Effective Entity Linking and Disambiguation Algorithms for User-Generated Content (UGC)

Effective Entity Linking and Disambiguation Algorithms for User-Generated Content (UGC)

Senthil Kumar Narayanasamy (VIT University, India) and Dinakaran Muruganantham (VIT University, India)
DOI: 10.4018/978-1-5225-5384-7.ch018
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The exponential growth of data emerging out of social media is causing challenges in decision-making systems and poses a critical hindrance in searching for the potential information. The major objective of this chapter is to convert the unstructured data in social media into the meaningful structure format, which in return brings the robustness to the information extraction process. Further, it has the inherent capability to prune for named entities from the unstructured data and store the entities into the knowledge base for important facts. In this chapter, the authors explain the methods to identify all the critical interpretations taken over to find the named entities from Twitter streams and the techniques to proportionally link it with appropriate knowledge sources such as DBpedia.
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The conventional methods followed for information extraction in text documents (as they are structured and well-formed) is totally different with information extraction in social media contents. The social media contents are mostly unstructured and especially ill-formed to extract the information from it. As stated by the authors Laere, Schockaert, Tanasescu, Dhoedt, & Jones (2014) and Giridhar, Abdelzaher, George, & Kaplan (2015, March), it was estimated that the accuracy rate of precision in structured documents is pointing to 89% whereas unstructured documents hold below 64%. To culminate this difference, several approaches have been discussed and techniques were proposed to boost the precision and recall rate of unstructured documents as given by Lee, Ganti, Srivatsa, & Li (2014, December) and Imran, Castillo, Diaz, & Vieweg (2015); but still, problems persist and pertaining in many situations. In order to streamline the accuracy rate over precision and recall, we have here proposed some methods to augment the precision and use new strategies to overcome the impeding difficulties.

To start with the extraction process, the principal task is to find the potential named entities out from the unstructured text. In our case, we have taken Twitter social media content and identified the named entities from its streams. But the objectivity comes when we deal with real world entities which have been mapped with one-to-many cardinality over knowledge sources and pinches in for the major setbacks for further processes. Besides as the tweets are very short and most of the instances informal in nature, finding potential named entities out of tweet is a crucial task for any automated systems. This sort of ambiguity conundrum is very high in information retrieval context and yields huge difficulties to Named Entity Recognition (NER) systems. To conduct entity identification process, we have used the Markov Network (Lee et al., 2014, December), that was deployed for many conventional information extraction tasks and yielded high accuracy rate. In our cases as we have taken Twitter social media streams, the entities were represented with nodes and the edges will get connected between the conditional dependencies over selected named entities. If we dig deep closer to this whole network, it would almost resemble to Bayesian Network except the fact that edges were cyclic and undirected. For any document, the entity is appropriately mapped with its sheer interpretation of selected named entities suggested by the knowledge source. In some worst cases as we had witnessed in the empirical results, it has shown that few entities has no link to relate with the knowledge source and it has paved way for ambiguous connection and lead to bad search results. This was taken as one of the research gap identified in the extraction process and we had given the solution for the same in the following sections.

The Hidden Markov Model uses many language processing tasks such as POS tagging, Named Entity Detection, and Classification, etc. In this proposed approach, we have taken Twitter as a social media site and carry out the process of identifying the potential named entities from Twitter streams. As the tweets are very short and noisy, finding named entities is a challenging task and linking named entities to appropriate knowledge base mentions is yet another cumbersome process to deal with. Hence, in this proposed system, we have explained the mechanism to link entities to knowledge base, removing the ambiguity persisting over the extracted named entities and enhance the capabilities of searching much easier than before using semantic Web technologies like RDF/SPARQL.

Key Terms in this Chapter

RDF: Resource description framework (RDF) is a family of world wide web consortium (W3C) specifications originally designed as a metadata data model.

SPARQL: SPARQL (pronounced “sparkle,” a recursive acronym for SPARQL protocol and RDF query language) is an RDF query language, that is, a semantic query language for databases, able to retrieve and manipulate data stored in resource description framework (RDF) format.

NER: Named-entity recognition (NER; also known as entity identification, entity chunking, and entity extraction) is a subtask of information extraction that seeks to locate and classify named entities in text into predefined categories such as the names of persons, organizations, locations, expressions of times, quantities.

DBpedia: The DBpedia DataID vocabulary is a metadata system for detailed descriptions of datasets and their physical instances, as well as their relation to agents like persons or organizations in regard to their rights and responsibilities.

Word Sense Disambiguation: In computational linguistics, word-sense disambiguation (WSD) is an open problem of natural language processing and ontology. WSD is identifying which sense of a word (i.e., meaning) is used in a sentence, when the word has multiple meanings.

LDA: In natural language processing, latent dirichlet allocation is a generative statistical model that allows sets of observations to be explained by unobserved groups that explain why some parts of the data are similar.

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