Handling Granularity Differences in Knowledge Integration

Knowledge integration is a process of combining two different knowledge representations together. This task is important especially in learning where new information is combined with prior knowledge or in understanding where a coherent knowledge representation should be generated out of several knowledge fragments. A challenging problem in KI is handling granularity differences, i.e. combining together two knowledge representations with granularity differences. This paper presents an algorithm to find such correspondences between two representations with a granularity difference and to combine the two representations together based on the correspondences. The algorithm uses coarsening operators which generate coarse-grained representations from a representation. At the end, we introduce a large scale project in which the algorithm will be used. Knowledge Integration (KI) is a process of combining two pieces of knowledge together. KI is an important step in human activities such as learning, reading comprehension, reasoning and others. For example, in learning, one must combine the new knowledge with the old, and in reading comprehension, one must relate together individual sentences in a text which often differ in level of detail. Just as in human activities, KI is also important in many AI tasks such as knowledge acquisition, story understanding, and multidocument summarization. All these tasks commonly require the combination of several pieces of knowledge to generate a coherent knowledge representation. A challenging problem in KI is integrating together two knowledge representations with granularity differences. Simply putting the representations together without considering the granularity differences could result in an incorrect representation. Ideally, the two representations should be aligned together based on identification of which parts in the fine-grained correspond to which parts in the coarsegrained. This paper presents an algorithm to find the correspondences between two representations with a granularity difference and to combine the representations together based Copyright c © 2007, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. In this paper, by knowledge, we mean factual knowledge which can be represented by logic. We will use conceptual graphs or logical predicates as our notation. on the correspondences. The algorithm uses coarsening operators which generate coarse-grained representations from a representation. Example This section compares a good and a bad KI on the two texts below about blood circulation. These texts used in the learning-by-reading project (Barker et al. 2007) were excerpted from a biology textbook. Fig. 1a and fig. 1b show the encoding of the long and the short text respectively. The Component Library (Barker, Porter, & Clark 2001) was used as the ontology for the encoding. Text1: Hearts pump blood through the body. Blood carries oxygen to organs throughout the body. Blood leaves the heart, then goes to the lungs where it is oxygenated. The oxygen given to the blood by the lungs is then burned by organs throughout the body. Eventually the blood returns to the heart, depleted of oxygen. It is then pumped by the heart back to the lungs. Text2: Heart pumps blood to circulate around the body. Fig. 2 shows a bad combination by a simple graph join (Sowa 1984) on the two representations in fig. 1. In this combination, Move3 and Move1 are unified together. Notice that Move3 is from the coarse-grained representation (fig. 1a), and Move1 is from the fine-grained representation(fig. 1b). Consequently, Move13, resulting from the unification of Move1 and Move3, comes to have two destinations (Lung1 and Heart12), such that its English interpretation would be “Blood moves from the Heart to the Heart and the Lung.” In contrast, fig. 3 illustrates a good combination in which Move1 is identified as a superevent of Obtain1, Move1, Carry1, Move2, and thus subevent relations are estabilished between Move1 and Obtain1, Move1, Carry1, Move2. The English interpretation for this combination would be Blood moves from Heart to Heart. This Due to the limitation of space, some information in the long text is not encoded.