Learning to link with wikipedia

Milne, David; Witten, Ian H.

doi:10.1145/1458082.1458150

Cited by 933 publications

(907 citation statements)

References 9 publications

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“…Each text has all the meaning bearing phrases annotated with at most one DBpedia resource. Aquaint50 dataset contains 50 documents from the AQUAINT corpus, that were used by Milne and Witten [19]. They have been linked and disambiguated to Wikipedia articles by their system, and the results were evaluated using Amazon Mechanical Turk 5 .…”

Section: Evaluated Methodsmentioning

confidence: 99%

Path-Based Semantic Relatedness on Linked Data and Its Use to Word and Entity Disambiguation

Hulpuș

Prangnawarat

Hayes

2015

The Semantic Web - ISWC 2015

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Abstract. Semantic relatedness and disambiguation are fundamental problems for linking text documents to the Web of Data. There are many approaches dealing with both problems but most of them rely on word or concept distribution over Wikipedia. They are therefore not applicable to concepts that do not have a rich textual description. In this paper, we show that semantic relatedness can also be accurately computed by analysing only the graph structure of the knowledge base. In addition, we propose a joint approach to entity and word-sense disambiguation that makes use of graph-based relatedness. As opposed to the majority of state-of-the-art systems that target mainly named entities, we use our approach to disambiguate both entities and common nouns. In our experiments, we first validate our relatedness measure on multiple knowledge bases and ground truth datasets and show that it performs better than related state-of-the-art graph based measures. Afterwards, we evaluate the disambiguation algorithm and show that it also achieves superior disambiguation accuracy with respect to alternative state-ofthe-art graph-based algorithms.

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Section: Evaluated Methodsmentioning

confidence: 99%

Path-Based Semantic Relatedness on Linked Data and Its Use to Word and Entity Disambiguation

Hulpuș

Prangnawarat

Hayes

2015

The Semantic Web - ISWC 2015

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“…This paper mines associative relations between entities from Wikipedia 1 , using the method proposed by Milne and Witten(2008). Specifically, given two entities e 1 and e 2 , we compute the semantic distance dist(e 1 , e 2 ) between them as:…”

Section: Mining Entity Semantic Knowledgementioning

confidence: 99%

Reasoning with Heterogeneous Knowledge for Commonsense Machine Comprehension

Lin¹,

Sun²,

Han³

2017

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

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Reasoning with commonsense knowledge is critical for natural language understanding.Traditional methods for commonsense machine comprehension mostly only focus on one specific kind of knowledge, neglecting the fact that commonsense reasoning requires simultaneously considering different kinds of commonsense knowledge. In this paper, we propose a multi-knowledge reasoning method, which can exploit heterogeneous knowledge for commonsense machine comprehension. Specifically, we first mine different kinds of knowledge (including event narrative knowledge, entity semantic knowledge and sentiment coherent knowledge) and encode them as inference rules with costs. Then we propose a multiknowledge reasoning model, which selects inference rules for a specific reasoning context using attention mechanism, and reasons by summarizing all valid inference rules. Experiments on RocStories show that our method outperforms traditional models significantly.

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“…A famous example of this kind of similarity measures is the Normalized Google Distance [9], which uses Google as a corpus of documents. We use the Wikipedia [27] relatedness measure, as defined by Milne et al [20,21] because of its easiness of use. This distance adapts the Normalized Google Distance to use Wikipedia as a corpus of reference for computation.…”

Section: Combining Probabilistic Semantic Similarity Measures Within mentioning

confidence: 99%

“…with probability α n+α (21) where x * i is one of the k unique values among the observations gathered.…”

Section: Preliminaries: Dirichlet Processmentioning

confidence: 99%

Bridging Gaps Between Subjective Logic and Semantic Web

Ceolin¹,

Nottamkandath²,

Fokkink³

2014

Uncertainty Reasoning for the Semantic Web III

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Abstract. Subjective logic is a powerful probabilistic logic which is useful to handle data in case of uncertainty. Subjective logic and the Semantic Web can mutually benefit from each other, since subjective logic is useful to handle the inner noisiness of the Semantic Web data, while the Semantic Web offers a means to obtain evidence useful for performing evidential reasoning based on subjective logic. In this chapter we describe three extensions and applications of subjective logic in the Semantic Web, namely: the use of deterministic and probabilistic semantic similarity measures for weighing subjective opinions, a way for accounting for partial observations, and "open world opinion", i.e. subjective opinions based on Dirichlet processes, which extend multinomial opinions. For each of these extensions, we provide examples and applications to prove their validity.

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Learning to link with wikipedia

Cited by 933 publications

References 9 publications

Path-Based Semantic Relatedness on Linked Data and Its Use to Word and Entity Disambiguation

Path-Based Semantic Relatedness on Linked Data and Its Use to Word and Entity Disambiguation

Reasoning with Heterogeneous Knowledge for Commonsense Machine Comprehension

Bridging Gaps Between Subjective Logic and Semantic Web

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