Upper ontology

About: Upper ontology is a research topic. Over the lifetime, 9767 publications have been published within this topic receiving 220721 citations. The topic is also known as: top-level ontology & foundation ontology.

Sinica BOW (Bilingual Ontological Wordnet): Integration of Bilingual WordNet and SUMO

Abstract: The Academia Sinica Bilingual Ontological Wordnet (Sinica BOW) integrates three resources: WordNet, English-Chinese Translation Equivalents Database (ECTED), and SUMO (Suggested Upper Merged Ontology) The three resources were originally linked in two pairs: WordNet 16 was manually mapped to SUMO (Niles & Pease 2003) and also to ECTED (the English lemmas in WordNet were mapped to their Chinese lexical equivalents) ECTED encodes both equivalent pairs and their semantic relations (Huang et al 2003) With the integration of these three key resources, Sinica BOW functions both as an English-Chinese bilingual wordnet and a bilingual lexical access to SUMO Sinica BOW allows versatile access and facilitates a combination of lexical, semantic, and ontological information Versatility is built in with its bilinguality, and the lemma-based merging of multiple resources First, either English or Chinese can be used for the query, as well as for presenting the content of the resources Second, the user can easily access the logical structure of both the WordNet and SUMO ontology using either words or conceptual nodes Third, multiple linguistic indexing is built in to allow additional versatility Fourth, domain information allows another dimension of knowledge manipulation 1 Background and Motivation Conceptual structure and lexical access are two essential elements of human knowledge Bilingual representation of both conceptual structure and lexical information will enable language independent knowledge processing In this paper, we introduce a new type of integrated language resources: Bilingual Ontological Wordnet The Academia Sinica Bilingual Ontological Wordnet (Sinica BOW) was constructed in 2003 We argue that such combination of ontology and wordnet will 1) give each linguistic form a rigorous conceptual location, 2) clarify the relation between the conceptual classification and its linguistic instantiation, and 3) facilitate genuine cross-lingual access of knowledge 2 Resources and Structure The Academia Sinica Bilingual Ontological Wordnet (Sinica BOW) integrates three resources: WordNet, English-Chinese Translation Equivalents Database (ECTED), and SUMO (Suggested Upper Merged Ontology) WordNet is a lexical knowledgebase for English language that was created at Cognitive Science Laboratory of Princeton University in 1990 (Fellbaum 1998) Its content is divided into four categories based on psycholinguistic principles: nouns, verbs, adjectives and adverbs WordNet organizes the lexical information according to word meaning and each synset groups together a set of lemmas sharing the same sense In addition, WordNet is a semantic network linking synsets withvlexical semantic relations WordNet is widely used in Natural Language Processing applications and linguistic research The most updated version of WordNet is WordNet 20 We adopted WordNet 16, the version which is used by most applications so far ECTED was constructed at Academia Sinica as a crucial step towards bootstrapping a Chinese wordnet with English WordNet (Huang et al 2002, Huang et al 2003) The translation equivalence database was hand-crafted by the WordNet team at CKIP, Academia Sinica First, all possible Chinese translations of an English synset word (from WN 16) are extracted from several available online bilingual (EC or CE) resources These translation candidates were then checked by a team of translators with near-native bilingual ability For each of the 99,642 English synsets, the translator selected the three most appropriate translation equivalents whenever possible The translation equivalences were defaulted to lexicalized words, rather than descriptive phrases, whenever possible The translation equivalences were then manually verified Note that after the first round of translation, there were about 5% of the lemmas whose Chinese translation can neither be found in our bilingual resources nor be filled by the translators We spent another 2 person-year consulting various special dictionaries to fill in the gaps SUMO is a upper ontology constructed by the IEEE Standard Upper Ontology Working Group and maintained at Teknowledge Corporation SUMO contains roughly 1,000 conceptual nodes for knowledge representation It can be applied to automated reasoning, information retrieval and inter-operability in E-commerce, education and NLP tasks Niles & Pease (2003) mapped synsets of WordNet and concept of SUMO in three relations: synonymy, hypernymy and instantiation For instance, the synset "animal" (a living organism characterized by voluntary movement) in WordNet is synonymous with the SUMO concept of "Animal" In "bank" (a financial institution that accepts deposits and channels the money into lending activities) this case, bank is a corporation that is a hypernym of the associated synset President of the United States (the office of the US head of state) is an instantiation of "position" concept Through the linking and the interface available at the SUMO website (http://ontologyteknowledgecom), each English lemma can be mapped to a SUMO ontology node Figure 1: The resource and structure of Sinica BOW The three above resources were originally linked in two pairs: WordNet 16 was mapped to SUMO by Niles and Pease ECTED maps English synsets in WordNet to Chinese lexical equivalents, which encodes both equivalent pairs and their semantic relations (Huang et al 2003) WordNet synsets thus became the natural mediation for our integration work Thus, with the integration of these three key resources, Sinica BOW can function both as an English-Chinese bilingual wordnet and a bilingual lexical access to SUMO In other words, Sinica BOW allows a 2x2x2 query design, where a query could be in either Chinese or English, either in lexical lemmas of SUMO terms, and the query target can either be the wordnet content or the SUMO ontology The design of Sinica BOW has an additional domain information layer, as shown in Figure 1 The domain information will be represented by a set of Domain Lexico-Taxonomy (DLT, Huang, Li, & Hong 2004) In this design, our main concern is domain inter-operability It can be safely assumed that domain exclusive words (ie lemma-sense pairs) are recorded only in domain lexicon, hence there will be no ambiguity and no inter-operability issues We concentrate instead on the lexical items that intersect with the general lexicon On one hand, since these are the lemmas that may occur in more than one domain with one or more different meanings, domain specification would help resolving the ambiguity On the other hand, these general lemmas with domain applicability can be effective signatures for the applicable domains The real challenge to domain inter-operability involves the 'unknown' domains where no comprehensive domain lexica/corpora are available We argue that this problem can be greatly ameliorated by tagging the general lexicon with possible domain tags When domain tags are assigned to lemmas whenever possible, the general lexicon will contain substantial partial domain lexica Although we cannot expect to construct full-scale domain lexica within the general lexicon, these domain-tagged lexical items 3 Presentational Versatility Sinica BOW allows versatile access and facilitates a combination of lexical semantic and ontological information The versatility is built in with bilinguality, and lemma-based merging of multiple language sources The versatility and combinatory presentation is crucial to the presentation of a knowledge system 31 Lexicon-driven Access Since the main goal of Sinica BOW concerns knowledge representation, the lemma based or conceptual node based query results are directed linked to the full knowledgebase and expandable The Sinica BOW access is lexicon-driven Each query returns a structured lexical entry, presented as a tree-structured menu A keyword query returns with a menu arranged according to word senses, as shown in Figure 2 The top level information returned including POS, usage ranking, and cross-reference links In addition to wordnet information, cross-references to up to five resources are pre-compiled for either language For an English word, the main resource is of course the bilingual wordnet information that our team constructed Major outside references are listed for quick hyperlink These include corpora and both EC and CE dictionaries For Chinese, the main resource is again our bilingual wordnet In addition, links are established to Sinica Corpus, to Wen-Land (a learner's Lexical KnowledgeNet), and to online monolingual and bilingual dictionaries In addition to online access of multiple sources information, each lemma's distribution in these resources is also a good indicator of its usage level The access to the ontology and the domain taxonomy are also lexicon-driven That is, in addition to using the pre-defined ontology or domain terms (in either English or Chinese), a query based on a lexical term is also possible For SUMO, it will return a node where the word appears in It can also be achieved by looking up the ontological or domain node the word belongs to One last but critical feature of the lexicon-driven access is the possibility to re-start a query with any lexical node When expansion reaches at the leave node and results in a new word, clicking on the word is equivalent to start a new keyword search WorNet Offset SUMO Domain Domain Lexicons ECTED WordNet Sinica BOW

