Wikipedia Link Structure Research Articles

Evaluating semantic similarity and relatedness between concepts by combining taxonomic and non-taxonomic semantic features of WordNet and Wikipedia

Many applications in cognitive science and artificial intelligence utilize semantic similarity and relatedness to solve difficult tasks such as information retrieval, word sense disambiguation, and text classification. Previously, several approaches for evaluating concept similarity and relatedness based on WordNet or Wikipedia have been proposed. WordNet-based methods rely on highly precise knowledge but have limited lexical coverage. In contrast, Wikipedia-based models achieve more coverage but sacrifice knowledge quality. Therefore, in this paper, we focus on developing a comprehensive semantic similarity and relatedness method based on WordNet and Wikipedia. To improve the accuracy of existing measures, we combine various taxonomic and non-taxonomic features of WordNet, including gloss, lemmas, examples, sister-terms, derivations, holonyms/meronyms, and hypernyms/hyponyms, with Wikipedia gloss and hyperlinks, to describe concepts. We present a novel technique for extracting ‘is-a’ and ‘part-whole’ relationships between concepts using the Wikipedia link structure. The suggested technique identifies taxonomic and non-taxonomic relationships between concepts and offers dense vector representations of concepts. To fully exploit WordNet and Wikipedia’s semantic attributes, the proposed method integrates their semantic knowledge at feature-level, combining semantic similarity and relatedness into a single comprehensive measure. The experimental results demonstrate the effectiveness of the proposed method over state-of-the-art measures on various gold standard benchmarks.

Wikipedia bi-linear link (WBLM) model: A new approach for measuring semantic similarity and relatedness between linguistic concepts using Wikipedia link structure

Wikipedia links its articles by manually defined semantic relations called the Wikipedia hyperlink (link) structure. The existing Wikipedia link-based semantic similarity (SS) and semantic relatedness (SR) computation models, such as Wikipedia one-way link (WOLM) model and Wikipedia two-way link (WTLM) model, do not assess the strengths of the relationships between a candidate concept and its links (out-links or in-links). These models treat all the links as equally important even though some links are semantically more influential than others and should be given more importance. This phenomenon reduces the accuracy of these models. This paper presents the Wikipedia bi-linear link (WBLM) model that extends the previously proposed WOLM and WTLM models. The WBLM model explores the Wikipedia link structure as a semantic graph and discovers the strongly (bi-linear links) and weakly (out-links or in-links) connected links of a candidate concept. It improves the link-based vector representations of concepts by assigning weights to their connected links according to the strengths of their semantic associations. The experimental results demonstrate that the proposed WBLM model significantly improves the SS and SR computation accuracy of the WOLM model (6.9%, 8%, 24%, 17.3%, 31.2%, 30.6%, 26.5%, and 35.4%) and WTLM model (1.2%, 3.9%, 7.1%, 9.9%, 11%, 6.3%, 12.7%, and 13%), in terms of linear correlations with human judgments on gold standard benchmarks, including MC30, RG65, WS203, SimLex, 353All, MTurk287, MTurk771, and MEN3000, respectively. Moreover, this research offers a deep insight into the Wikipedia link structure and provides an adequate base for understanding it as a semantic graph.

Extracting Semantics from Random Walks on Wikipedia: Comparing Learning and Counting Methods

Semantic relatedness between words has been extracted from a variety of sources.In this ongoing work, we explore and compare several options for determining if semantic relatedness can be extracted from navigation structures in Wikipedia. In that direction, we first investigate the potential of representation learning techniques such as DeepWalk in comparison to previously applied methods based on counting co-occurrences. Since both methods are based on (random) paths in the network, we also study different approaches to generate paths from Wikipedia link structure. For this task, we do not only consider the link structure of Wikipedia, but also actual navigation behavior of users. Finally, we analyze if semantics can also be extracted from smaller subsets of the Wikipedia link network. As a result we find that representation learning techniques mostly outperform the investigated co-occurrence counting methods on the Wikipedia network. However, we find that this is not the case for paths sampled from human navigation behavior.

Research on Domain Term Dictionary Construction Based on Chinese Wikipedia

Domain terms are words or phrases that represent concepts or relationships in a specific domain. It can represent the characteristics of corresponding domains. The automatic construction of domain-specific dictionary is an important task in natural language processing, which can be adopted in domain-specific ontology construction, vertical search, text classification, information retrieval, question answering system etc. In this paper, we propose a novel method for constructing domain term dictionary based on Chinese Wikipedia web resource and deep learning technology. We for first time explore to word representation by Word2vec model integrating Wikipedia link structure. Then we use word clustering algorithm and seed word extraction method to construct an original domain dictionary. Moreover, neural network method is applied to extend domain dictionary. In experiments, different methods were employed to extract the domain-specific terms and their performances were compared in automobile field, the results reveal the effectiveness of our method for construction of domain-specific dictionary.

