Sensor data presents the input of further simulations and plays a major role in early warning systems. Processing a high amount of data collected from different sensors requires interoperability at different levels. One of the remaining challenges is to overcome semantic heterogeneity during discovery, retrieval and integration of geodata within the open and distributed environments of current ...

It is recognized that many evaluation metrics of machine translation in use that focus on surface word level suffer from their lack of tolerance of linguistic variance, and the incorporation of linguistic features can improve their performance. To this end, WordNet is therefore widely utilized by recent evaluation metrics as a thesaurus for identifying synonym pairs. On this basis, word pairs i...

Cross language information retrieval (CLIR) presents huge ambiguous results as polysemy problems. Therefore, the semantic approach comes to solve the polysemy problem which that the same word may have different meanings according to the context of sentences. This paper presents semantic technique on queries for retrieving more relevant results in CLIR) that concentrates on the Arabic, Malay or ...

This study addresses the problem of improving the quality of natural language (NL) understanding by means of the involvement of the additional criterion of correct formal representation of the input inquiry. This criterion is based on the logical compatibility between the input inquiry, translated into the formal language, and the domain, encoded in the same language. Inquiry generality is meas...

We invent referential translation machines (RTMs), a computational model for identifying the translation acts between any two data sets with respect to a reference corpus selected in the same domain, which can be used for judging the semantic similarity between text. RTMs make quality and semantic similarity judgments possible by using retrieved relevant training data as interpretants for reach...

This paper describes the experimental combination of traditional Natural Language Processing (NLP) technology with the Semantic Web building stack in order to extend the expert knowledge required for a Machine Translation (MT) task. Therefore, we first give a short introduction in the state of the art of MT and the Semantic Web and discuss the problem of disambiguation being one of the common c...

We present a method for translating semantic relationships between languages where relationships are defined as pattern clusters. Given a pattern set which represents a semantic relationship, we use the web to extract sample term pairs of this relationship. We automatically translate the obtained term pairs using multilingual dictionaries and disambiguate the translated pairs using web counts. ...

Automatic translation systems may be characterized for the most part in one of two ways: (1) Translators in which the language processors are internally dependent on the description of the syntax of the language being processed. (2) Translators externally dependent on the syntax.* Programming a translation system of the first type has two major advantages. It can be set up quicker and it requir...

We propose Bilingually-constrained Recursive Auto-encoders (BRAE) to learn semantic phrase embeddings (compact vector representations for phrases), which can distinguish the phrases with different semantic meanings. The BRAE is trained in a way that minimizes the semantic distance of translation equivalents and maximizes the semantic distance of nontranslation pairs simultaneously. After traini...