The Web bears the potential to become the world’s most comprehensive knowledge base. Organizing information from the Web into entity-relationship graph structures could be a first step towards unleashing this potential. In a second step, the inherent semantics of such structures would have to be exploited by expressive search techniques that go beyond today’s keyword search paradigm. In this re...

Entity search is a new application meeting either precise or vague requirements from the search engines users. Baidu Cup 2016 Challenge just provided such a chance to tackle the problem of the entity search. We achieved the first place with the average MAP scores on 4 tasks including movie, tvShow, celebrity and restaurant. In this paper, we propose a series of similarity features based on both...

Named Entity Disambiguation (NED) refers to the task of mapping different named entity mentions in running text to their correct interpretations in a specific knowledge base (KB). This paper presents a collective disambiguation approach using a graph model. All possible NE candidates are represented as nodes in the graph and associations between different candidates are represented by edges bet...

Ranking results to queries over information spaces where the data is represented in a directed graph has been the focus of much recent research work. In this report, we study three ranking paradigms addressing di erent issues in this regard. Entity-Relation(ER) graphs have entities as nodes, with relations between them being represented as edges. The PageRank system for ranking relies on the li...

Coreference resolution is one of the first stages in deep language understanding and its importance has been well recognized in the natural language processing community. In this paper, we propose a generative, unsupervised ranking model for entity coreference resolution by introducing resolution mode variables. Our unsupervised system achieves 58.44% F1 score of the CoNLL metric on the English...

This paper address the problem of entity linking. Specifically, given an entity mentioned in unstructured texts, the task is to link this entity with an entry stored in the existing knowledge base. This is an important task for information extraction. It can serve as a convenient gateway to encyclopedic information, and can greatly improve the web users’ experience. Previous learning based solu...

We focus on two research issues in entity search: how to score a document or snippet that potentially supports a candidate entity, and how to aggregate or combine scores from different snippets into an entity score. Proximity scoring has been studied in IR outside the scope of entity search. However, aggregation has been hardwired except in a few cases where probabilistic language models are us...

The group of PRIS focuses on both tasks in Entity Track this year, Related Entity Finding (REF) and Entity List Completion (ELC). This paper reports the approaches to the two tasks. In REF, three points are improved based the method of last two year: building entity lexicons including more information, introducing a distance algorithm between keywords and entities to entity ranking, allocating ...

In this paper we describe our participation in INEX 2009 in the Ad Hoc Track, the Book Track, and the Entity Ranking Track. In the Ad Hoc track we investigate focused link evidence, using only links from retrieved sections. The new collection is not only annotated with Wikipedia categories, but also with YAGO/WordNet categories. We explore how we can use both types of category information, in t...