Using ontology for measuring semantic similarity for question answering system

A major challenge among various applications of computational linguistics, natural language processing and information retrieval is to measure semantic similarity accurately. In this research paper, various ontology-based approaches i.e. compute semantic similarity between words have been studied and listed their benefits and shortcomings on the various identified parameters. Earlier, correlation with human judgment was the single criteria for the judgment of good similarity measures. In this paper, more parameters for semantic similarity measures have been identified and a relative analysis of similarity measures is performed on the identified parameters. These identified parameters can be further utilized for formulating the new semantic similarity-measures in the latest research area of text mining, web mining and information retrieval. We have identified various parameters like features-set, applicability on various ontologies such as single ontology, cross ontology or fuzzy ontology, Ontology type, dataset applied and relationship mapping for the various measures. Through detailed analysis we have found that feature based and hybrid approaches has higher accuracy as compare to edge and content based methods and works in different type of ontologies. Recent research drawing interest to find new feature set in this area like fuzzy distance, graph generation and text snippets etc. Max Accuracy was achieved in single ontology 0.87 and 0.83 over cross ontologies. WordNet and MeSH are maximally utilized Global Ontologies.

WITHDRAWN: Psycholinguistic evaluation of ASC based semantic similarity measures

Determining the extent to which two text snippets are semantically equivalent is a well-researched topic in the areas of natural language processing, information retrieval and text summarization. The sentence-to-sentence similarity scoring is extensively used in both generic and query-based summarization of documents as a significance or a similarity indicator. Nevertheless, most of these applications utilize the concept of semantic similarity measure only as a tool, without paying importance to the inherent properties of such tools that ultimately restrict the scope and technical soundness of the underlined applications. This paper aims to contribute to fill in this gap. It investigates three popular WordNet hierarchical semantic similarity measures, namely path-length, Wu and Palmer and Leacock and Chodorow, from both algebraical and intuitive properties, highlighting their inherent limitations and theoretical constraints. We have especially examined properties related to range and scope of the semantic similarity score, incremental monotonicity evolution, monotonicity with respect to hyponymy/hypernymy relationship as well as a set of interactive properties. Extension from word semantic similarity to sentence similarity has also been investigated using a pairwise canonical extension. Properties of the underlined sentence-to-sentence similarity are examined and scrutinized. Next, to overcome inherent limitations of WordNet semantic similarity in terms of accounting for various Part-of-Speech word categories, a WordNet “All word-To-Noun conversion” that makes use of Categorial Variation Database (CatVar) is put forward and evaluated using a publicly available dataset with a comparison with some state-of-the-art methods. The finding demonstrates the feasibility of the proposal and opens up new opportunities in information retrieval and natural language processing tasks.

Several approaches for computing semantic similarity and relatedness measures between terms have been developed. This paper proposes a new semantic similarity measure between two nodes concentrating on nouns as well as their hypernym/hyponym relationships based on the structure of Wordnet. In particular, the similarity of two given nouns depends not only on their positions but also on their relevancy connections in the hierarchy. We evaluate our measure and the other ones on dataset of Miller-Charles and then compute the correlation coefficients to the human judgments. The experimental results show that our method outperforms edge-counting methods.

There's now an increase in the number of Question Answering communities where large archives of question and answer pairs are collected up over time. These archives help traditional type-specified Question Answering (QA) systems to overcome type constraints and enable a service of general types. Semantic similarity measures between sentences dominate the overall performance of such Archive-based QA systems in finding similar questions in the archive to users' requests. Available approaches to sentence similarity measurement mainly utility word-to-word similarity measures directly in a bag-of-words way. In this paper, we take the syntactic evidence into account and carry out an examination on the impact of syntactic information on the sentence similarity measurement. We also compare the performance of our syntactic information incorporated approach with some baseline retrieval models. Experiments show that our approach outperforms other models both in mean average precision (MAP) and recall.

A key challenge for document clustering consists in finding a proper similarity measure for text documents that enables the generation of cohesive groups. Measures based on the classic bag-of-words model take into account solely the presence (and frequency) of words in documents. In doing so, semantically similar documents which use different vocabularies may end up in different clusters. For this reason, semantic similarity measures that use external knowledge, such as word n-gram corpora or thesauri, have been proposed in the literature. In this paper, the Frequency Google Tri-gram Measure is proposed to assess similarity between documents based on the frequencies of terms in the compared documents as well as the Google n-gram corpus as an additional semantic similarity source. Clustering algorithms are applied to several real datasets in order to experimentally evaluate the quality of the clusters obtained with the proposed measure and compare it with a number of state-of-the-art measures from the literature. The experimental results demonstrate that the proposed measure improves significantly the quality of document clustering, based on statistical tests. We further demonstrate that clustering results combining bag-of-words and semantic similarity are superior to those obtained with either approach independently.

