Terms Of Semantic Similarity Research Articles

GSLAlign: community detection and local PPI network alignment

High throughput protein–protein interaction (PPI) profiling and computational techniques have resulted in generating a large amount of PPI network data. The study of PPI networks helps in understanding the biological processes of the proteins. The comparative study of the PPI networks helps in identifying the conserved interactions across the species. This article presents a novel local PPI network aligner ‘GSLAlign’ that consists of two stages. It first detects the communities from the PPI networks by applying the GraphSAGE algorithm using gene expression data. In the second stage, the detected communities are aligned using a community aligner that is based on protein sequence similarity. The community detection algorithm produces more separable and biologically accurate communities as compared to previous community detection algorithms. Moreover, the proposed community alignment algorithm achieves 3–8% better results in terms of semantic similarity as compared to previous local aligners. The average connectivity and coverage of the proposed algorithm are also better than the existing aligners. Communicated by Ramaswamy H. Sarma

Fully Transformer-Equipped Architecture for end-to-end Referring Video Object Segmentation

Referring Video Object Segmentation (RVOS) requires segmenting the object in video referred by a natural language query. Existing methods mainly rely on sophisticated pipelines to tackle such cross-modal task, and do not explicitly model the object-level spatial context which plays an important role in locating the referred object. Therefore, we propose an end-to-end RVOS framework completely built upon transformers, termed Fully Transformer-Equipped Architecture (FTEA), which treats the RVOS task as a mask sequence learning problem and regards all the objects in video as candidate objects. Given a video clip with a text query, the visual–textual features are yielded by encoder, while the corresponding pixel-level and word-level features are aligned in terms of semantic similarity. To capture the object-level spatial context, we have developed the Stacked Transformer, which individually characterizes the visual appearance of each candidate object, whose feature map is decoded to the binary mask sequence in order directly. Finally, the model finds the best matching between mask sequence and text query. In addition, to diversify the generated masks for candidate objects, we impose a diversity loss on the model for capturing more accurate mask of the referred object. Empirical studies have shown the superiority of the proposed method on three benchmarks, e.g., FETA achieves 45.1% and 38.7% in terms of mAP on A2D Sentences (3782 videos) and J-HMDB Sentences (928 videos), respectively; it achieves 56.6% in terms of J&F on Ref-YouTube-VOS (3975 videos and 7451 objects). Particularly, compared to the best candidate method, it has a gain of 2.1% and 3.2% in terms of P@0.5 on the former two, respectively, while it has a gain of 2.9% in terms of J on the latter one.

IBiR : Bug-report-driven Fault Injection

Much research on software engineering relies on experimental studies based on fault injection. Fault injection, however, is not often relevant to emulate real-world software faults since it “blindly” injects large numbers of faults. It remains indeed challenging to inject few but realistic faults that target a particular functionality in a program. In this work, we introduce iBiR , a fault injection tool that addresses this challenge by exploring change patterns associated to user-reported faults. To inject realistic faults, we create mutants by re-targeting a bug-report-driven automated program repair system, i.e., reversing its code transformation templates. iBiR is further appealing in practice since it requires deep knowledge of neither code nor tests, just of the program’s relevant bug reports. Thus, our approach focuses the fault injection on the feature targeted by the bug report. We assess iBiR by considering the Defects4J dataset. Experimental results show that our approach outperforms the fault injection performed by traditional mutation testing in terms of semantic similarity with the original bug, when applied at either system or class levels of granularity, and provides better, statistically significant estimations of test effectiveness (fault detection). Additionally, when injecting 100 faults, iBiR injects faults that couple with the real ones in around 36% of the cases, while mutation testing achieves less than 4%.

Ontology-Based Probabilistic Estimation for Assessing Semantic Similarity of Land Use/Land Cover Classification Systems

To accurately and formally represent the historical trajectory and present the current situation of land use/land cover (LULC), numerous types of classification standards for LULC have been developed by different nations, institutes, organizations, etc.; however, these land cover classification systems and legends generate polysemy and ambiguity in integration and sharing. The approaches for dealing with semantic heterogeneity have been developed in terms of semantic similarity. Generally speaking, these approaches lack domain ontologies, which might be a significant barrier to implementing these approaches in terms of semantic similarity assessment. In this paper, we propose an ontological approach to assess the similarity of the domain of LULC classification systems and standards. We develop domain ontologies to explicitly define the descriptions and codes of different LULC classification systems and standards as semantic information, and formally organize this semantic information as rules for logical reasoning. Then, we utilize a Bayes algorithm to create a conditional probabilistic model for computing the semantic similarity of terms in two separate LULC land cover classification systems. The experiment shows that semantic similarity can be effectively measured by integrating a probabilistic model based on the content of ontology.

Recomendación por similitud semántica en repositorios con grandes volúmenes de datos de medición

Los proyectos de medición y evaluación son definidos utilizando un marco formal de medición y evaluación, el cual permita considerar la entidad bajo monitoreo, la cual forma parte de nuestro análisis. Puede suceder, que dicha entidad, no cuente con experiencia o conocimiento previos, provocando esto que no se pueda recomendar sugerencias en situaciones determinadas. En este sentido, y de manera de poder solucionar dicha situación, es que se plantea la idea de recomendar de acuerdo con la similitud semántica que presenten las entidades bajo monitoreo, considerando las mediciones surgidas del proyecto de medición y evaluación definidos previamente. El objetivo es justamente poder detectar entidades bajo monitoreo similares estableciendo un puntaje de similitud que permita brindar experiencias y-o recomendaciones por analogía con otra entidad en caso de ausencia de estas en la entidad objeto de análisis. Aquí se presenta un avance parcial de esta línea de investigación, indicando los resultados obtenidos en lo que respecta a similitud semántica, coeficientes de similitud, estrategias de recomendación y actualización de diferentes componentes de la arquitectura de procesamiento de flujo de datos.

