A Survey of Exploiting WordNet in Ontology Matching
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paper_title: Using Corpus Statistics And WordNet Relations For Sense Identification

paper_content:

Corpus-based approaches to word sense identification have flexibility and generality but suffer from a knowledge acquisition bottleneck. We show how knowledge-based techniques can be used to open the bottleneck by automatically locating training corpora. We describe a statistical classifier that combines topical context with local cues to identify a word sense. The classifier is used to disambiguate a noun, a verb, and an adjective. A knowledge base in the form of WordNet's lexical relations is used to automatically locate training examples in a general text corpus. Test results are compared with those from manually tagged training examples.
---
paper_title: Determining Semantic Similarity among Entity Classes from Different Ontologies

paper_content:

Semantic similarity measures play an important role in information retrieval and information integration. Traditional approaches to modeling semantic similarity compute the semantic distance between definitions within a single ontology. This single ontology is either a domain-independent ontology or the result of the integration of existing ontologies. We present an approach to computing semantic similarity that relaxes the requirement of a single ontology and accounts for differences in the levels of explicitness and formalization of the different ontology specifications. A similarity function determines similar entity classes by using a matching process over synonym sets, semantic neighborhoods, and distinguishing features that are classified into parts, functions, and attributes. Experimental results with different ontologies indicate that the model gives good results when ontologies have complete and detailed representations of entity classes. While the combination of word matching and semantic neighborhood matching is adequate for detecting equivalent entity classes, feature matching allows us to discriminate among similar, but not necessarily equivalent entity classes.
---
paper_title: Semantic Similarity Based on Corpus Statistics and Lexical Taxonomy

paper_content:

This paper presents a new approach for measuring semantic similarity/distance between words and concepts. It combines a lexical taxonomy structure with corpus statistical information so that the semantic distance between nodes in the semantic space constructed by the taxonomy can be better quantified with the computational evidence derived from a distributional analysis of corpus data. Specifically, the proposed measure is a combined approach that inherits the edge-based approach of the edge counting scheme, which is then enhanced by the node-based approach of the information content calculation. When tested on a common data set of word pair similarity ratings, the proposed approach outperforms other computational models. It gives the highest correlation value (r = 0.828) with a benchmark based on human similarity judgements, whereas an upper bound (r = 0.885) is observed when human subjects replicate the same task.
---
paper_title: Semantic Enrichment for Ontology Mapping

paper_content:

In this paper, we present a heuristic mapping method and a prototype mapping system that support the process of semi-automatic ontology mapping for the purpose of improving semantic interoperability in heterogeneous systems. The approach is based on the idea of semantic enrichment, i.e. using instance information of the ontology to enrich the original ontology and calculate similarities between concepts in two ontologies. The functional settings for the mapping system are discussed and the evaluation of the prototype implementation of the approach is reported.
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paper_title: Verb Semantics And Lexical Selection

paper_content:

This paper will focus on the semantic representation of verbs in computer systems and its impact on lexical selection problems in machine translation (MT). Two groups of English and Chinese verbs are examined to show that lexical selection must be based on interpretation of the sentences as well as selection restrictions placed on the verb arguments. A novel representation scheme is suggested, and is compared to representations with selection restrictions used in transfer-based MT. We see our approach as closely aligned with knowledge-based MT approaches (KBMT), and as a separate component that could be incorporated into existing systems. Examples and experimental results will show that, using this scheme, inexact matches can achieve correct lexical selection.
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paper_title: Using Information Content to Evaluate Semantic Similarity in a Taxonomy

paper_content:

This paper presents a new measure of semantic similarity in an IS-A taxonomy, based on the notion of information content. Experimental evaluation suggests that the measure performs encouragingly well (a correlation of r = 0.79 with a benchmark set of human similarity judgments, with an upper bound of r = 0.90 for human subjects performing the same task), and significantly better than the traditional edge counting approach (r = 0.66).
---
paper_title: Semantic Similarity Based on Corpus Statistics and Lexical Taxonomy

paper_content:

