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Collective entity linking in web text: A graph-based method
- in: Proceedings of the 34th international Conference on Research and Development in Information Retrieval
, 2011
"... Entity Linking (EL) is the task of linking name mentions in Web text with their referent entities in a knowledge base. Traditional EL methods usually link name mentions in a document by assuming them to be independent. However, there is often additional interdependence between different EL decisions ..."
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Cited by 52 (2 self)
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Entity Linking (EL) is the task of linking name mentions in Web text with their referent entities in a knowledge base. Traditional EL methods usually link name mentions in a document by assuming them to be independent. However, there is often additional interdependence between different EL decisions, i.e., the entities in the same document should be semantically related to each other. In these cases, Collective Entity Linking, in which the name mentions in the same document are linked jointly by exploiting the interdependence between them, can improve the entity linking accuracy. This paper proposes a graph-based collective EL method, which can model and exploit the global interdependence between different EL decisions. Specifically, we first propose a graph-based representation, called Referent Graph, which can model the global interdependence between different EL decisions. Then we propose a collective inference algorithm, which can jointly infer the referent entities of all name mentions by exploiting the interdependence captured in Referent Graph. The key benefit of our method comes from: 1) The global interdependence model of EL decisions; 2) The purely collective nature of the inference algorithm, in which evidence for related EL decisions can be reinforced into high-probability decisions. Experimental results show that our method can achieve significant performance improvement over the traditional EL methods.
Knowledge Base Population: Successful Approaches and Challenges
"... In this paper we give an overview of the Knowledge Base Population (KBP) track at the 2010 Text Analysis Conference. The main goal of KBP is to promote research in discovering facts about entities and augmenting a knowledge base (KB) with these facts. This is done through two tasks, Entity Linking – ..."
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Cited by 51 (9 self)
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In this paper we give an overview of the Knowledge Base Population (KBP) track at the 2010 Text Analysis Conference. The main goal of KBP is to promote research in discovering facts about entities and augmenting a knowledge base (KB) with these facts. This is done through two tasks, Entity Linking – linking names in context to entities in the KB – and Slot Filling – adding information about an entity to the KB. A large source collection of newswire and web documents is provided from which systems are to discover information. Attributes (“slots”) derived from Wikipedia infoboxes are used to create the reference KB. In this paper we provide an overview of the techniques which can serve as a basis for a good KBP system, lay out the remaining challenges by comparison with traditional Information Extraction (IE) and Question Answering (QA) tasks, and provide some suggestions to address these challenges. 1
A generative entity-mention model for linking entities with knowledge base
- In ACL
, 2011
"... Linking entities with knowledge base (entity linking) is a key issue in bridging the textual data with the structural knowledge base. Due to the name variation problem and the name ambiguity problem, the entity linking decisions are critically depending on the heterogenous knowledge of entities. In ..."
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Cited by 25 (1 self)
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Linking entities with knowledge base (entity linking) is a key issue in bridging the textual data with the structural knowledge base. Due to the name variation problem and the name ambiguity problem, the entity linking decisions are critically depending on the heterogenous knowledge of entities. In this paper, we propose a generative probabilistic model, called entity-mention model, which can leverage heterogenous entity knowledge (including popularity knowledge, name knowledge and context knowledge) for the entity linking task. In our model, each name mention to be linked is modeled as a sample generated through a three-step generative story, and the entity knowledge is encoded in the distribution of entities in document P(e), the distribution of possible names of a specific entity P(s|e), and the distribution of possible contexts of a specific entity P(c|e). To find the referent entity of a name mention, our method combines the evidences from all the three distributions P(e), P(s|e) and P(c|e). Experimental results show that our method can significantly outperform the traditional methods. 1
No noun phrase left behind: Detecting and typing unlinkable entities
- In Proceedings of EMNLP
, 2012
"... Entity linking systems link noun-phrase mentions in text to their corresponding Wikipedia articles. However, NLP applications would gain from the ability to detect and type all entities mentioned in text, including the long tail of entities not prominent enough to have their own Wikipedia articles. ..."