Word Vector Embeddings and Domain Specific Semantic based Semi-Supervised Ontology Instance Population

An ontology defines a set of representational primitives which model a domain of knowledge or discourse. With the arising fields such as information extraction and knowledge management, the role of ontology has become a driving factor of many modern day systems. Ontology population, on the other hand, is an inherently problematic process, as it needs manual intervention to prevent the conceptual drift. The semantic sensitive word embedding has become a popular topic in natural language processing with its capability to cope with the semantic challenges. Incorporating domain specific semantic similarity with the word embeddings could potentially improve the performance in terms of semantic similarity in specific domains. Thus, in this study, we propose a novel way of semi-supervised ontology population through word embeddings and domain specific semantic similarity as the basis. We built several models including traditional benchmark models and new types of models which are based on word embeddings. Finally, we ensemble them together to come up with a synergistic model which outperformed the candidate models by 33% in comparison to the best performed candidate model.

Are there qualitative differences in the representation of abstract and concrete words? Within-language and cross-language evidence from the semantic priming paradigm.

The different organizational frameworks theory proposes that there is a qualitative difference between the representation of concrete and abstract words in memory: Concrete concepts would be primarily organized in terms of semantic similarity whereas abstract concepts would be mainly organized by their association with other concepts. Evidence in support of this proposal has been mostly obtained with neuropsychological populations and, to a lesser extent, with healthy participants. In the present work, we tested the different organizational frameworks theory by using, for the first time, a semantic priming paradigm both within language and across languages. The results revealed that there was priming for both semantically similar and associative relations when words were concrete. However, with abstract words, priming was only observed when pairs and targets were associated. These results do not support the proposal of Crutch and coworkers, suggesting that the experimental paradigm as well as the type of relations tested may modulate the pattern of effects obtained with concrete and abstract words.

Varieties of intensification

This paper is a commentary on Beltrama and Bochnak (2015), who propose a uniform semantics for different uses of cross-categorial intensifiers -issimo in Italian and šému in Washo as universal quantifiers over contexts. Two concerns of their uniform semantics are raised, (i) unexpected restrictions on intensification, and (ii) non-truth-conditional intensification. It is argued that a uniform semantics like Beltrama and Bochnak’s is unachievable, and suggested that the multitude of uses commonly observed with intensifiers should be captured in terms of semantic similarity, rather than semantic identity.

Organized Modularity in the Interactome: Evidence from the Analysis of Dynamic Organization in the Cell Cycle.

The organization of global protein interaction networks (PINs) has been extensively studied and heatedly debated. We revisited this issue in the context of the analysis of dynamic organization of a PIN in the yeast cell cycle. Statistically significant bimodality was observed when analyzing the distribution of the differences in expression peak between periodically expressed partners. A close look at their behavior revealed that date and party hubs derived from this analysis have some distinct features. There are no significant differences between them in terms of protein essentiality, expression correlation and semantic similarity derived from gene ontology (GO) biological process hierarchy. However, date hubs exhibit significantly greater values than party hubs in terms of semantic similarity derived from both GO molecular function and cellular component hierarchies. Relating to three-dimensional structures, we found that both single- and multi-interface proteins could become date hubs coordinating multiple functions performed at different times while party hubs are mainly multi-interface proteins. Furthermore, we constructed and analyzed a PPI network specific to the human cell cycle and highlighted that the dynamic organization in human interactome is far more complex than the dichotomy of hubs observed in the yeast cell cycle.

Vector Space Models of Word Meaning and Phrase Meaning: A Survey

AbstractDistributional models represent a word through the contexts in which it has been observed. They can be used to predict similarity in meaning, based on the distributional hypothesis, which states that two words that occur in similar contexts tend to have similar meanings. Distributional approaches are often implemented in vector space models. They represent a word as a point in high‐dimensional space, where each dimension stands for a context item, and a word's coordinates represent its context counts. Occurrence in similar contexts then means proximity in space. In this survey we look at the use of vector space models to describe the meaning of words and phrases: the phenomena that vector space models address, and the techniques that they use to do so. Many word meaning phenomena can be described in terms of semantic similarity: synonymy, priming, categorization, and the typicality of a predicate's arguments. But vector space models can do more than just predict semantic similarity. They are a very flexible tool, because they can make use of all of linear algebra, with all its data structures and operations. The dimensions of a vector space can stand for many things: context words, or non‐linguistic context like images, or properties of a concept. And vector space models can use matrices or higher‐order arrays instead of vectors for representing more complex relationships. Polysemy is a tough problem for distributional approaches, as a representation that is learned from all of a word's contexts will conflate the different senses of the word. It can be addressed, using either clustering or vector combination techniques. Finally, we look at vector space models for phrases, which are usually constructed by combining word vectors. Vector space models for phrases can predict phrase similarity, and some argue that they can form the basis for a general‐purpose representation framework for natural language semantics.