Creating a Probabilistic Model for WordNet

We present a probabilistic model for extracting and storing information from WordNet and the British National Corpus. We map the data into a directed probabilistic graph that can be used to compute the conditional probability between a pair of words from the English language. For example, the graph can be used to deduce that there is a 10% probability that someone who is interested in dogs is also interested in the word “canine”. We propose three ways for computing this probability, where the best results are achieved when performing multiple random walks in the graph. Unlike existing approaches that only process the structured data in WordNet, we process all available information, including natural language descriptions. The available evidence is expressed as simple Horn clauses with probabilities. It is then aggregated using a Markov Logic Network model to create the probabilistic graph. We experimentally validate the quality of the data on five different benchmarks that contain collections of pairs of words and their semantic similarity as determined by humans. In the experimental section, we show that our random walk algorithm with logarithmic distance metric produces higher correlation with the results of the human judgment on three of the five benchmarks and better overall average correlation than the current state-of-the-art algorithms

Quantitative dynamics of design thinking and creativity perspectives in company context

This study is intended to provide in-depth insights into how design thinking and creativity issues are understood and possibly evolve in the course of design discussions in a company context. For that purpose, we use the seminar transcripts of the Design Thinking Research Symposium 12 (DTRS12) dataset “Tech-centred Design Thinking: Perspectives from a Rising Asia,” which are primarily concerned with how Korean companies implement design thinking and what role designers currently play. We employed a novel method of information processing based on constructed dynamic semantic networks to investigate the seminar discussions according to company representatives and company size. We compared the quantitative dynamics in two seminars: the first involved managerial representatives of four companies, and the second involved specialized designers and management of a design center of single company. On the basis of dynamic semantic networks, we quantified the changes in four semantic measures—abstraction, polysemy, information content, and pairwise word similarity—in chronologically reconstructed individual design-thinking processes. Statistical analyses show that design thinking in the seminar with four companies, exhibits significant differences in the dynamics of abstraction, polysemy, and information content, compared to the seminar with the design center of single company. Both the decrease in polysemy and abstraction and the increase in information content in the individual design-thinking processes in the seminar with four companies indicate that design managers are focused on more concrete design issues, with more information and less ambiguous content to the final design product. By contrast, specialized designers manifest more abstract thinking and appear to exhibit a slightly higher level of divergence in their design processes. The results suggest that design thinking and creativity issues are articulated differently depending on designer roles and the company size

HESML: A scalable ontology-based semantic similarity measures library with a set of reproducible experiments and a replication dataset

This work is a detailed companion reproducibility paper of the methods and experiments proposed by Lastra-Díaz and García-Serrano in (2015, 2016) [56–58], which introduces the following contributions: (1) a new and efficient representation model for taxonomies, called PosetHERep, which is an adaptation of the half-edge data structure commonly used to represent discrete manifolds and planar graphs; (2) a new Java software library called the Half-Edge Semantic Measures Library (HESML) based on PosetHERep, which implements most ontology-based semantic similarity measures and Information Content (IC) models reported in the literature; (3) a set of reproducible experiments on word similarity based on HESML and ReproZip with the aim of exactly reproducing the experimental surveys in the three aforementioned works; (4) a replication framework and dataset, called WNSimRep v1, whose aim is to assist the exact replication of most methods reported in the literature; and finally, (5) a set of scalability and performance benchmarks for semantic measures libraries. PosetHERep and HESML are motivated by several drawbacks in the current semantic measures libraries, especially the performance and scalability, as well as the evaluation of new methods and the replication of most previous methods. The reproducible experiments introduced herein are encouraged by the lack of a set of large, self-contained and easily reproducible experiments with the aim of replicating and confirming previously reported results. Likewise, the WNSimRep v1 dataset is motivated by the discovery of several contradictory results and difficulties in reproducing previously reported methods and experiments. PosetHERep proposes a memory-efficient representation for taxonomies which linearly scales with the size of the taxonomy and provides an efficient implementation of most taxonomy-based algorithms used by the semantic measures and IC models, whilst HESML provides an open framework to aid research into the area by providing a simpler and more efficient software architecture than the current software libraries. Finally, we prove the outperformance of HESML on the state-of-the-art libraries, as well as the possibility of significantly improving their performance and scalability without caching using PosetHERep

