Semantic Relatedness Measures Research Articles

Rel Topic : A graph-based semantic relatedness measure in topic ontologies and its applicability for topic labeling of old press articles

Graph-based semantic measures have been used to solve problems in several domains. They tend to compare semantic entities in order to estimate their similarity or relatedness. While semantic similarity is applicable to hierarchies or taxonomies, semantic relatedness is adapted to ontologies. In this work, we propose a novel semantic relatedness measure, named Rel Topic , within topic ontologies for topic labeling purposes. In contrast to traditional measures, which are dependent on textual resources, Rel Topic considers semantic properties of entities in ontologies. Thus, correlations of nodes and weights of nodes and edges are assessed. The pertinence of Rel Topic is evaluated for topic labeling of old press articles. For this purpose, a topic ontology representing the articles, named Topic-OPA, is derived from open knowledge graphs by applying a SPARQL-based automatic approach. A use-case is presented in the context of the old French newspaper Le Matin. The generated topics are evaluated using a dual evaluation approach with the help of human annotators. Our approach shows an agreement quite close to that shown by humans. The entire approach’s reuse is demonstrated for labeling a different context of articles, recent (modern) newspapers.

Exploiting Wikipedia to Measure the Semantic Relatedness between Arabic Terms

Measuring the semantic relatedness between words or terms plays an important role in many domains such as linguistics and artificial intelligence. Although this topic has been widely explored in the literature, most efforts focused on the English text, while little has been done to measure the similarity between Arabic terms. A growing number of semantic relatedness measures have relied on an underlying background knowledge such as Wikipedia. They often map terms to Wikipedia concepts, and then use the content or hyperlink structure of the corresponding Wikipedia articles to estimate the similarity between terms. However, existing approaches mostly focused on the English version of Wikipedia, while limited work has been done on the Arabic version. This work proposes an approach that takes advantage of Wikipedia features to measure the relationship between Arabic terms. It exploits two types of relations to gain rich features for the similarity measure, which are: the context-based relation and the category-based relation. The context-based relation is measured based on the intersection between incoming links of Wikipedia articles, while the category-based relation is measured by utilizing the taxonomy of Wikipedia categories. The proposed approach was evaluated based on a translated version of the WordSimilarity-353 benchmark dataset. The results show that our approach generally outperforms several approaches in the literature that use the same dataset in English. However, the poor structure and content of the Arabic version of Wikipedia compared to the English version has resulted in several incorrect similarity scores.

A vector-based semantic relatedness measure using multiple relations within SNOMED CT and UMLS

ObjectiveTo propose a new vector-based relatedness metric that derives word vectors from the intrinsic structure of biomedical ontologies, without consulting external resources such as large-scale biomedical corpora. Materials and MethodsSNOMED CT on the mapping layer of UMLS was used as a testbed ontology. Vectors were created for every concept at the end of all semantic relations—attribute–value relations and descendants as well as is_a relation—of the defining concept. The cosine similarity between the averages of those vectors with respect to each defining concept was computed to produce a final semantic relatedness. ResultsTwo benchmark sets that include a total of 62 biomedical term pairs were used for evaluation. Spearman’s rank coefficient of the current method was 0.655, 0.744, and 0.742 with the relatedness rated by physicians, coders, and medical experts, respectively. The proposed method was comparable to a word-embedding method and outperformed path-based, information content-based, and another multiple relation-based relatedness metrics. DiscussionThe current study demonstrated that the addition of attribute relations to the is_a hierarchy of SNOMED CT better conforms to the human sense of relatedness than models based on taxonomic relations. The current approach also showed that it is robust to the design inconsistency of ontologies. ConclusionUnlike the previous vector-based approach, the current study exploited the intrinsic semantic structure of an ontology, precluding the need for external textual resources to obtain context information of defining terms. Future research is recommended to prove the validity of the current method with other biomedical ontologies.

How much is a cow like a meow? A novel database of human judgements of audiovisual semantic relatedness.