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Cited by 17 (2 self)
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Entity linking systems link noun-phrase mentions in text to their corresponding Wikipedia articles. However, NLP applications would gain from the ability to detect and type all entities mentioned in text, including the long tail of entities not prominent enough to have their own Wikipedia articles. In this paper we show that once the Wikipedia entities mentioned in a corpus of textual assertions are linked, this can further enable the detection and fine-grained typing of the unlinkable entities. Our proposed method for detecting unlinkable entities achieves 24 % greater accuracy than a Named Entity Recognition baseline, and our method for fine-grained typing is able to propagate over 1,000 types from linked Wikipedia entities to unlinkable entities. Detection and typing of unlinkable entities can increase yield for NLP applications such as typed question answering. 1
Elementary: Large-scale Knowledge-base Construction via Machine Learning and Statistical Inference
"... Researchers have approached knowledge-base construction (KBC) with a wide range of data resources and techniques. We present Elementary, a prototype KBC system that is able to combine diverse resources and different KBC techniques via machine learning and statistical inference to construct knowledge ..."
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Cited by 17 (5 self)
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Researchers have approached knowledge-base construction (KBC) with a wide range of data resources and techniques. We present Elementary, a prototype KBC system that is able to combine diverse resources and different KBC techniques via machine learning and statistical inference to construct knowledge bases. Using Elementary, we have implemented a solution to the TAC-KBP challenge with quality comparable to the state of the art, as well as an end-to-end online demonstration that automatically and continuously enriches Wikipedia with structured data by reading millions of webpages on a daily basis. We describe several challenges and our solutions in designing, implementing, and deploying Elementary. In particular, we first describe the conceptual framework and architecture of Elementary, and then discuss how we address scalability challenges to enable Web-scale deployment. First, to take advantage of diverse data resources and proven techniques, Elementary employs Markov logic, a succinct yet expressive language to specify probabilistic graphical models. Elementary accepts both domain-knowledge rules and classical machine-learning models such as conditional random fields, thereby integrating different data resources and KBC techniques in a principled manner. Second, to support large-scale KBC with terabytes of data and millions of entities, Elementary
Entity linking at web scale
- In Knowledge Extraction Workshop (AKBC-WEKEX
, 2012
"... This paper investigates entity linking over millions of high-precision extractions from a corpus of 500 million Web documents, toward the goal of creating a useful knowledge base of general facts. This paper is the first to report on entity linking over this many extractions, and describes new oppor ..."
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Cited by 15 (1 self)
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This paper investigates entity linking over millions of high-precision extractions from a corpus of 500 million Web documents, toward the goal of creating a useful knowledge base of general facts. This paper is the first to report on entity linking over this many extractions, and describes new opportunities (such as corpus-level features) and challenges we found when entity linking at Web scale. We present several techniques that we developed and also lessons that we learned. We envision a future where information extraction and entity linking are paired to automatically generate knowledge bases with billions of assertions over millions of linked entities. 1
Kore: keyphrase overlap relatedness for entity disambiguation
- In Proceedings of the 21st ACM CIKM
, 2012
"... Measuring the semantic relatedness between two entities is the basis for numerous tasks in IR, NLP, and Web-based knowledge extraction. This paper focuses on disambiguating names in a Web or text document by jointly mapping all names onto semantically related entities registered in a knowledge base. ..."
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Cited by 14 (2 self)
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Measuring the semantic relatedness between two entities is the basis for numerous tasks in IR, NLP, and Web-based knowledge extraction. This paper focuses on disambiguating names in a Web or text document by jointly mapping all names onto semantically related entities registered in a knowledge base. To this end, we have developed a novel notion of semantic relatedness between two entities represented as sets of weighted (multi-word) keyphrases, with consideration of partially overlapping phrases. This measure improves the quality of prior link-based models, and also eliminates the need for (usually Wikipedia-centric) explicit interlinkage between entities. Thus, our method is more versatile and can cope with long-tail and newly emerging entities that have few or no links associated with them. For efficiency, we have developed approximation techniques based on min-hash sketches and locality-sensitive hashing. Our experiments on semantic relatedness and on named entity disambiguation demonstrate the superiority of our method compared to state-of-the-art baselines.
