This work presents a method founded in instancebased learning for inductive (memory-based) reasoning on ABoxes. The method, which exploits a semantic dissimilarity measure between concepts and instances, can be employed both to answer class membership queries and to predict new assertions that may be not logically entailed by the knowledge base. In a preliminary experimentation, we show that th...

This paper proposes an instance-based learning approach for the ontology matching problem. This approach is applicable to scenarios where instances of the ontologies to be matched are exchanged between sources. An initial population of instances is used as a training set of a non-supervised algorithm that constructs mappings between properties of classes from the ontologies. To demonstrate the ...

There are some viruses and bacteria that have been identified as bioterrorism weapons. However, there are a lot other viruses and bacteria that can be potential bioterrorism weapons. A system that can automatically suggest potential bioterrorism weapons will help laypeople to discover these suspicious viruses and bacteria. In this paper we apply instance-based learning & text mining approach to...

Empirical studies in inductive language learning point at pure memory-based learning as a successful approach to many language learning tasks, often performing better than lerning methods that abstract from the learning material. The possibility is left open, however, that limited, careful abstraction in memory-based learning may be harmless to generalisation, as long as the disjunctivity of la...

In this paper we present a memory-based semantic role labeling (SRL) system for Catalan and Spanish. We approach the SRL task as two distinct classification problems: the assignment of semantic roles to arguments of verbs, and the assignment of a semantic class to verbs. We hypothesize that the two tasks can be solved in a uniform way, for both languages. Building on the same pool of features r...

An ACT-R model of sensemaking in a geospatial intelligence task was developed based on Instance-Based Learning Theory (IBLT). The model (a) maintains hypotheses about the probability of attacks by insurgent groups, (b) seeks new information based on those hypotheses, and (c) updates hypotheses based on new evidence. The model provides a functional account of how these sensemaking processes are ...

In this work we research the effect of micro-context on a memory-based learning (MBL) system for word sense disambiguation. We report results achieved on the data set provided by the English Lexical Sample Task introduced in the Senseval 3 competition. Our study revisits the belief that the disambiguation task profits more from a wider context and indicates that in reality system performance is...

We describe the Dutch word sense disambiguation data submitted to SENSEVAL-2, and give preliminary results on the data using a WSD system based on memory-based learning and statistical keyword selection.

This paper is concerned with the task of preposition generation in the context of a grammar checker. Relevant features for this task can range from lexical features, such as words and their part-ofspeech tags in the vicinity of the preposition, to syntactic features that take into account the attachment site of the prepositional phrase (PP), as well as its argument/adjunct distinction. We compa...

The dynamics of cooperation in repeated Prisoner's Dilemma (PD) interactions are captured by an instance-based learning model that assumes dynamic adjustment of expected outcomes (IBL-PD model). This research presents this model’s predictions across a large number of PD payoff matrices, in the absence of human data. Rapoport and Chammah (1965) test three hypotheses in a large set of PD payoff m...