Semantic information about objects, events, and scenes influences how humans perceive, interact with, and navigate the world. The semantic information about any object or event can be highly complex and frequently draws on multiple sensory modalities, which makes it difficult to quantify. Past studies have primarily relied on either a simplified binary classification of semantic relatedness based on category or on algorithmic values based on text corpora rather than human perceptual experience and judgement. With the aim to further accelerate research into multisensory semantics, we created a constrained audiovisual stimulus set and derived similarity ratings between items within three categories (animals, instruments, household items). A set of 140 participants provided similarity judgments between sounds and images. Participants either heard a sound (e.g., a meow) and judged which of two pictures of objects (e.g., a picture of a dog and a duck) it was more similar to, or saw a picture (e.g., a picture of a duck) and selected which of two sounds it was more similar to (e.g., a bark or a meow). Judgements were then used to calculate similarity values of any given cross-modal pair. An additional 140 participants provided word judgement to calculate similarity of word-word pairs.The derived and reported similarity judgements reflect a range of semantic similarities across three categories and items, and highlight similarities and differences among similarity judgments between modalities. We make the derived similarity values available in a database format to the research community to be used as a measure of semantic relatedness in cognitive psychology experiments, enabling more robust studies of semantics in audiovisual environments.

An Efficient Method for Biomedical Word Sense Disambiguation Based On Web-Kernel Similarity

Searching for the best sense for a polysemous word remains one of the greatest challenges in the representation of biomedical text. To this end, Word Sense Disambiguation (WSD) algorithms mostly rely on an External Source of Knowledge, like a Thesaurus or Ontology, for automatically selecting the proper concept of an ambiguous term in a given Window of Context using semantic similarity and relatedness measures. In this paper, we propose a Web-based Kernel function for measuring the semantic relatedness between concepts to disambiguate an expression versus multiple possible concepts. This measure uses the large volume of documents returned by PubMed Search engine to determine the greater context for a biomedical short text through a new term weighting scheme based on Rough Set Theory (RST). To illustrate the efficiency of our proposed method, we evaluate a WSD algorithm based on this measure on a biomedical dataset (MSH-WSD) that contains 203 ambiguous terms and acronyms. The obtained results demonstrate promising improvements.

An Efficient Method for Biomedical Word Sense Disambiguation Based on Web-Kernel Similarity

Searching for the best sense for a polysemous word remains one of the greatest challenges in the representation of biomedical text. To this end, Word Sense Disambiguation (WSD) algorithms mostly rely on an External Source of Knowledge, like a Thesaurus or Ontology, for automatically selecting the proper concept of an ambiguous term in a given Window of Context using semantic similarity and relatedness measures. In this paper, we propose a Web-based Kernel function for measuring the semantic relatedness between concepts to disambiguate an expression versus multiple possible concepts. This measure uses the large volume of documents returned by PubMed Search engine to determine the greater context for a biomedical short text through a new term weighting scheme based on Rough Set Theory (RST). To illustrate the efficiency of our proposed method, we evaluate a WSD algorithm based on this measure on a biomedical dataset (MSH-WSD) that contains 203 ambiguous terms and acronyms. The obtained results demonstrate promising improvements.

Review of: "RelTopic: A Graph-Based Semantic Relatedness Measure in Topic Ontologies and Its Applicability for Topic Labeling of Old Press Articles"

Review of: "RelTopic: A Graph-Based Semantic Relatedness Measure in Topic Ontologies and Its Applicability for Topic Labeling of Old Press Articles"

Calculating semantic relatedness of lists of nouns using WordNet path length

Lists of semantically related words are better recalled on immediate memory tests than otherwise equivalent lists of unrelated words. However, measuring the degree of relatedness is not straightforward. We report three experiments that assess the ability of various measures of semantic relatedness—including latent semantic analysis (LSA), GloVe, fastText, and a number of measures based on WordNet—to predict whether two lists of words will be differentially recalled. In Experiment 1, all measures except LSA correctly predicted the observed better recall of the related than the unrelated list. In Experiment 2, all measures except JCN predicted that abstract words would be recalled equally as well as concrete words because of their enhanced semantic relatedness. In Experiment 3, LSA, GLoVe, and fastText predicted an enhanced concreteness effect because the concrete words were more related; three WordNet measures predicted a small concreteness effect because the abstract and concrete words did not differ in semantic relatedness; and three other WordNet measures predicted no concreteness effect because the abstract words were more related than the concrete words. A small concreteness effect was observed. Over the three experiments, only two measures, both based on simple WordNet path length, predicted all three results. We suggest that the results are not unexpected because semantic processing in episodic memory experiments differs from that in reading, similarity judgment, and analogy tasks which are the most common way of assessing such measures.

