Ontologies tend to be found everywhere. They are viewed as the silver bullet for many applications, such as database integration, peer-to-peer systems, e-commerce, semantic web services, or social networks. However, in open or evolving systems, such as the semantic web, different parties would, in general, adopt different ontologies. Thus, merely using ontologies, like using XML, does not reduce heterogeneity: it just raises heterogeneity problems to a higher level. Euzenat and Shvaikos book is devoted to ontology matching as a solution to the semantic heterogeneity problem faced by computer systems. Ontology matching aims at finding correspondences between semantically related entities of different ontologies. These correspondences may stand for equivalence as well as other relations, such as consequence, subsumption, or disjointness, between ontology entities. Many different matching solutions have been proposed so far from various viewpoints, e.g., databases, information systems, and artificial intelligence. The second edition of Ontology Matching has been thoroughly revised and updated to reflect the most recent advances in this quickly developing area, which resulted in more than 150 pages of new content. In particular, the book includes a new chapter dedicated to the methodology for performing ontology matching. It also covers emerging topics, such as data interlinking, ontology partitioning and pruning, context-based matching, matcher tuning, alignment debugging, and user involvement in matching, to mention a few. More than 100 state-of-the-art matching systems and frameworks were reviewed. With Ontology Matching, researchers and practitioners will find a reference book that presents currently available work in a uniform framework. In particular, the work and the techniques presented in this book can be equally applied to database schema matching, catalog integration, XML schema matching and other related problems. The objectives of the book include presenting (i) the state of the art and (ii) the latest research results in ontology matching by providing a systematic and detailed account of matching techniques and matching systems from theoretical, practical and application perspectives.

Ontology Matching

This article presents a measure of semantic similarity in an IS-A taxonomy based on the notion of shared information content. Experimental evaluation against a benchmark set of human similarity judgments demonstrates that the measure performs better than the traditional edge-counting approach. The article presents algorithms that take advantage of taxonomic similarity in resolving syntactic and semantic ambiguity, along with experimental results demonstrating their effectiveness.

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Semantic similarity in a taxonomy: an information-based measure and its application to problems of ambiguity in natural language

Classic IR (information retrieval) is inherently predicated on users searching for information, the so-called "information need". But the need behind a web search is often not informational -- it might be navigational (give me the url of the site I want to reach) or transactional (show me sites where I can perform a certain transaction, e.g. shop, download a file, or find a map). We explore this taxonomy of web searches and discuss how global search engines evolved to deal with web-specific needs.

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A taxonomy of web search

An intelligent automated assistant system engages with the user in an integrated, conversational manner using natural language dialog, and invokes external services when appropriate to obtain information or perform various actions. The system can be implemented using any of a number of different platforms, such as the web, email, smartphone, and the like, or any combination thereof. In one embodiment, the system is based on sets of interrelated domains and tasks, and employs additional functionally powered by external services with which the system can interact.

Intelligent Automated Assistant

Researchers in both machine translation (e.g., Brown et al., 1990) and bilingual lexicography (e.g., Klavans and Tzoukermann, 1990) have recently become interested in studying parallel texts, texts such as the Canadian Hansards (parliamentary proceedings) which are available in multiple languages (French and English). This paper describes a method for aligning sentences in these parallel texts, based on a simple statistical model of character lengths. The method was developed and tested on a small trilingual sample of Swiss economic reports. A much larger sample of 90 million words of Canadian Hansards has been aligned and donated to the ACL/DCI.

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A program for aligning sentences in bilingual corpora

This paper deals with the treatment of high vowels /i y u/ in Canadian French Text-to-Speech system. Among significant differences in the structure of the vowel system, we focused on two aspects in which high vowels behave quite differently in the two dialects of French: 1) the tenseness rule according to which the three vowels are systematically contrasted by the tense/lax feature in Canadian French; 2) the reduction-deletion rule. Reduction and deletion are frequently observed at the phonetic level in Canadian dialect. Moreover, both variety of French exhibit some effects of devoicing and vowel shortening besides rule 2. In order to determine the effect of geographic origin and linguistic style on the phonetic features of /i y u/ we performed analysis on corpora recorded by two professional speakers in the two dialects of French. The outcome of the comparison allowed us to create a set of new rules for the treatment of high vowels in the Canadian version of the Bell Labs TTS system.

High vowel /i y u/ in canadian and continental French: an analysis for a TTS system.

Segmental duration in French text-to-speech synthesis.

GIST-IT: Combining Linguistic and Machine Learning Techniques for Email Summarization

A natural language interface for relational databases has been utilized at AT&T Bell Laboratories as the natural language component of the DARPA ATIS common task. This is a part of a larger project that consists of incorporating a natural language component into the Bell Laboratories Speech Recognizer.The commercially available system used in this project was developed by Natural Language Incorporation (NLI), in particular by J. Ginsparg [Ginsparg, 1976]. We relate our experience in adapting the NLI interface to handle domain dependent ATIS queries. The results of this allowed the exploration of several important issues in speech and natural language:1. the feasabilitiy of using an off-the-shelf commercial product for a language understanding front end to a speech recognizer,2. the constraints of using a general-purpose product for a specific task.

The use of commercial natural language interface in the ATIS task

This paper presents the results of an experiment using machine-readable dictionaries (MRDs) and corpora for building concatenative units for text to speech (TTS) systems. Theoretical questions concerning the nature of phonemic data in dictionaries are raised; phonemic dictionary data is viewed as a representative corpus over which to extract n-gram phonemic frequencies in the language. Dictionary data are compared to corpus data, and phoneme inventories are evaluated for coverage. A methodology is defined to compute phonemic n-grams for incorporation into a TTS system.

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Evelyne Tzoukermann

Papers

High vowel /i y u/ in canadian and continental French: an analysis for a TTS system.

Segmental duration in French text-to-speech synthesis.

GIST-IT: Combining Linguistic and Machine Learning Techniques for Email Summarization

The use of commercial natural language interface in the ATIS task

Machine-readable dictionaries in text-to-speech systems