A Semantic neighborhood approach to relatedness evaluation on well-founded domain ontologies

In the context of natural language processing and information retrieval, ontologies can improve the results of the word sense disambiguation (WSD) techniques. By making explicit the semantics of the term, ontology-based semantic measures play a crucial role in determining how different ontology classes have a similar or related meaning. In this context, it is common to use semantic similarity as a basis for WSD. However, the measures generally consider only taxonomic relationships, which negatively affect the discrimination of two ontology classes that are related by the other relationship types. On the other hand, semantic relatedness measures consider diverse types of relationships to determine how much two classes on the ontology are related. However, these measures, especially the path-based approaches, have as the main drawback a high computational complexity to calculate the relatedness value. Also, for both types of semantic measures, it is unpractical to store all similarity or relatedness values between all ontology classes in memory, especially for ontologies with a large number of classes. In this work, we propose a novel approach based on semantic neighbors that aim to improve the performance of the knowledge-based measures in relatedness analysis. We also explain how to use this proposal into the path and feature-based measures. We evaluate our proposal on WSD using an existent domain ontology for a well-core description. This ontology contains 929 classes related to rock facies. Also, we use a set of sentences from four different corpora on the Oil&Gas domain. In the experiments, we compare our proposal with state-of-the-art semantic relatedness measures, such as path-based, feature-based, information content, and hybrid methods regarding the F-score, evaluation time, and memory consumption. The experimental results show that the proposed method obtains F-score gains in WSD, as well as a low evaluation time and memory consumption concerning the traditional knowledge-based measures.No contexto do processamento de linguagem natural e recuperação de informações, as ontologias podem melhorar os resultados das técnicas de desambiguação. Ao tornar explícita a semântica do termo, as medidas semânticas baseadas em ontologia desempenham um papel crucial para determinar como diferentes classes de ontologia têm um significado semelhante ou relacionado. Nesse contexto, é comum usar similaridade semântica como base para a desembiguação. No entanto, as medidas geralmente consideram apenas relações taxonômicas, o que afeta negativamente a discriminação de duas classes de ontologia relacionadas por outros tipos de relações. Por outro lado, as medidas de relacionamento semântico consideram diversos tipos de relacionamentos ontológicos para determinar o quanto duas classes estão relacionadas. No entanto, essas medidas, especialmente as abordagens baseadas em caminhos, têm como principal desvantagem uma alta complexidade computacional para sua execução. Além disso, tende a ser impraticável armazenar na memória todos os valores de similaridade ou relacionamento entre todas as classes de uma ontologia, especialmente para ontologias com um grande número de classes. Neste trabalho, propomos uma nova abordagem baseada em vizinhos semânticos que visa melhorar o desempenho das medidas baseadas em conhecimento na análise de relacionamento. Também explicamos como usar esta proposta em medidas baseadas em caminhos e características. Avaliamos nossa proposta na desambiguação utilizando uma ontologia de domínio preexistente para descrição de testemunhos. Esta ontologia contém 929 classes relacionadas a fácies de rocha. Além disso, usamos um conjunto de sentenças de quatro corpora diferentes no domínio Petróleo e Gás. Em nossos experimentos, comparamos nossa proposta com medidas de relacionamento semântico do estado-daarte, como métodos baseados em caminhos, características, conteúdo de informação, e métodos híbridos em relação ao F-score, tempo de avaliação e consumo de memória. Os resultados experimentais mostram que o método proposto obtém ganhos de F-score na desambiguação, além de um baixo tempo de avaliação e consumo de memória em relação às medidas tradicionais baseadas em conhecimento