Lecture

Entity Disambiguation

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In course

CS-423: Distributed information systems

This course introduces the foundations of information retrieval, data mining and knowledge bases, which constitute the foundations of today's Web-based distributed information systems. ...

Description

This lecture covers the concept of Entity Disambiguation, which involves linking text mentions to entries in a knowledge base. It discusses challenges like homonyms and synonyms, and methods such as Label Propagation and Link Prediction. The instructor explains the importance of coherence in entity graphs and the use of Personalized PageRank for disambiguation. The lecture also explores the approach of finding the most likely valid concept candidate linked to a mention, considering factors like total support and popularity measures. Various results from different disambiguation methods are presented, along with a quiz on entity disambiguation and named entity recognition.

Instructor

Karl Aberer

Karl Aberer received his PhD in mathematics in 1991 from the ETH Zürich. From 1991 to 1992 he was postdoctoral fellow at the International Computer Science Institute (ICSI) at the University of California, Berkeley. In 1992, he joined the Integrated Publication and Information Systems institute (IPSI) of GMD in Germany, where he was leading the research division Open Adaptive Information Management Systems. In 2000 he joined EPFL as full professor. Since 2005 he is the director of the Swiss National Research Center for Mobile Information and Communication Systems ( NCCR-MICS, www.mics.ch ). He is member of the editorial boards of VLDB Journal, ACM Transaction on Autonomous and Adaptive Systems and World Wide Web Journal. He has been consulting for the Swiss government in research and science policy as a member of the Swiss Research and Technology Council ( SWTR ) from 2003 - 2011.

Official source

Related concepts (32)

Named-entity recognition

Named-entity recognition (NER) (also known as (named) entity identification, entity chunking, and entity extraction) is a subtask of information extraction that seeks to locate and classify named entities mentioned in unstructured text into pre-defined categories such as person names, organizations, locations, medical codes, time expressions, quantities, monetary values, percentages, etc. Most research on NER/NEE systems has been structured as taking an unannotated block of text, such as this one: Jim bought 300 shares of Acme Corp.

Homonym

In linguistics, homonyms are words which are either homographs – words that have the same spelling (regardless of pronunciation) – or homophones – words that have the same pronunciation (regardless of spelling) –, or both. Using this definition, the words row (propel with oars), row (a linear arrangement) and row (an argument) are homonyms because they are homographs (though only the first two are homophones): so are the words see (vision) and sea (body of water), because they are homophones (though not homographs).

Word-sense disambiguation

Word-sense disambiguation (WSD) is the process of identifying which sense of a word is meant in a sentence or other segment of context. In human language processing and cognition, it is usually subconscious/automatic but can often come to conscious attention when ambiguity impairs clarity of communication, given the pervasive polysemy in natural language. In computational linguistics, it is an open problem that affects other computer-related writing, such as discourse, improving relevance of search engines, anaphora resolution, coherence, and inference.

Knowledge-based systems

A knowledge-based system (KBS) is a computer program that reasons and uses a knowledge base to solve complex problems. The term is broad and refers to many different kinds of systems. The one common theme that unites all knowledge based systems is an attempt to represent knowledge explicitly and a reasoning system that allows it to derive new knowledge. Thus, a knowledge-based system has two distinguishing features: a knowledge base and an inference engine.

Knowledge graph

In knowledge representation and reasoning, knowledge graph is a knowledge base that uses a graph-structured data model or topology to integrate data. Knowledge graphs are often used to store interlinked descriptions of entities - objects, events, situations or abstract concepts - while also encoding the semantics underlying the used terminology. Since the development of the Semantic Web, knowledge graphs are often associated with linked open data projects, focusing on the connections between concepts and entities.

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Information Extraction & Knowledge Inference

Explores information extraction, knowledge inference, taxonomy induction, and entity disambiguation.

Information Extraction: Methods and Applications

Explores methods for information extraction, including traditional and embedding-based approaches, supervised learning, distant supervision, and taxonomy induction.

Entity Disambiguation and Link Prediction

Explores entity disambiguation, linking text to knowledge bases, and link prediction in knowledge graphs with examples from Wikipedia.