The measurement of semantic similarity is a fundamental task in natural language processing. In the settings of a community question answering (cQA) system, it is essentially a classification problem: given a pair of questions, label it similar, relevant, or irrelevant. Traditional methods, either those at word level or at sentence level, typically require many lexical and syntactic resources, which are not available in languages other than English. In addition, there does not exist a finely annotated dataset for our purpose. In this paper, we constructed a dataset containing 4,322 labelled question pairs in Chinese, which is, to the best of our knowledge, the first open Chinese dataset for question similarity classification. We propose a novel framework for measuring the semantic similarity between sentences based on the architecture of a recurrent neural network (RNN) encoderdecoder, which does not require lexical or syntactic resources. We solve the problem of lacking labelled data by first training the RNN using a larger dataset of question pairs that are automatically labelled with heuristic scores, and then fine-tuning it with our smaller, manually labelled dataset. The two-step training scheme improves the accuracy of classification compared to single-step training, and also outperforms other traditional models. The proposed model is capable of both classification and candidate ranking.

Fuzzy control GA with a novel hybrid semantic similarity strategy for text clustering

BackgroundOntologies are widely used as metadata in biological and biomedical datasets. Measures of semantic similarity utilize ontologies to determine how similar two entities annotated with classes from ontologies are, and semantic similarity is increasingly applied in applications ranging from diagnosis of disease to investigation in gene networks and functions of gene products.ResultsHere, we analyze a large number of semantic similarity measures and the sensitivity of similarity values to the number of annotations of entities, difference in annotation size and to the depth or specificity of annotation classes. We find that most similarity measures are sensitive to the number of annotations of entities, difference in annotation size as well as to the depth of annotation classes; well-studied and richly annotated entities will usually show higher similarity than entities with only few annotations even in the absence of any biological relation.ConclusionsOur findings may have significant impact on the interpretation of results that rely on measures of semantic similarity, and we demonstrate how the sensitivity to annotation size can lead to a bias when using semantic similarity to predict protein-protein interactions.

The exploding number of computational models produced by Systems Biologists over the last years is an invitation to structure and exploit this new wealth of information. Researchers would like to trace models relevant to specific scientific questions, to explore their biological content, to align and combine them, and to match them with experimental data. To automate these processes, it is essential to consider semantic annotations, which describe their biological meaning. As a prerequisite for a wide range of computational methods, we propose general and flexible similarity measures for Systems Biology models computed from semantic annotations. By using these measures and a large extensible ontology, we implement a platform that can retrieve, cluster, and align Systems Biology models and experimental data sets. At present, its major application is the search for relevant models in the BioModels Database, starting from initial models, data sets, or lists of biological concepts. Beyond similarity searches, the representation of models by semantic feature vectors may pave the way for visualisation, exploration, and statistical analysis of large collections of models and corresponding data.

Measuring textual semantic similarity has been a subject of intense discussion in NLP and AI for many years. A new area of research has emerged that applies semantic similarity measures within Twitter. However, the development of these measures for the semantic analysis of tweets imposes fundamental challenges. The sparsity, ambiguity, and informality present in social media are hampering the performance of traditional textual similarity measures as have special syntactic and semantic characteristics. This paper reviews and evaluates the performance of topological, statistical, and hybrid similarity measures, in the context of Twitter analysis. Furthermore, the performance of each measure is compared against a naive keyword-based similarity computation method to assess the significance of semantic computation in capturing the meaning in tweets. An experiment is designed and conducted to evaluate the different measures through examining various metrics, including correlation, error rates, and statistical tests on a benchmark dataset. The potential weaknesses of semantic similarity measures in relation to Twitter applications of textual similarity assessment and the research contributions are discussed. This research highlights challenges and potential improvement areas for the semantic similarity of tweets, a resource for researchers and practitioners.

