Over the past decade, knowledge bases (KB) have been increasingly utilized to complete and enrich representation of queries documents in order improve document retrieval task. Although many approaches used KB for such purposes, problem how effectively leverage entity-based still needs be resolved. This paper proposes a Purely Entity-based Semantic Search Approach Information Retrieval (PESS4IR)...

In this paper, we construct an entity recognition and linking system using Chinese Wikipedia and knowledge base. We utilize refined filter rules in entity recognition module, and then generate candidate entities by search engine and attributes in Wikipedia article pages. In entity linking module, we propose a hybrid entity re-ranking method combined with three features: textual and semantic mat...

In this paper, we describe our participation in the English Subtopic Mining and Document Ranking subtasks of the NTCIR-11 IMINE Task. In the Subtopic Mining subtask, we mine subtopics from query suggestions, query dimensions, and Freebase entities of a given query, rank them based on their importance for the given query, and finally construct a two-level hierarchy of subtopics. In the Document ...

Nowadays, search on the Web goes beyond the retrieval of textual Web sites and increasingly takes advantage of the growing amount of structured data. Of particular interest is entity search, where the units of retrieval are structured entities instead of textual documents. These entities reside in different sources, which may provide only limited information about their content and are therefor...

In this paper, we report the participation of ZZ_INFO_TECH team in entity linking task for Knowledge Base Population at Text Analysis Conference 2013. Our team submitted 4 runs for this evaluation task. In our system, we adopts some simple heuristic rules and context similarity to filter irrelevant candidates; extracts multiple features and trains SVM Ranking model to realize candidate ranking;...

This paper introduces Explicit Semantic Ranking (ESR), a new ranking technique that leverages knowledge graph embedding. Analysis of the query log from our academic search engine, SemanticScholar.org, reveals that a major error source is its inability to understand the meaning of research concepts in queries. To addresses this challenge, ESR represents queries and documents in the entity space ...

The popularity of social media shatters the barrier for online users to create and share information at any place at any time. As a consequence, it has become increasing difficult to locate relevance information about an entity. Timeline has been proven to provide an effective and efficient access to understand an entity by displaying a list of episodes about the entity in chronological order. ...

Corpus-based set expansion (i.e., finding the “complete” set of entities belonging to the same semantic class, based on a given corpus and a tiny set of seeds) is a critical task in knowledge discovery. It may facilitate numerous downstream applications, such as information extraction, taxonomy induction, question answering, and web search. To discover new entities in an expanded set, previous ...

This demo presents MAGES (multilingual angle-integrated grouping-based entity summarization), an entity summarization system for a large knowledge base such as DBpedia based on a entity-group-bound ranking in a single integrated entity space across multiple language-specific editions. MAGES offers a multilingual angle-integrated space model, which has the advantage of overcoming missing semanti...

This paper describes a multiple-stage retrieval framework for the task of related entity finding on TREC 2010 Entity Track. In the document retrieval stage, search engine is used to improve the retrieval accuracy. In the entity extraction and filtering stage, we extract entity with NER tools, Wikipedia and text pattern recognition. Then stoplist and other rules are employed to filter entity. De...