Knowledge representation and reasoning

About: Knowledge representation and reasoning is a research topic. Over the lifetime, 20078 publications have been published within this topic receiving 446310 citations. The topic is also known as: KR & KR².

Retrieval effectiveness of an ontology-based model for information selection

Abstract: Technology in the field of digital media generates huge amounts of nontextual information, audio, video, and images, along with more familiar textual information. The potential for exchange and retrieval of information is vast and daunting. The key problem in achieving efficient and user-friendly retrieval is the development of a search mechanism to guarantee delivery of minimal irrelevant information (high precision) while insuring relevant information is not overlooked (high recall). The traditional solution employs keyword-based search. The only documents retrieved are those containing user-specified keywords. But many documents convey desired semantic information without containing these keywords. This limitation is frequently addressed through query expansion mechanisms based on the statistical co-occurrence of terms. Recall is increased, but at the expense of deteriorating precision. One can overcome this problem by indexing documents according to context and meaning rather than keywords, although this requires a method of converting words to meanings and the creation of a meaning-based index structure. We have solved the problem of an index structure through the design and implementation of a concept-based model using domain-dependent ontologies. An ontology is a collection of concepts and their interrelationships that provide an abstract view of an application domain. With regard to converting words to meaning, the key issue is to identify appropriate concepts that both describe and identify documents as well as language employed in user requests. This paper describes an automatic mechanism for selecting these concepts. An important novelty is a scalable disambiguation algorithm that prunes irrelevant concepts and allows relevant ones to associate with documents and participate in query generation. We also propose an automatic query expansion mechanism that deals with user requests expressed in natural language. This mechanism generates database queries with appropriate and relevant expansion through knowledge encoded in ontology form. Focusing on audio data, we have constructed a demonstration prototype. We have experimentally and analytically shown that our model, compared to keyword search, achieves a significantly higher degree of precision and recall. The techniques employed can be applied to the problem of information selection in all media types.

Combining the Expressivity of UCPOP with the Efficiency of Graphplan

Abstract: There has been a great deal of recent work on new approaches to efficiently generating plans in systems such as Graphplan and SATplan However, these systems only provide an impoverished representation language compared to other planners, such as UCPOP, ADL, or Prodigy This makes it difficult to represent planning problems using these new planners This paper addresses this problem by providing a completely automated set of transformations for converting a UCPOP domain representation into a Graphplan representation The set of transformations extends the Graphplan representation language to include disjunctions, negations, universal quantification, conditional effects, and axioms We tested the resulting planner on the 18 test domains and 41 problems that come with the UCPOP 40 distribution Graphplan with the new preprocessor is able to solve every problem in the test set and on the hard problems (ie, those that require more than one second of CPU time) it can solve them significantly faster than UCPOP While UCPOP was unable to solve 7 of the test problems within a search limit of 100,000 nodes (which requires 414 to 980 CPU seconds), Graphplan with the preprocessor solved them all in under 15 CPU seconds (including the preprocessing time)

Method and apparatus for frame-based knowledge representation in the unified modeling language (UML)

Abstract: According to an embodiment of the present invention, there is provided a method and system for emulating a knowledge representation in a Unified Modeling Language (UML) environment. A Meta-Object Facility metamodel and UML profile are grounded in a foundation ontology. The elements representing the knowledge representation ontology are mapped to elements of UML, based on the grounded Meta-Object Facility metamodel and UML profile, thereby emulating the knowledge representation in a UML environment.