Human language is naturally fuzzy by nature, with words meaning different things to different people, depending on the context. Fuzzy words are words with a subjective meaning, which are typically used in everyday human natural language dialogue and are often ambiguous and vague in meaning and are based on an individual’s perception. Fuzzy Sentence Similarity Measures (FSSM) are algorithms that can compare two or more short texts which contain human-perception-based words and return a numeric measure of similarity of meaning between them. This paper proposes a new FSSM called FUSE (FUzzy Similarity mEasure), to assess an individual’s perception within a FSSM. FUSE is an ontology-based similarity measure that uses Interval Type-2 fuzzy sets to model relationships between categories of human perception-based words. The FUSE algorithm has been developed over four versions and evaluated on several published and newly created datasets. Typically, results have shown that calculating the semantic similarity of two short texts using FUSE, gives a higher correlation with the average human ratings (AHR) compared to traditional sentence similarity measures that do not consider the presence of fuzzy words. This paper focuses on the second version of the FUSE algorithm, referred to as FUSE_2.0 which has been compared to several state-of-the-art, semantic similarity measures (SSM), including the only published FSSM, FAST (Fuzzy Algorithm for Similarity Testing), which has a limited dictionary of fuzzy words and uses Type-1 to model relationships between categories of human perception-based words. Results have shown that FUSE_2.0 achieves a higher correlation with the average human ratings (AHR) compared to traditional SSM’s and FAST. The key contributions of this work can be summarised as follows: The development of a new methodology to model fuzzy words using Interval Type-2 fuzzy sets. This has led to the creation of a fuzzy dictionary for nine fuzzy categories, a useful resource which can be used by other researchers in the field of natural language processing and Computing with Words (CWW) with other fuzzy applications such as semantic clustering.

Semantic similarity is a metric used to know the similarity degree of two concepts in an ontology or a taxonomy. Semantic similarity has a wide variety applications on artificial intelligence, natural language processing, biomedical informatics, geoinformatics and semantic web and is usually applied on machine translation and word-sense disambiguation. In this research, semantic similarity measures are used to evaluate taxonomic relationships in a domain ontology. This evaluation was carried out by using a proposed algorithm and through the accuracy measure. The semantic similarity measures implemented are based on information content and were proposed by the following authors: Resnik, Lin, Jiang & Conrath and Mazandu & Mulder. Mainly, this research contributes to the automatic evaluation of ontologies in the task of evaluating the ontology taxonomy. The experimental results show that the measures have at least 88% accuracy. In addition, the system has an accuracy of 94% compared to validation responses from an expert.

This paper explains about similarity measure and the relationship between the knowledge repositories. This paper also describes the significance of document similarity measures, algorithms and to which type of text it can be applied Document similarity measures are of full text similarity, paragraph similarity, sentence similarity, semantic similarity, structural similarity and statistical measures. Two different frameworks had been proposed in this paper, one for measuring document to document similarity and the other model which measures similarity between documents to multiple documents. These two proposed models can use any one of the similarity measures in implementation aspect, which is been put forth for further research.

In the field of biology, researchers need to compare genes or gene products using semantic similarity measures (SSM). Continuous data growth and diversity in data characteristics comprise what is called big data; current biological SSMs cannot handle big data. Therefore, these measures need the ability to control the size of big data. We used parallel and distributed processing by splitting data into multiple partitions and applied SSM measures to each partition; this approach helped manage big data scalability and computational problems. Our solution involves three steps: split gene ontology (GO), data clustering, and semantic similarity calculation. To test this method, split GO and data clustering algorithms were defined and assessed for performance in the first two steps. Three of the best SSMs in biology [Resnik, Shortest Semantic Differentiation Distance (SSDD), and SORA] are enhanced by introducing threaded parallel processing, which is used in the third step. Our results demonstrate that introducing threads in SSMs reduced the time of calculating semantic similarity between gene pairs and improved performance of the three SSMs. Average time was reduced by 24.51% for Resnik, 22.93%, for SSDD, and 33.68% for SORA. Total time was reduced by 8.88% for Resnik, 23.14% for SSDD, and 39.27% for SORA. Using these threaded measures in the distributed system, combined with using split GO and data clustering algorithms to split input data based on their similarity, reduced the average time more than did the approach of equally dividing input data. Time reduction increased with increasing number of splits. Time reduction percentage was 24.1%, 39.2%, and 66.6% for Threaded SSDD; 33.0%, 78.2%, and 93.1% for Threaded SORA in the case of 2, 3, and 4 slaves, respectively; and 92.04% for Threaded Resnik in the case of four slaves.