11,685 research outputs found
Syntactic vs. Semantic Locality: How Good Is a Cheap Approximation?
Extracting a subset of a given OWL ontology that captures all the ontology's
knowledge about a specified set of terms is a well-understood task. This task
can be based, for instance, on locality-based modules (LBMs). These come in two
flavours, syntactic and semantic, and a syntactic LBM is known to contain the
corresponding semantic LBM. For syntactic LBMs, polynomial extraction
algorithms are known, implemented in the OWL API, and being used. In contrast,
extracting semantic LBMs involves reasoning, which is intractable for OWL 2 DL,
and these algorithms had not been implemented yet for expressive ontology
languages. We present the first implementation of semantic LBMs and report on
experiments that compare them with syntactic LBMs extracted from real-life
ontologies. Our study reveals whether semantic LBMs are worth the additional
extraction effort, compared with syntactic LBMs
Tractable approximate deduction for OWL
Acknowledgements This work has been partially supported by the European project Marrying Ontologies and Software Technologies (EU ICT2008-216691), the European project Knowledge Driven Data Exploitation (EU FP7/IAPP2011-286348), the UK EPSRC project WhatIf (EP/J014354/1). The authors thank Prof. Ian Horrocks and Dr. Giorgos Stoilos for their helpful discussion on role subsumptions. The authors thank Rafael S. Gonçalves et al. for providing their hotspots ontologies. The authors also thank BoC-group for providing their ADOxx Metamodelling ontologies.Peer reviewedPostprin
A Semantic Similarity Measure for Expressive Description Logics
A totally semantic measure is presented which is able to calculate a
similarity value between concept descriptions and also between concept
description and individual or between individuals expressed in an expressive
description logic. It is applicable on symbolic descriptions although it uses a
numeric approach for the calculus. Considering that Description Logics stand as
the theoretic framework for the ontological knowledge representation and
reasoning, the proposed measure can be effectively used for agglomerative and
divisional clustering task applied to the semantic web domain.Comment: 13 pages, Appeared at CILC 2005, Convegno Italiano di Logica
Computazionale also available at
http://www.disp.uniroma2.it/CILC2005/downloads/papers/15.dAmato_CILC05.pd
Is Structure Necessary for Modeling Argument Expectations in Distributional Semantics?
Despite the number of NLP studies dedicated to thematic fit estimation,
little attention has been paid to the related task of composing and updating
verb argument expectations. The few exceptions have mostly modeled this
phenomenon with structured distributional models, implicitly assuming a
similarly structured representation of events. Recent experimental evidence,
however, suggests that human processing system could also exploit an
unstructured "bag-of-arguments" type of event representation to predict
upcoming input. In this paper, we re-implement a traditional structured model
and adapt it to compare the different hypotheses concerning the degree of
structure in our event knowledge, evaluating their relative performance in the
task of the argument expectations update.Comment: conference paper, IWC
Thematic roles – universal, particular, and idiosyncratic aspects
Thematic Roles (or Theta-Roles) are theoretical constructs that account for a variety of well known empirical facts, which are more or less clearly delimited. In other words, Theta-Roles are not directly observable, but they do have empirical content that is open to empirical observation. The objective of the present paper is to sketch the nature and content of Theta-Roles, distinguishing their universal foundation as part of the language faculty, their language particular realization, which depends on the conditions of individual languages, and idiosyncratic properties, determined by specific information of individual lexical items
Refrain from Standards? French, Cavemen and Computers. A (short) Story of Multidimensional Analysis in French Prehistoric Archaeology
Focusing on the history of prehistoric archaeology in the 20th century, this papers shows (1) that statistical multidimensional analyses were carried out by a new kind of actors who challenged the previous common language shared by prehistorians. This fundamental change was important, considering that (2) language is a fundamental point for the epistemology of archaeology. However, a comparison of multidimensional analyses applications over time shall make clear that (3) the differences are mostly a generational matter: the transmission processes between them will be addressed
Tensor Product Generation Networks for Deep NLP Modeling
We present a new approach to the design of deep networks for natural language
processing (NLP), based on the general technique of Tensor Product
Representations (TPRs) for encoding and processing symbol structures in
distributed neural networks. A network architecture --- the Tensor Product
Generation Network (TPGN) --- is proposed which is capable in principle of
carrying out TPR computation, but which uses unconstrained deep learning to
design its internal representations. Instantiated in a model for image-caption
generation, TPGN outperforms LSTM baselines when evaluated on the COCO dataset.
The TPR-capable structure enables interpretation of internal representations
and operations, which prove to contain considerable grammatical content. Our
caption-generation model can be interpreted as generating sequences of
grammatical categories and retrieving words by their categories from a plan
encoded as a distributed representation
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