This paper presents a new approach for measuring semantic similarity/distance between words and concepts. It combines a lexical taxonomy structure with corpus statistical information so that the semantic distance between nodes in the semantic space constructed by the taxonomy can be better quantified with the computational evidence derived from a distributional analysis of corpus data. Specifically, the proposed measure is a combined approach that inherits the edge-based approach of the edge counting scheme, which is then enhanced by the node-based approach of the information content calculation. When tested on a common data set of word pair similarity ratings, the proposed approach outperforms other computational models. It gives the highest correlation value (r = 0.828) with a benchmark based on human similarity judgements, whereas an upper bound (r = 0.885) is observed when human subjects replicate the same task.
---
paper_title: Determining Semantic Similarity among Entity Classes from Different Ontologies

paper_content:

Semantic similarity measures play an important role in information retrieval and information integration. Traditional approaches to modeling semantic similarity compute the semantic distance between definitions within a single ontology. This single ontology is either a domain-independent ontology or the result of the integration of existing ontologies. We present an approach to computing semantic similarity that relaxes the requirement of a single ontology and accounts for differences in the levels of explicitness and formalization of the different ontology specifications. A similarity function determines similar entity classes by using a matching process over synonym sets, semantic neighborhoods, and distinguishing features that are classified into parts, functions, and attributes. Experimental results with different ontologies indicate that the model gives good results when ontologies have complete and detailed representations of entity classes. While the combination of word matching and semantic neighborhood matching is adequate for detecting equivalent entity classes, feature matching allows us to discriminate among similar, but not necessarily equivalent entity classes.
---
paper_title: Using Corpus Statistics And WordNet Relations For Sense Identification

paper_content:

Corpus-based approaches to word sense identification have flexibility and generality but suffer from a knowledge acquisition bottleneck. We show how knowledge-based techniques can be used to open the bottleneck by automatically locating training corpora. We describe a statistical classifier that combines topical context with local cues to identify a word sense. The classifier is used to disambiguate a noun, a verb, and an adjective. A knowledge base in the form of WordNet's lexical relations is used to automatically locate training examples in a general text corpus. Test results are compared with those from manually tagged training examples.
---
paper_title: Determining Semantic Similarity among Entity Classes from Different Ontologies

paper_content:

Semantic similarity measures play an important role in information retrieval and information integration. Traditional approaches to modeling semantic similarity compute the semantic distance between definitions within a single ontology. This single ontology is either a domain-independent ontology or the result of the integration of existing ontologies. We present an approach to computing semantic similarity that relaxes the requirement of a single ontology and accounts for differences in the levels of explicitness and formalization of the different ontology specifications. A similarity function determines similar entity classes by using a matching process over synonym sets, semantic neighborhoods, and distinguishing features that are classified into parts, functions, and attributes. Experimental results with different ontologies indicate that the model gives good results when ontologies have complete and detailed representations of entity classes. While the combination of word matching and semantic neighborhood matching is adequate for detecting equivalent entity classes, feature matching allows us to discriminate among similar, but not necessarily equivalent entity classes.
---
paper_title: Finding Out About: A Cognitive Perspective on Search Engine Technology and the WWW

paper_content:

The World Wide Web is rapidly filling with more text than anyone could have imagined a short time ago. However, the task of determining which data is relevant has become appreciably harder. In this original new work Richard Belew brings a cognitive science perspective to the study of information as a computer science discipline. He introduces the idea of Finding Out About (FOA), the process of actively seeking out information relevant to a topic of interest. Belew describes all facets of FOA, ranging from creating a good characterization of what the user seeks to evaluating the successful performance of search engines. His volume clearly shows how to build many of the tools that are useful for searching collections of text and other media. While computer scientists make up the book's primary audience, Belew skillfully presents technical details in a manner that makes important themes accessible to readers more comfortable with words than equations. (A CD is included with the text.)
---
paper_title: Semantic Enrichment for Ontology Mapping

paper_content:

In this paper, we present a heuristic mapping method and a prototype mapping system that support the process of semi-automatic ontology mapping for the purpose of improving semantic interoperability in heterogeneous systems. The approach is based on the idea of semantic enrichment, i.e. using instance information of the ontology to enrich the original ontology and calculate similarities between concepts in two ontologies. The functional settings for the mapping system are discussed and the evaluation of the prototype implementation of the approach is reported.
---
paper_title: Investigating semantic similarity measures across the Gene Ontology:

paper_content:

Motivation: Many bioinformatics data resources not only hold data in the form of sequences, but also as annotation. In the majority of cases, annotation is written as scientific natural language: this is suitable for humans, but not particularly useful for machine processing. Ontologies offer a mechanism by which knowledge can be represented in a form capable of such processing. In this paper we investigate the use of ontological annotation to measure the similarities in knowledge content or ‘semantic similarity’ between entries in a data resource. These allow a bioinformatician to perform a similarity measure over annotation in an analogous manner to those performed over sequences. Am easure of semantic similarity for the knowledge component of bioinformatics resources should afford a biologist a new tool in their repetoire of analyses. Results: We present the results from experiments that investigate the validity of using semantic similarity by comparison with sequence similarity. We show a simple extension that enables a semantic search of the knowledge held within sequence databases. Availability: Software available from http://www.russet.
---
paper_title: Semantic Similarity Based on Corpus Statistics and Lexical Taxonomy

paper_content:

This paper presents a new approach for measuring semantic similarity/distance between words and concepts. It combines a lexical taxonomy structure with corpus statistical information so that the semantic distance between nodes in the semantic space constructed by the taxonomy can be better quantified with the computational evidence derived from a distributional analysis of corpus data. Specifically, the proposed measure is a combined approach that inherits the edge-based approach of the edge counting scheme, which is then enhanced by the node-based approach of the information content calculation. When tested on a common data set of word pair similarity ratings, the proposed approach outperforms other computational models. It gives the highest correlation value (r = 0.828) with a benchmark based on human similarity judgements, whereas an upper bound (r = 0.885) is observed when human subjects replicate the same task.
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Title: A Survey of Exploiting WordNet in Ontology Matching

Section 1: Introduction
Description 1: Provide an overview of the importance of ontologies in the Semantic Web and the need for ontology matching. Introduce the role of WordNet in enhancing similarity measures.

Section 2: WordNet
Description 2: Describe the structure and components of WordNet, including synsets and various semantic relationships. Discuss the multilingual extension EuroWordNet.

Section 3: Exploiting WordNet in Ontology Matching
Description 3: Outline the use of WordNet in calculating semantic similarity for ontology matching. Categorize methods into edge-based, information-based statistics, and hybrid methods.

Section 4: Edge-based Methods
Description 4: Detail various edge-based methods for calculating semantic similarity using WordNet. Include specific algorithms like those proposed by Wu and Palmer, Resnik, and others.

Section 5: Information-based Statistics Methods
Description 5: Explain information-based statistics methods for semantic similarity, including Resnik's approach and Lin's adaptation. Discuss their application in ontology matching.

Section 6: Hybrid Methods
Description 6: Discuss combined models that integrate edge-based and information-based approaches. Provide examples such as Jiang and Conrath's model, Rodriguez's approach, and others.

Section 7: Applying WordNet Based Semantic Similarity Methods in Ontology Matching
Description 7: Describe the preprocessing steps involved in applying WordNet based methods, such as tokenisation, stemming, and stop-word removal. Explain how semantic similarity methods are applied in practical ontology matching scenarios.

Section 8: Conclusions
Description 8: Summarize the different WordNet-based semantic similarity measures and their applications in ontology matching. Highlight the tools that implement these measures and their effectiveness.