We discuss the problems associated with versioning ontologies in distributed environments. This is an important issue because ontologies can be of great use in structuring and querying intemet information, but many of the Intemet's characteristics, such as distributed ownership, rapid evolution, and heterogeneity, make ontology management difficult. We present SHOE, a web-based knowledge representation language that supports multiple versions of ontologies. We then discuss the features of SHOE that address ontology versioning, the affects of ontology revision on SHOE web pages, and methods for implementing ontology integration using SHOE's extension and version mechanisms. 1. Introduction As the use of ontologies becomes more prevalent, there is a more pressing need for good ontology management schemes. This is especially true once an ontology has been used to structure data, since changing it can be very expensive. Often the solution is to "get it right the first time", however, in long term applications, there is always the chance that new information will be discovered or that different features of the domain will become important. Therefore, we must think of ontology development as an ongoing process. In a centralized environment, it may be possible to coordinate ontology revisions with corresponding revisions to the data that was structured using the ontology. However, as the volume of data increases this become more difficult.
In this paper we explain how merging of ontologies is captured by the pushout construction from category theory, and argue that this is a very natural approach to the problem. We study this independent of a specific choice of ontology representation language, and thus provide a sort of blueprint for the development of algorithms applicable in practice. For this purpose, we view category theory as a universal "meta specification language" that enables us to specify properties of ontological relationships and constructions in a way that does not depend on any particular implementation. This can be achieved since the basic objects of study in category theory are the relationships between multiple ontological specifications, not the internal structure of a single knowledge representation. Categorical pushouts are already considered in some approaches to ontology research (Jannink et al. 1998; Schorlemmer, Potter, & Robertson 2002; Goguen 2005; Kent 2005) and we do not claim our treatment to be entirely original.
Building ontologies is a difficult task requiring skills in logics and ontological analysis. Domain experts usually reach as far as organizing a set of concepts into a hierarchy in which the semantics of the relations is under-specified. The categorization of Wikipedia is a huge concept hierarchy of this form, covering a broad range of areas. We propose an automatic method for bootstrapping domain ontologies from the categories of Wikipedia. The method first selects a subset of concepts that are relevant for a given domain. The relevant concepts are subsequently split into classes and individuals, and, finally, the relations between the concepts are classified into subclass_of, instance_of, part_of, and generic related_to. We evaluate our method by generating ontology skeletons for the domains of Computing and Music. The quality of the generated ontologies has been measured against manually built ground truth datasets of several hundred nodes.
Chenthamarakshan, Vijil (IBM T J Watson Research Center Yorktown Heights) | Melville, Prem (IBM T J Watson Research Center Yorktown Heights) | Sindhwani, Vikas (IBM T J Watson Research Center Yorktown Heights) | Lawrence, Richard D (IBM T J Watson Research Center Yorktown Heights)
The rapid construction of supervised text classification models is becoming a pervasive need across many modern applications. To reduce human-labeling bottlenecks, many new statistical paradigms (e.g., active, semi-supervised, transfer and multi-task learning) have been vigorously pursued in recent literature with varying degrees of empirical success. Concurrently, the emergence of Web 2.0 platforms in the last decade has enabled a world-wide, collaborative human effort to construct a massive ontology of concepts with very rich, detailed and accurate descriptions. In this paper we propose a new framework to extract supervisory information from such ontologies and complement it with a shift in human effort from direct labeling of examples in the domain of interest to the much more efficient identification of concept-class associations. Through empirical studies on text categorization problems using the Wikipedia ontology, we show that this shift allows very high-quality models to be immediately induced at virtually no cost.
In this paper we discuss moral ontology and reasoning as it relates to HRI. We review several theories of ought(A), where we examine the meaning of "ought" and the ontological status of A, e.g. as abstract properties of acts, individual act propositions, states-of-affairs, and practitions. We discuss several forms of moral deliberation based on those theories. We argue that a discussion of moral ontology and reasoning will play a role in understanding the ethical and social implications of HRI, and we offer three conjectures towards that end.