“Introducing Capisco: a semantically-enhanced search and discovery system for large-scale text corpora”

This article discusses a new approach to scholarly search and discovery in large-scale text corpora. While lexicographic search is at present the predominant means to access large document corpora, it cannot directly address the inherent ambiguity of natural language. As a pragmatic solution, many scholars manually build their own list of suitable search terms to be used in repeated searches in digital libraries and other online resources; however, scholars then have to resolve on a case-by-case basis issues caused by synonyms, homonyms and OCR errors. Our approach differs from this by supporting scholars in developing and refining a set of relevant concepts, searches a large document collection using semantic concepts, and categorizes the potentially relevant documents from search results into worksets. The developed technique revisits the notion of semantic search and redesigns both the underlying data representation and interface support. This is achieved through an end-to-end design that relies centrally on a Concept-in-Context network sourced through the link structure of Wikipedia. We discuss here the principles of our approach, its implementation in the Capisco prototype, and the relationship between established search techniques and our approach.

An Efficient Approach For Semantically-Enhanced Document Clustering By Using Wikipedia Link Structure

Traditional techniques of document clustering do not consider the semantic relationships between words when assigning documents to clusters. For instance, if two documents talking about the same topic do that using different words (which may be synonyms or semantically associated), these techniques may assign documents to different clusters. Previous research has approached this problem by enriching the document representation with the background knowledge in an ontology. This paper presents a new approach to enhance document clustering by exploiting the semantic knowledge contained in Wikipedia. We first map terms within documents to their corresponding Wikipedia concepts. Then, similarity between each pair of terms is calculated by using the Wikipedia's link structure. The document’s vector representation is then adjusted so that terms that are semantically related gain more weight. Our approach differs from related efforts in two aspects: first, unlink others who built their own methods of measuring similarity through the Wikipedia categories; our approach uses a similarity measure that is modelled after the Normalized Google Distance which is a well-known and low-cost method of measuring term similarity. Second, it is more time efficient as it applies an algorithm for phrase extraction from documents prior to matching terms with Wikipedia. Our approach was evaluated by being compared with different methods from the state of the art on two different datasets. Empirical results showed that our approach improved the clustering results as compared to other approaches.

Wikipedia-based topic clustering for microblogs

Microblogging has become a primary channel by which people not only share information, but also search for information. However, microblog search results are most often displayed by simple criteria such as creation time or author. A review of the literature suggests that clustering by topic may be useful, but short posts offer limited scope for clustering using lexical evidence alone. This paper therefore presents an approach to topical clustering based on augmenting lexical evidence with the use of Wikipedia as an external source of evidence for topical similarity. The main idea is to link terms in microblog posts to Wikipedia pages and then to leverage Wikipedia's link structure to estimate semantic similarity, Results show statistically significant relative improvements of about 3% in cluster purity using a relatively small (7500-post, 5-topic) Twitter test collection. Linking terms in microblog posts to Wikipedia pages is also shown to offer a useful basis for cluster labeling.

Entity ranking in Wikipedia: utilising categories, links and topic difficulty prediction

Entity ranking has recently emerged as a research field that aims at retrieving entities as answers to a query. Unlike entity extraction where the goal is to tag names of entities in documents, entity ranking is primarily focused on returning a ranked list of relevant entity names for the query. Many approaches to entity ranking have been proposed, and most of them were evaluated on the INEX Wikipedia test collection. In this paper, we describe a system we developed for ranking Wikipedia entities in answer to a query. The entity ranking approach implemented in our system utilises the known categories, the link structure of Wikipedia, as well as the link co-occurrences with the entity examples (when provided) to retrieve relevant entities as answers to the query. We also extend our entity ranking approach by utilising the knowledge of predicted classes of topic difficulty. To predict the topic difficulty, we generate a classifier that uses features extracted from an INEX topic definition to classify the topic into an experimentally pre-determined class. This knowledge is then utilised to dynamically set the optimal values for the retrieval parameters of our entity ranking system. Our experiments demonstrate that the use of categories and the link structure of Wikipedia can significantly improve entity ranking effectiveness, and that topic difficulty prediction is a promising approach that could also be exploited to further improve the entity ranking performance.

Extraction of Bilingual Terminology from a Multilingual Web-based Encyclopedia

With the demand for bilingual dictionaries covering domain-specific terminology, research in the field of automatic dictionary extraction has become popular. However, the accuracy and coverage of dictionaries created based on bilingual text corpora are often not sufficient for domain-specific terms. Therefore, we present an approach for extracting bilingual dictionaries from the link structure of Wikipedia, a huge scale encyclopedia that contains a vast number of links between articles in different languages. Our methods analyze not only these interlanguage links but extract even more translations from redirect page and link text information. In an experiment which we have interpreted in detail, we proved that the combination of redirect page and link text information achieves much better results than the traditional approach of extracting bilingual terminology from parallel corpora.