Enhancing Accuracy of Semantic Relatedness Measurement by Word Single-Meaning Embeddings

We propose a lightweight algorithm of learning word single-meaning embeddings (WSME), by exploring WordNet synsets and Doc2vec document embeddings, to enhance the accuracy of semantic relatedness measurement. In our model, each polyseme is decomposed into a series of monosemous words with diverse WordNet synset tags which represent different word meanings, and there is a one-to-one correspondence between a word meaning and a vector. Our algorithm proceeds in 3 steps. First, the word sense disambiguation of each polyseme in different contexts is achieved by computing the maximum relatedness between the context of this polyseme and all its candidate meaning definitions in WordNet. Second, each tagged word is lemmatized according to its synset tag to alleviate the word sparsity problem caused by polysemes decomposition. Third, the word single-meaning embeddings are learned from the meaning-tagged corpus, and the semantic relatedness between words can be more accurately measured based on such embeddings. Our experimental results show that our algorithm achieves better performance on the semantic relatedness measurement compared with existing techniques.

Review of: "RelTopic: A Graph-Based Semantic Relatedness Measure in Topic Ontologies and Its Applicability for Topic Labeling of Old Press Articles"

Review of: "RelTopic: A Graph-Based Semantic Relatedness Measure in Topic Ontologies and Its Applicability for Topic Labeling of Old Press Articles"

Semantic Technologies for Semantic Applications. Part 2. Models of Comparative Text Semantics

Both parts of this paper discuss the basic aspects of semantic computing, semantic technologies, and semantic applications applied to NL-text big data processing for knowledge extracting and decision making. The basic components of the corresponding systems and technologies are reviewed, which include ontologies and semantic models of their use, semantic resources, and semantic component. The semantic resources contain knowledge about the semantics and means for refinement of this semantics. The semantic component of the technology is used to formally describe the meaning of NL-entities and numerically evaluate their pairwise semantic similarity. The main focus of this part is on numerical models of pairwise semantic similarity of NL-entities. These models are important for solving tasks of text semantic clustering and classification and their various applications. Various types of semantic relatedness and semantic similarity measures for NL-entities in the context of semantic computing tasks are discussed and compared. Problems that constrain the practical use of semantic technologies for the development of semantic applications are analyzed.

Exploiting non-taxonomic relations for measuring semantic similarity and relatedness in WordNet

Various applications in computational linguistics and artificial intelligence employ semantic similarity to solve challenging tasks, such as word sense disambiguation, text classification, information retrieval, machine translation, and document clustering. To our knowledge, research to date rely solely on the taxonomic relation “ISA” to evaluate semantic similarity and relatedness between terms. This paper explores the benefits of using all types of non-taxonomic relations in large linked data, such as WordNet knowledge graph, to enhance existing semantic similarity and relatedness measures. We propose a holistic poly-relational approach based on a new relation-based information content and non-taxonomic-based weighted paths to devise a comprehensive semantic similarity and relatedness measure. To demonstrate the benefits of exploiting non-taxonomic relations in a knowledge graph, we used three strategies to deploy non-taxonomic relations at different granularity levels. We conduct experiments on four well-known gold standard datasets. The results of our proposed method demonstrate an improvement over the benchmark semantic similarity methods, including the state-of-the-art knowledge graph embedding techniques, that ranged from 3.8%–23.8%, 1.3%–18.3%, 31.8%–117.2%, and 19.1%–111.1%, on all gold standard datasets MC, RG, WordSim, and Mturk, respectively. These results demonstrate the robustness and scalability of the proposed semantic similarity and relatedness measure, significantly improving existing similarity measures.

Use of word and graph embedding to measure semantic relatedness between Unified Medical Language System concepts.

