Collective entity linking in web text

Entity Linking with Multiple Knowledge Bases: An Ontology Modularization Approach

“…We adapted the work presented in [3] to use Jamendo 12 and Linked Movie Database 13 , two publicly available Linked Data datasets. As those datasets do not have full text descriptions for all entities, the mapping between text around mentions and textual descriptions could not be used, as well as the computation of TF-IDF.…”

“…the ambiguity of a mention) as the main drawback for Entity Linking [4,3,6]. In other words, as bigger the Knowledge Base and the number of candidates per mention as much time it may take to find a solution.…”

What’s New? Analysing Language-Specific Wikipedia Entity Contexts to Support Entity-Centric News Retrieval

“…Examples include information retrieval [4,14,18], named entity disambiguation [1,2,7,8,11,12], text classification [25] and entity ranking [10]. To extract the content of an entity context, many researches directly used the Wikipedia article describing the entity [1,2,8,9,14,[25][26][27]; some works extended the article with all the other Wikipedia articles linked to the Wikipedia article describing the entity [6,7,12]; while some only considered the first paragraph of the Wikipedia article describing the entity [2]. Different from these approaches, our Graph-based approach not only employs in-links and languagelinks to broaden the article set that is likely to mention the entity, but also performs a finer-grained process: extracting the sentences that mention the entity, such that all the sentences in our context are closely related to the target entity.…”

“…As to the context-based representation vector of the entity, [1,11] defined it as the tf-idf/word count/binary occurrence values of all the vocabulary words in the context content; [2,19] defined it as the word count/binary occurrence values of other entities in the context content; [5,6,9,14,25] defined it as the tf-idf similarity values between the target entity's context content and other entities' context contents from Wikipedia; [27] defined it as the visiting probability from the target entity to other entities from Wikipedia; [7,26] used a measurement based on the common entities linked to the target entity and other entities from Wikipedia. Different from all former researches, we employ aspect weights that have a different interpretation of the frequency and selectivity than the typical tf-idf values and take co-occurrence and language specificity of the aspects into account.…”

DoSeR - A Knowledge-Base-Agnostic Framework for Entity Disambiguation Using Semantic Embeddings

“…The PageRank algorithm is a well-researched link-based ranking algorithm that simulates a random walk on the underlying graph and reflects the importance of each node. It has been shown to provide good performance for many applications [25], also in entity disambiguation tasks [6].…”