This paper addresses the task of extending a given synset with additional synonyms taking into account synonymy strength as a fuzzy value.Given a mono/multilingual synset and a threshold (a fuzzy value [0 -1]), our goal is to extract new synonyms above this threshold from existing lexicons.We present twofold contributions: an algorithm and a benchmark dataset.The dataset consists of 3K candidate synonyms for 500 synsets.Each candidate synonym is annotated with a fuzzy value by four linguists.The dataset is important for (i) understanding how much linguists (dis/)agree on synonymy, in addition to (ii) using the dataset as a baseline to evaluate our algorithm.Our proposed algorithm extracts synonyms from existing lexicons and computes a fuzzy value for each candidate.Our evaluations show that the algorithm behaves like a linguist and its fuzzy values are close to those proposed by linguists (using RMSE and MAE).The dataset and a demo page are publicly available at https: //portal.sina.birzeit.edu/synonyms.

Arabic language lacks semantic datasets and sense inventories.The most common semantically-labeled dataset for Arabic is the ArabGlossBERT, a relatively small dataset that consists of 167K context-gloss pairs (about 60K positive and 107K negative pairs), collected from Arabic dictionaries.This paper presents an enrichment to the ArabGlossBERT dataset, by augmenting it using (Arabic-English-Arabic) machine back-translation.Augmentation increased the dataset size to 352K pairs (149K positive and 203K negative pairs).We measure the impact of augmentation using different data configurations to fine-tune BERT on target sense verification (TSV) task.Overall, the accuracy ranges between 78% to 84% for different data configurations.Although our approach performed at par with the baseline, we did observe some improvements for some POS tags in some experiments.Furthermore, our fine-tuned models are trained on a larger dataset covering larger vocabulary and contexts.We provide an in-depth analysis of the accuracy for each part-of-speech (POS).

We present our progress in developing a novel algorithm to extract synonyms from bilingual dictionaries.Identification and usage of synonyms play a significant role in improving the performance of information access applications.The idea is to construct a translation graph from translation pairs, then to extract and consolidate cyclic paths to form bilingual sets of synonyms.The initial evaluation of this algorithm illustrates promising results in extracting Arabic-English bilingual synonyms.In the evaluation, we first converted the synsets in the Arabic WordNet into translation pairs (i.e., losing word-sense memberships).Next, we applied our algorithm to rebuild these synsets.We compared the original and extracted synsets obtaining an F-Measure of 82.3% and 82.1% for Arabic and English synsets extraction, respectively.

Querying large data graphs has brought the attention of the research community. Many solutions were proposed, such as Oracle Semantic Technologies, Virtuoso, RDF3X, and C-Store, among others. Although such approaches have shown good performance in queries with medium complexity, they perform poorly when the complexity of the queries increases. In this paper, the authors propose the Graph Signature Index, a novel and scalable approach to index and query large data graphs. The idea is that they summarize a graph and instead of executing the query on the original graph, they execute it on the summaries. The authors' experiments with Yago (16M triples) have shown that e.g., a query with 4 levels costs 62 sec using Oracle but it only costs about 0.6 sec with their index. Their index can be implemented on top of any Graph database, but they chose to implement it as an extension to Oracle on top of the SEM_MATCH table function. The paper also introduces disk-based versions of the Trace Equivalence and Bisimilarity algorithms to summarize data graphs, and discusses their complexity and usability for RDF graphs.

In this paper, we introduce a methodology for mapping linguistic ontologies lexicalized across different languages.We present a classification-based semantics for mappings of lexicalized concepts across different languages.We propose an experiment for validating the proposed cross-language mapping semantics, and discuss its role in creating a gold standard that can be used in assessing cross-language matching systems.

We present a query formulation language (called MashQL) in order to easily query and fuse structured data on the web. The main novelty of MashQL is that it allows people with limited IT skills to explore and query one (or multiple) data sources without prior knowledge about the schema, structure, vocabulary, or any technical details of these sources. More importantly, to be robust and cover most cases in practice, we do not assume that a data source should have - an offline or inline - schema. This poses several language-design and performance complexities that we fundamentally tackle. To illustrate the query formulation power of MashQL, and without loss of generality, we chose the Data web scenario. We also chose querying RDF, as it is the most primitive data model; hence, MashQL can be similarly used for querying relational databases and XML. We present two implementations of MashQL, an online mashup editor, and a Firefox add on. The former illustrates how MashQL can be used to query and mash up the Data web as simple as filtering and piping web feeds; and the Firefox add on illustrates using the browser as a web composer rather than only a navigator. To end, we evaluate MashQL on querying two data sets, DBLP and DBPedia, and show that our indexing techniques allow instant user interaction.

In this paper, we first introduce the notion of gloss for ontology engineering purposes. We propose that each vocabulary in an ontology should have a gloss. A gloss basically is an informal description of the meaning of a vocabulary that is supposed to render factual and critical knowledge to understanding a concept, but that is unreasonable or very difficult to formalize and/or articulate formally. We present a set of guidelines on what should and should not be provided in a gloss. Second, we propose to incorporate linguistic resources in the ontology engineering process. We clarify the importance of using lexical resources as a "consensus reference" in ontology engineering, and so enabling the adoption of the glosses found in these resources. A linguistic resource (i.e. its list of terms and their definitions) shall be seen as a shared vocabulary space for ontologies. We present an ontology engineering software tool (called DogmaModeler), and illustrate its support of reusing of WordNet's terms and glosses in ontology modeling.