ObjectiveThe study sought to explore the use of deep learning techniques to measure the semantic relatedness between Unified Medical Language System (UMLS) concepts.Materials and MethodsConcept sentence embeddings were generated for UMLS concepts by applying the word embedding models BioWordVec and various flavors of BERT to concept sentences formed by concatenating UMLS terms. Graph embeddings were generated by the graph convolutional networks and 4 knowledge graph embedding models, using graphs built from UMLS hierarchical relations. Semantic relatedness was measured by the cosine between the concepts’ embedding vectors. Performance was compared with 2 traditional path-based (shortest path and Leacock-Chodorow) measurements and the publicly available concept embeddings, cui2vec, generated from large biomedical corpora. The concept sentence embeddings were also evaluated on a word sense disambiguation (WSD) task. Reference standards used included the semantic relatedness and semantic similarity datasets from the University of Minnesota, concept pairs generated from the Standardized MedDRA Queries and the MeSH (Medical Subject Headings) WSD corpus.ResultsSentence embeddings generated by BioWordVec outperformed all other methods used individually in semantic relatedness measurements. Graph convolutional network graph embedding uniformly outperformed path-based measurements and was better than some word embeddings for the Standardized MedDRA Queries dataset. When used together, combined word and graph embedding achieved the best performance in all datasets. For WSD, the enhanced versions of BERT outperformed BioWordVec.ConclusionsWord and graph embedding techniques can be used to harness terms and relations in the UMLS to measure semantic relatedness between concepts. Concept sentence embedding outperforms path-based measurements and cui2vec, and can be further enhanced by combining with graph embedding.

Measuring semantic relatedness between words using related synsets in African Wordnet

Words should not be treated as if all they do is fulfil roles; they can also have relationships. In our everyday talk, we normally give the meanings of words in terms of their relationships. The approach for measuring semantic relatedness between words is determined by means of a set of words that are closely related to them, and these are normally referred to as the set of determiner words. This article examines the measures of semantic relatedness to which words are associated by means of the variety of types of semantic relationships – such as synonymy, hyponymy, function and association. This relationship is considered within the context of African Wordnet (AWN), which provides South African indigenous languages with a platform to access a machine-readable lexical database organised by meaning. This article argues that computing semantic relatedness using African Wordnet could be helpful in developing an understanding of the meaning of related words for use in different African languages.

Knowledge-Based Semantic Relatedness measure using Semantic features

Measuring semantic relatedness has received much attention for uses in many fields such as information retrieval and natural language processing. For handling synonymous problem in distributional-based measures, many researchers are investigating how to exploit semantic features in lexical sources to form knowledge-based measures. In the knowledge-based measures, a hierarchy model is used to measure the relatedness between words based on only the taxonomical features extracted from a provided lexical source. In this paper, a new knowledge feature-based measure is proposed to build the semantic vector of a word construct on taxonomical and non-taxonomical feature of relation words. The proposed measure utilised the topological parameters that weight the importance of each element in the semantic vector. One of the gold dataset used to assess the proposed model and compare the findings with other related works. The results demonstrated the effectiveness of the proposed model on measuring semantic relatedness between words. In this paper, the research framework is identified based on the observations made on the previous related works that have been conducted for semantic representation and semantic relatedness measures. The required data in this research includes the semantic knowledge-based approach and the evaluation datasets. The semantic knowledge that will be used throughout of this research is extracted from English WordNet 3.1. On the other hand, the evaluation datasets covers the gold standard benchmarks which have been used for evaluating the semantic relatedness measurements and text mining tasks. Finally, the evaluation is preform to evaluate the proposed method (PM) based on approach in this research, in which obtained the result have been analyzed, to discuss and compare based on different performance measure and finding the strength and weakness in this paper, to alternative the semantic representation correlated to this research, to designing and develop the topical-based on the semantic representation method for text mining from Social media.

A survey of semantic relatedness evaluation datasets and procedures

Semantic relatedness between words is a core concept in natural language processing. While countless approaches have been proposed, measuring which one works best is still a challenging task. Thus, in this article, we give a comprehensive overview of the evaluation protocols and datasets for semantic relatedness covering both intrinsic and extrinsic approaches. One the intrinsic side, we give an overview of evaluation datasets covering more than 100 datasets in 20 different languages from a wide range of domains. To provide researchers with better guidance for selecting suitable dataset or even building new and better ones, we describe also the construction and annotation process of the datasets. We also shortly describe the evaluation metrics most frequently used for intrinsic evaluation. As for the extrinsic side, several applications involving semantic relatedness measures are detailed through recent research works and by explaining the benefit brought by the measures.

