Knowledge Base Population using Semantic Label Propagation

A crucial aspect of a knowledge base population system that extracts new facts from text corpora, is the generation of training data for its relation extractors. In this paper, we present a method that maximizes the effectiveness of newly trained relation extractors at a minimal annotation cost. Manual labeling can be significantly reduced by Distant Supervision, which is a method to construct training data automatically by aligning a large text corpus with an existing knowledge base of known facts. For example, all sentences mentioning both 'Barack Obama' and 'US' may serve as positive training instances for the relation born_in(subject,object). However, distant supervision typically results in a highly noisy training set: many training sentences do not really express the intended relation. We propose to combine distant supervision with minimal manual supervision in a technique called feature labeling, to eliminate noise from the large and noisy initial training set, resulting in a significant increase of precision. We further improve on this approach by introducing the Semantic Label Propagation method, which uses the similarity between low-dimensional representations of candidate training instances, to extend the training set in order to increase recall while maintaining high precision. Our proposed strategy for generating training data is studied and evaluated on an established test collection designed for knowledge base population tasks. The experimental results show that the Semantic Label Propagation strategy leads to substantial performance gains when compared to existing approaches, while requiring an almost negligible manual annotation effort.Comment: Submitted to Knowledge Based Systems, special issue on Knowledge Bases for Natural Language Processin

Generating collaborative systems for digital libraries: A model-driven approach

This is an open access article shared under a Creative Commons Attribution 3.0 Licence (http://creativecommons.org/licenses/by/3.0/). Copyright @ 2010 The Authors.The design and development of a digital library involves different stakeholders, such as: information architects, librarians, and domain experts, who need to agree on a common language to describe, discuss, and negotiate the services the library has to offer. To this end, high-level, language-neutral models have to be devised. Metamodeling techniques favor the definition of domainspecific visual languages through which stakeholders can share their views and directly manipulate representations of the domain entities. This paper describes CRADLE (Cooperative-Relational Approach to Digital Library Environments), a metamodel-based framework and visual language for the definition of notions and services related to the development of digital libraries. A collection of tools allows the automatic generation of several services, defined with the CRADLE visual language, and of the graphical user interfaces providing access to them for the final user. The effectiveness of the approach is illustrated by presenting digital libraries generated with CRADLE, while the CRADLE environment has been evaluated by using the cognitive dimensions framework

Programming patterns and development guidelines for Semantic Sensor Grids (SemSorGrid4Env)

The web of Linked Data holds great potential for the creation of semantic applications that can combine self-describing structured data from many sources including sensor networks. Such applications build upon the success of an earlier generation of 'rapidly developed' applications that utilised RESTful APIs. This deliverable details experience, best practice, and design patterns for developing high-level web-based APIs in support of semantic web applications and mashups for sensor grids. Its main contributions are a proposal for combining Linked Data with RESTful application development summarised through a set of design principles; and the application of these design principles to Semantic Sensor Grids through the development of a High-Level API for Observations. These are supported by implementations of the High-Level API for Observations in software, and example semantic mashups that utilise the API

Semantic Model Alignment for Business Process Integration

Business process models describe an enterprise’s way of conducting business and in this form the basis for shaping the organization and engineering the appropriate supporting or even enabling IT. Thereby, a major task in working with models is their analysis and comparison for the purpose of aligning them. As models can differ semantically not only concerning the modeling languages used, but even more so in the way in which the natural language for labeling the model elements has been applied, the correct identification of the intended meaning of a legacy model is a non-trivial task that thus far has only been solved by humans. In particular at the time of reorganizations, the set-up of B2B-collaborations or mergers and acquisitions the semantic analysis of models of different origin that need to be consolidated is a manual effort that is not only tedious and error-prone but also time consuming and costly and often even repetitive. For facilitating automation of this task by means of IT, in this thesis the new method of Semantic Model Alignment is presented. Its application enables to extract and formalize the semantics of models for relating them based on the modeling language used and determining similarities based on the natural language used in model element labels. The resulting alignment supports model-based semantic business process integration. The research conducted is based on a design-science oriented approach and the method developed has been created together with all its enabling artifacts. These results have been published as the research progressed and are presented here in this thesis based on a selection of peer reviewed publications comprehensively describing the various aspects

Managing Knowledge as Business Rules

In today’s business environment, it is a certainty that will manage to survive especially those organizations which are striving to adapt quickly and with low costs to the new demands of market competition. Knowledge represented by internal business rules of an organization can help crystallize their orientation in order to ensure a competitive advantage in the market. In this context and in a relatively short time, a new trend in software development has arisen, ex-tending current methods and putting a strong emphasis on business rules. This article outlines the importance of managing business rules in an organized manner using dedicated software products and furthermore presents a general prototype for a business rules repository.Business Rules, Management, Knowledge, Rule Engine, Repository Prototype

Applying Model-Based Data Engineering to Evaluate the Alignment of Information Modeled Within JC3IEDM, MSDL, and MATREX-FOM

The need for a common representation of entities and their relations to support the easier composition and federation of independently developed solutions in support of the user has been identified and addressed in several papers presented during recent simulation interoperability workshop. One of the underlying assumptions is that standards derived from the same conceptual domain can easily be converted into each others, as they deal with the same concepts. In a project conducted for the U.S. Army’s Program Executive Office (PEO) Soldier, three of such solutions for military operations (with focus on the land forces) were utilized to capture the underlying concepts of land warfare: the Joint Consultation, Command and Control Information Exchange Data Model (JC3IEDM), the Military Scenario Description Language (MSDL), and the Modeling Architecture for Technology, Research, and Experimentation (MATREX) Federation Object Model (FOM). When we applied the methods of Model-based Data Engineering (MBDE) we observed, that these three standards are not conceptually as well aligned as we assumed. We identified several significant gaps. The findings of this paper will contribute to support designers, engineers and project managers in a better way to understand, (1) which data are needed operationally, (2) how gaps can be identified regarding supporting standards, (3) how the gaps can be closed, and (4) what data transformation must be conducted when dealing with different standards in data-rich integration projects to ensure cost-efficient and operationally effective solutions