Organisations that develop semantic models have a lot to think about. The envisioned benefits from semantic solutions have to compete with many factors of uncertainty that threaten project results. Challenges lie not only in the evident scarcity of knowledge, skills and tools but also in other factors such as business objectives and requirements.
In this talk Lieke Verhelst shares her long experience as a semantic modeller. Side by side with subject matter experts she has constructed semantic models and infrastructures for the environment, construction and education sectors. She will point out which common pitfalls she has seen during a ontology development process. While illustrating these, she will come to answering the question why SKOS is the key to success for semantic solution projects.
The development of Big Data technologies offers new perspectives in building powerful disambiguation systems. New approaches can be imagined to discover and normalize non-controlled vocabularies such as named entities.
In this presentation, I will explain how Reportlinker.com, an award-winning market research solution, developed an inference engine based on supervised analysis to disambiguate the names of companies found in a corpus of unstructured documents.
Through several examples, I will explain the main steps of our approach:
- The discovery of non-verified fact (hypotheses) using a large volume of data
- The transformation of hypotheses into verified facts, using an iterative graph processing system
- The construction of a relational graph to attribute new context around each normalized concept.
Our customer represents one of the fastest growing organizations in the $30B Multi-level Marketing (MLM) industry. The customer has been managing their business with a relational database solution for over four months that has unfortunately been misaligned with internal data requirements.
Due to the lack of documentation and understanding of the misaligned solution, the company was not able to generate quarterly business and sales reports. For example, a simple question: “How many Orders were placed in May 2015” meant numerous things to different people and departments within the organization.
In this presentation we will discuss how semantic technologies play a key role in addressing this problem. We will highlight how we bootstrap an Enterprise Ontology from a relational database and how we virtually create a Semantic Data Warehouse by mapping the relational database to the Enterprise Ontology without having to physically move the data.
To serve their daily readership of 2.2 million and realise their 'digital-first' strategy, the Financial Times chose Ontotext's GraphDB Enterprise Edition. The installation of Ontotext's RDF database has pushed the state of the art by increasing the scalability, reliability and availability of semantic technology. Additionally, Ontotext provided a number of NLP-services leveraging the semantic database to provide concept extraction, disambiguation and personalised recommendation service for FT's customers.
Our talk will discuss the problems facing the implementation of semantic-driven approaches in an enterprise environment, the lessons learned and how those problems were overcome to help a world-renowned news publisher provide innovative new products and services.
Interactive session: Join us for an in-depth technology demonstration and take the hot seat on our panel session.
The Multilateral Interoperability Programme (MIP) is a multinational military standardization committee with participants from 24 member nations and NATO. It develops interoperability specifications for Command and Control Information Systems (C2IS). A key product is the MIP Information Model (MIM) that serves as a standard for information exchange for multiple echelons in joint and combined operations. Technically, the MIM is based on UML, extended by so-called UML profiles that constitute the MIM meta model. The MIM refers to various legacy data models and is under continuous development for enabling interoperability under changing operational requirements. To ensure model soundness and consistency, it comes with a suite of sophisticated tools for semantic analysis and configuration management. It seeks to close the gap between the domain expert on the one hand and the software implementer on the other hand, enabling model-driven software development. To this end, several transformations for the MIM have been defined. Among them is a transformation to OWL2. The derivation of an OWL ontology from the MIM makes it possible to add domain knowledge that cannot be expressed adequately with UML. The OWL-transformation is thus an important step in constructing a commonly agreed upon, extensible C2 ontology.
In order to deal with marketing strategy and competitive intelligence, industries need to monitor the Web to gather and make sense of such a large amount of information. This information is scattered and it takes time for humans to analyze the different resources and to compile the gathered knowledge in an intelligent way. SMILK is a Joint laboratory between the Inria research institute and the VISEO company to study the strong coupling of algorithms and linguistic models at a semantic level, the extraction and the disambiguation of the knowledge guided by Web resources and the combination of various ways of reasoning (logical inferences, approximations and similarity, etc.).
In this context, we will present a prototype gathering results so far obtained to enrich user knowledge while browsing the Web using Natural Language Processing, Web Open Data, and Social Networks. Our presentation will focus on the demonstration of an easy to install and use browser plugin enriching the users’ experience with data gathering and intelligence making functionalities applied in real time to the visualized page.
Potential uses of machine intelligence in health care applications have made big waves in the news recently. While the focus has been mainly in diagnostic help, there is another realm where the potential is staggering, that of speeding up drug research. I will describe a first concrete example, a pilot project of Merck using the InfoCodex software, in which semantic machine intelligence was successfully used to comb through large quantities of biomedical research papers in search of hidden correlations pointing to new biomarkers for diabetes and obesity.
Clinical decision support system application are a class of application to support physicians in their choice in treating patients. Cancer treatment is one of the area where Cdss applications can help physicians due to strict recommendations and the need for deciding if patients are eligible to enter clinical trial.
In Poland a pilot study conducted at the Marie-Curie Oncology Center in Warsaw (MCMCC) revealed that medical errors lead to decrease of patient survival at about 20%. The key point of our prototype was to educate and/or directly help medical practitioners in applying recommended treatment.
Our application, called Clinical decision support system (Cdss) for GIST cancer treatment, has been developed in collaboration with the MCMCC to help physicians treat GIST cancer and is currently used experimentally in this center. Our application has been formalized using semantic web standards from the application structure to the patient and clinical recommendations that are shown. The application is using fast reasoning to decide what is the next step in the treatment and to add recommendations to the form that is shown. This kind of technologies have already been applied to such applications but never to this extent. The Cdss application that we have implemented is a practical implementation of the semantic form paradigm that we are working on in our company: a way to formalize forms by using semantic technologies to enhance productivity and data consistency.
Based on the technological concepts of cyber-physical systems, the Internet of Things and the Internet of Services, the vision of smart factories is summarized as “Industrie 4.0”. The goal: a smart factory that works adaptable and resource-efficient, integrating customers and partners into the business and value-added processes. Cyber-physical systems combine software technical components with mechanical and electronic parts.
Semantic technologies are seen as a key to the realization of such systems semantic technologies. They are able to integrate all product and production process relevant data and build a layer of connected information within one single model.
However, technical documentation for machinery and equipment actually still describes traditionally closed technical systems with clearly defined functions and components. The documentation for the manufactured products is also based on a fixed, predetermined product. Instead, documentation in the Age of Industrie 4.0 has to use flexible and topic oriented approaches to cover the adaptive and individualized products and services they are describing. Furthermore they also must support applications such as the operation and maintenance of the parts.