KeyVector: Unsupervised Keyphrase Extraction Using Weighted Topic via Semantic Related

Keyphrase extraction is a task of automatically selecting topical phrases from a document. We present KeyVector, an unsupervised approach with weighted topics via semantic relatedness for keyphrase extraction. Our method relies on various measures of semantic relatedness of documents, topics and keyphrases in the same vector space, which allow us to compute three keyphrase ranking scores: global semantic score, find more important keyphrases for a given document by measuring the semantic relation between documents and keyphrase embeddings; topic weight, pruning/selecting the candidate keyphrases on the topic level; topic inner score, ranking the keyphrases inside each topic. Keyphrases are then generated by ranking the values of combined three scores for each candidate. We conducted experiments on three evaluation data sets of different length documents and domains. Results show that KeyVector out performs state of the art methods on short, medium and long documents.

Semantic relatedness algorithm for keyword sets of geographic metadata

ABSTRACTAdvances in linked geospatial data, recommender systems, and geographic information retrieval have led to urgent necessity to assess the overall semantic relatedness between keyword sets of geographic metadata. In this study, a new model is proposed for computing the semantic relatedness between arbitrary two keyword sets of geographic metadata stored in current global spatial data infrastructures. In this model, the overall semantic relatedness is derived by pairing these keywords that are found to be most relevant to each other and averaging their relatedness. To find the most relevant keywords across two keyword sets precisely, the keywords in the keyword set of geographic metadata are divided into three kinds: the thesaurus elements, the WordNet elements, and the statistical elements. The thesaurus-lexical relatedness measure (TLRM), the extended thesaurus-lexical relatedness measure (ETLRM), and the Longest Common Substring method are proposed to compute the semantic relatedness between two thesaurus elements, two WordNet elements, a thesaurus element, and a WordNet element and two statistical elements, respectively. A human data set – the geographic-metadata’s keyword set relatedness dataset, which was used to evaluate the precision of the semantic relatedness measures of keyword sets of geographic metadata, was created. The proposed method was evaluated against the human-generated relatedness judgments and was compared with the Jaccard method and Vector Space Model. The results demonstrated that the proposed method achieved a high correlation with human judgments and outperformed the existing methods. Finally, the proposed method was applied to quantitatively linked geospatial data.

Combining and learning word embedding with WordNet for semantic relatedness and similarity measurement

In this research, we propose 3 different approaches to measure the semantic relatedness between 2 words: (i) boost the performance of GloVe word embedding model via removing or transforming abnormal dimensions; (ii) linearly combine the information extracted from WordNet and word embeddings; and (iii) utilize word embedding and 12 linguistic information extracted from WordNet as features for Support Vector Regression. We conducted our experiments on 8 benchmark data sets, and computed Spearman correlations between the outputs of our methods and the ground truth. We report our results together with 3 state‐of‐the‐art approaches. The experimental results show that our method can outperform state‐of‐the‐art approaches in all the selected English benchmark data sets.

Web video classification with visual and contextual semantics

SummaryOn the social Web, the amount of video content either originated from wireless devices or previously received from media servers has increased enormously in the recent years. The astounding growth of Web videos has stimulated researchers to propose new strategies to organize them into their respective categories. Because of complex ontology and large variation in content and quality of Web videos, it is difficult to get sufficient, precisely labeled training data, which causes hindrance in automatic video classification. In this paper, we propose a novel content‐ and context‐based Web video classification framework by rendering external support through category discriminative terms (CDTs) and semantic relatedness measure (SRM). Mainly, a three‐step framework is proposed. Firstly, content‐based video classification is proposed, where twofold use of high‐level concept detectors is leveraged to classify Web videos. Initially, category classifiers induced from VIREO‐374 detectors are trained to classify Web videos, and then concept detectors with high confidence for each video are mapped to CDT through SRM‐assisted semantic content fusion function to further boost the category classifiers, which intuitively provide a more robust measure for Web video classification. Secondly, a context‐based video classification is proposed, where twofold use of contextual information is also harnessed. Initially, cosine similarity and then semantic similarity are measured between text features of each video and CDT through vector space model (VSM)‐ and SRM‐assisted semantic context fusion function, respectively. Finally, classification results from content and context are fused to compensate for the shortcomings of each other, which enhance the video classification performance. Experiments on large‐scale video dataset validate the effectiveness of the proposed solution.