Linking Entities to a Knowledge Base with Query Expansion
"... In this paper we present a novel approach to entity linking based on a statistical language model-based information retrieval with query expansion. We use both local contexts and global world knowledge to expand query language models. We place a strong emphasis on named entities in the local context ..."
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Cited by 14 (0 self)
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In this paper we present a novel approach to entity linking based on a statistical language model-based information retrieval with query expansion. We use both local contexts and global world knowledge to expand query language models. We place a strong emphasis on named entities in the local contexts and explore a positional language model to weigh them differently based on their distances to the query. Our experiments on the TAC-KBP 2010 data show that incorporating such contextual information indeed aids in disambiguating the named entities and consistently improves the entity linking performance. Compared with the official results from KBP 2010 participants, our system shows competitive performance. 1
Computing FOAF Co-reference Relations with Rules and Machine Learning ⋆
"... Abstract. The friend of a friend (FOAF) vocabulary is widely used on the Web to describe ’agents ’ (people, groups and organizations) and their properties. Since FOAF does not require unique ID for agents, it is not clear when two FOAF instances should be linked as co-referent, i.e., denote the same ..."
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Cited by 11 (2 self)
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Abstract. The friend of a friend (FOAF) vocabulary is widely used on the Web to describe ’agents ’ (people, groups and organizations) and their properties. Since FOAF does not require unique ID for agents, it is not clear when two FOAF instances should be linked as co-referent, i.e., denote the same entity in the world. One approach is to use logical constraints such as the presence of inverse functional properties as evidence that two individuals are the same. Another applies heuristics based on the string similarity of values of FOAF properties such as name and school as evidence for or against co-reference. Performance is limited, however, by many factors: non-semantic string matching, noise, changes in the world, and the lack of more sophisticated graph analytics. We describe a prototype system that takes a set of FOAF agents and identifies subsets that are believed to be co-referent. The system uses logical constraints (e.g., IFPs), strong heuristics (e.g., FOAF agents described in the same file are not co-referent), and an SVM generated classifier. We present initial results using data collected from Swoogle and other sources and describe plans for additional analysis.
Mining evidences for named entity disambiguation
- In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
, 2013
"... Named entity disambiguation is the task of disambiguating named entity mentions in natural language text and link them to their corresponding entries in a knowledge base such as Wikipedia. Such disambiguation can help enhance readability and add semantics to plain text. It is also a central step in ..."
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Cited by 10 (2 self)
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Named entity disambiguation is the task of disambiguating named entity mentions in natural language text and link them to their corresponding entries in a knowledge base such as Wikipedia. Such disambiguation can help enhance readability and add semantics to plain text. It is also a central step in constructing high-quality information net-work or knowledge graph from unstructured text. Previous research has tackled this problem by making use of vari-ous textual and structural features from a knowledge base. Most of the proposed algorithms assume that a knowledge base can provide enough explicit and useful information to help disambiguate a mention to the right entity. However, the existing knowledge bases are rarely complete (likely will never be), thus leading to poor performance on short queries with not well-known contexts. In such cases, we need to col-lect additional evidences scattered in internal and external corpus to augment the knowledge bases and enhance their disambiguation power. In this work, we propose a genera-tive model and an incremental algorithm to automatically mine useful evidences across documents. With a specific modeling of “background topic ” and “unknown entities”, our model is able to harvest useful evidences out of noisy in-formation. Experimental results show that our proposed method outperforms the state-of-the-art approaches signif-icantly: boosting the disambiguation accuracy from 43% (baseline) to 86 % on short queries derived from tweets.