Materials Ontology: An Infrastructure for Exchanging Materials Information and Knowledge

Abstract: We have rich information resources for materials science and engineering - raw measurement data, computational simulation methods, digitized handbooks, and digital libraries. However, these resources have a wide variety of formats, terminologies, and concepts, which makes it difficult to find appropriate information for materials design, development, and evaluation. One solution to this problem is to integrate these resources into a computer readable concept map, called a domain ontology, which describes concepts and relationships among the concepts in materials science and engineering. This paper describes a trial that constructs a standard of metadata description using ontology language and demonstrates the validity of this construction through data exchange among heterogeneous material databases. "Materials Ontology," which consists of several sub ontologies corresponding to substance, process, environment, and property, is developed using the ontology language of the Semantic Web, OWL, which enables the definition of a flexible and detailed structure of materials information. A versatile "materials data format" is built on the Materials Ontology as a component of the materials information platform and is applied to exchange data among three different thermal property databases, maintained by two major materials science research institutes in Japan.

Using ontology to support development of software architectures

Abstract: In this paper we propose an approach to software development that focuses on architecture decisions and involves the use of ontology. In this approach the architecture is captured by an instance of an ontology. The ontology has four major components: architecture assets, architecture decisions, stakeholder concerns, and an architecture roadmap. We illustrate our approach through a case study involving a real-time credit-approval system and the use of Protege, an open-source ontology development tool.

Ontologies in the context of product lifecycle management: state of the art literature review

Abstract: The use of ontologies in the context of product lifecycle management (PLM) is gaining importance and popularity, while at the same time it generates a lot of controversy in discussions within scientific and engineering communities. Yet, what is ontology? What challenges have been addressed so far? What role does ontology play? Do we really need ontology? These are the core questions this paper seeks to address. We propose to conduct a comprehensive study of the concept of Ontology firstly in its domain of origin, Philosophy, and secondly in information science. Based on the understanding of this concept and an in-depth analysis of the state of the art, seven key roles of ontology are defined. These roles serve as a framework describing the general state of research on the use of ontologies in the context of PLM.