Nuno Seco

Design, Implementation and Evaluation of a New Semantic Similarity Metric Combining Features and Intrinsic Information Content

In many research fields such as Psychology, Linguistics, Cognitive Science, Biomedicine, and Artificial Intelligence, computing semantic similarity between words is an important issue. In this paper we present a new semantic similarity metric that exploits some notions of the early work done using a feature based theory of similarity, and translates it into the information theoretic domain which leverages the notion of Information Content (IC). In particular, the proposed metric exploits the notion of intrinsic IC which quantifies IC values by scrutinizing how concepts are arranged in an ontological structure. In order to evaluate this metric, we conducted an on line experiment asking the community of researchers to rank a list of 65 word pairs. The experiments web setup allowed to collect 101 similarity ratings, and to differentiate native and non-native English speakers. Such a large and diverse dataset enables to confidently evaluate similarity metrics by correlating them with human assessments. Experimental evaluations using WordNet indicate that our metric, coupled with the notion of intrinsic IC, yields results above the state of the art. Moreover, the intrinsic IC formulation also improves the accuracy of other IC based metrics. We implemented our metric and several others in the Java WordNet Similarity Library.

The importance of retrieval in creative design analogies

Analogy is an important reasoning process in creative design. It enables the generation of new design artifacts using ideas from semantically distant domains. Candidate selection is a crucial process in the generation of creative analogies. Without a good set of candidate sources, the success of subsequent phases can be compromised. Two main types of selection have been identified: semantics-based retrieval and structure-based retrieval. This paper presents an empirical study on the importance of the analogy retrieval strategy in the domain of software design. We argue that both types of selection are important, but they play different roles in the process.

Using CBR for Automation of Software Design Patterns

Software design patterns are used in software engineering as a way to improve and maintain software systems. Patterns are abstract solutions to problem categories, and they describe why, how, and when can a pattern be applied. Their description is based on natural language, which makes the automation of design patterns a difficult task. In this paper we present an approach for automation of design pattern application. We focus on the selection of what pattern to apply, and where to apply it. We follow a Case-Based Reasoning approach, providing a complete framework for pattern application. In our approach cases describe situations for application of patterns.

PAPEL: A Dictionary-Based Lexical Ontology for Portuguese

This paper describes a project aimed at creating a lexical ontology extracted (semi) automatically from a large Portuguese general dictionary. Although using machine readable dictionaries to extract semantic information is not new, we believe this is the first attempt for the Portuguese language. The paper describes a (to be) freely available resource, dubbed PAPEL, explaining the process used and the tools developed, and illustrating it with one specific relation: Causation.

Hyponymy Extraction and Web Search Behavior Analysis Based on Query Reformulation

A web search engine log is a very rich source of semantic knowledge. In this paper we focus on the extraction of hyponymy relations from individual user sessions by examining, search behavior. The results obtained allow us to identify specific reformulation models as ones that more frequently represent hyponymy relations. The extracted relations reflect the knowledge that the user is employing while searching the web. Simultaneously, this study leads to a better understanding of web user search behavior.

Using ontologies for software development knowledge reuse

As software systems become bigger and more complex, software developers need to cope with a growing amount of information and knowledge. The knowledge generated during the software development process can be a valuable asset for a software company. But in order to take advantage of this knowledge, the company must store and manage it for reuse. Ontologies are a powerful mechanism for representing knowledge and encoding its meaning. These structures can be used to model and represent the knowledge, stored in a knowledge management system, and classify it according to the knowledge domain that the system supports. This paper describes the Semantic Reuse System (SRS), which takes advantage of ontologies, represented using the knowledge representation languages of the Semantic Web, for software development knowledge reuse. We describe how this knowledge is stored and the reasoning mechanisms that support the reuse.

Noun sense disambiguation with wordnet for software design retrieval

Natural language understanding can be used to improve the usability of intelligent Computer Aided Software Engineering (CASE) tools. For a software designer it can be helpful in two ways: a broad range of natural language terms in the naming of software objects, attributes and methods can be used; and the system is able to understand the meaning of these terms so that it could use them in reasoning mechanisms like information retrieval. But, the problem of word sense disambiguation is an obstacle to the development of computational systems that can fully understand natural language. In order to deal with this problem, this paper presents a word sense disambiguation method and how it is integrated with a CASE tool.

Knowledge management using semantic web technologies: an application in software development

As software systems become bigger and more complex, software developers need to cope with a growing amount of information and knowledge. New supporting toolsfor management of knowledge created during softwaredevelopment and maintenance is a necessity felt in thesoftware industry. The knowledge generated during the software development process can be a valuable asset for a software company. But in order to take advantage of this knowledge, the company must acquire, store andmanage it for reuse. This paper describes SRS (Semantic Reuse System), a system for management and reuse of software development knowledge based on Semantic Web technologies. SRS enables software developers to submit knowledge that the system then analyzes and indexes for later use. We describe how the knowledge is represented and how the reasoning mechanisms support knowledge reuse.

A complex evaluation architecture for HAREM

In this paper we briefly describe the evaluation architecture and the measures employed in HAREM, the first evaluation contest for named entity recognition in Portuguese. All programs are publically available for experimentation.

Creative discovery in lexical ontologies

Compound terms play a surprisingly key role in the organization of lexical ontologies. However, their inclusion forces one to address the issues of completeness and consistency that naturally arise from this organizational role. In this paper we show how creative exploration in the space of literal compounds can reveal not only additional compound terms to systematically balance an ontology, but can also discover new and potentially innovative concepts in their own right.