion system. Finally, the issue of schema heterogeneity can have a negative impact on the fusion performance. To address these issues, we propose KnoFuss: an architecture for Semantic Web data integration based on the principles of problem-solving methods. Algorithms dealing with different fusion subtasks are represented as components of a modular architecture, and their capabilities are described formally. This allows the architecture to select appropriate methods and configure them depending on the processed data. In order to handle uncertainty, we propose a novel algorithm based on the Dempster-Shafer belief propagation. KnoFuss employs this algorithm to reason about uncertain data and method results in order to refine the fused knowledge base. Tests show that these solutions lead to improved fusion performance. Finally, we addressed the problem of data fusion in the presence of schema heterogeneity. We extended the KnoFuss framework to exploit results of automatic schema alignment tools and proposed our own schema matching algorithm aimed at facilitating data fusion in the Linked Data environment. We conducted experiments with this approach and obtained a substantial improvement in performance in comparison with public data repositories

Verification of knowledge shared across design and manufacture using a foundation ontology

Seamless computer-based knowledge sharing between departments of a manufacturing enterprise is useful in preventing unnecessary design revisions. A lack of interoperability between independently developed knowledge bases, however, is a major impediment in the development of a seamless knowledge sharing system. Interoperability, being an ability to overcome semantic and syntactic differences during computer-based knowledge sharing can be enhanced through the use of ontologies. Ontologies in computer science terms are hierarchical structures of knowledge stored in a computer-based knowledge base. Ontologies have been accepted by all as an interoperable medium to provide a non-subjective way of storing and sharing knowledge across diverse domains. Some semantic and syntactic differences, however, still crop up when these ontological knowledge bases are developed independently. A case study in an aerospace components manufacturing company suggests that shape features of a component are perceived differently by the designing and manufacturing departments. These differences cause further misunderstanding and misinterpretation when computer-based knowledge sharing systems are used across the two domains. Foundation or core ontologies can be used to overcome these differences and to ensure a seamless sharing of knowledge. This is because these ontologies provide a common grounding for domain ontologies to be used by individual domains or department. This common grounding can be used by the mediation and knowledge verification systems to authenticate the meaning of knowledge understood across different domains. For this reason, this research proposes a knowledge verification framework for developing a system capable of verifying knowledge between those domain ontologies which are developed out of a common core or foundation ontology. This framework makes use of ontology logic to standardize the way concepts from a foundation and core-concepts ontology are used in domain ontologies and then by using the same principles the knowledge being shared is verified. The Knowledge Frame Language which is based on Common Logic is used for formalizing example ontologies. The ontology editor used for browsing and querying ontologies is the Integrated Ontology Development Environment (IODE) by Highfleet Inc. An ontological product modelling technique is also developed in this research, to test the proposed framework in the scenario of manufacturability analysis. The proposed framework is then validated through a Java API specially developed for this purpose. Real industrial examples experienced during the case study are used for validation