Information extraction is the process of recognizing the particular fragments of a document that constitute its core semantic content. However, most previous information extraction systems were not effective for real-world information sources due to difficulties in acquiring and representing useful domain knowledge and in dealing with structural heterogeneity inherent in different sources. In o...

In this paper, we address the knowledge engineering problems for hypothesis generation motivated by applications that require timely exploration of hypotheses under unreliable observations. We looked at two applications: malware detection and intensive care delivery. In intensive care, the goal is to generate plausible hypotheses about the condition of the patient from clinical observations and...

One important intention of human-centered information visualization is to represent huge amounts of abstract data in a visual representation that allows even users from foreign application domains to interact with the visualization, to understand the underlying data, and finally, to gain new, application-related knowledge. The visualization will help experts as well as non-experts to link previ...

Noun-verb event frame (NVEF) knowledge in conjunction with an NVEF word-pair identifier [Tsai et al. 2002] comprises a system to support natural language processing (NLP) and natural language understanding (NLU). In [Tsai et al. 2002a], we demonstrated that NVEF knowledge can be used effectively to resolve the Chinese word-sense disambiguation (WSD) problem with 93.7% accuracy for nouns and ver...

This paper investigates the major characteristics needed by the next generation knowledge organization. The paper then proposes five organizational characteristics that support sustainable competitive advantage within an environment of rapid change, high complexity and large uncertainty. Next, eight major system characteristics are proposed that will allow the organization of the future to surv...

We describe novel aspects of a new natural language generator called Nitrogen. This generator has a highly flexible input representation that allows a spectrum of input from syntactic to semantic depth, and shifts the burden of many linguistic decisions to the statistical post-processor. The generation algorithm is compositional, making it efficient, yet it also handles non-compositional aspect...

The traditional workflow design paradigm relies heavily on humans who statically specify business processes. However, such a manual design approach is not suitable for many cases: (a) Inter-agency workflows that cross autonomous organizational boundaries require experts who possess knowledge required for defining workflows composed of services from the constituent organizations; (b) Customized ...

When humans perform inductive learning, they often enhance the process with background knowledge. With the increasing availability of well-formed collaborative knowledge bases, the performance of learning algorithms could be significantly enhanced if a way were found to exploit these knowledge bases. In this work, we present a novel algorithm for injecting external knowledge into induction algo...

We believe that it is important from the practical point of view to use natural language generation (NLG) in real world applications. The key benefits are outlined as follows: • Higher quality of generated texts • Cost effective maintenance and adaptability • Usability•(including acceptability by the users). Generally, it takes "exotic" (linguists, knowledge engineers) manpower to maintain, and...

Knowledge Graph Embedding methods aim at representing entities and relations in a knowledge base as points or vectors in a continuous vector space. Several approaches using embeddings have shown promising results on tasks such as link prediction, entity recommendation, question answering, and triplet classification. However, only a few methods can compute low-dimensional embeddings of very larg...