Entity linking

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In natural language processing, Entity Linking, also referred to as named-entity disambiguation (NED), named-entity recognition and disambiguation (NERD) or named-entity normalization (NEN) [1] is the task of assigning a unique identity to entities (such as famous individuals, locations, or companies) mentioned in text. For example, given the sentence "Paris is the capital of France", the main idea is to first identify "Paris" and "France" as named entities, and then to determine that "Paris" refers to the city of Paris and not to Paris Hilton or any other entity that could be referred to as "Paris" and "France" to the french country. The Entity Linking task is composed of 3 subtasks. First, Named Entity Recognition, which consist in the extraction of named entities from a text. Second, for each named entity, the objective is to generate candidates from a Knowledge Base (e.g. Wikipedia, Wikidata, DBPedia, ...). We call this step candidate generation. The main challenge being that we want to get the corresponding entity inside the candidates set. Lastly, the objective is to choose from the candidate set the correct entity. We call this step disambiguation.

Contents

On way to see the Entity Linking task is as the combination of the NER task followed by the NEL task, the NEL task being the combination of candidate generation and disambiguation.

In entity linking, each named entity is linked to a unique identifier. Often, this identifier corresponds to a Wikipedia page. Entity Linking - Short Example.png
In entity linking, each named entity is linked to a unique identifier. Often, this identifier corresponds to a Wikipedia page.

Introduction

In entity linking, words of interest (names of persons, locations and companies) are mapped from an input text to corresponding unique entities in a target knowledge base. Words of interest are called named entities (NEs), mentions, or surface forms. The target knowledge base depends on the intended application, but for entity linking systems intended to work on open-domain text it is common to use knowledge-bases derived from Wikipedia (such as Wikidata or DBpedia). [1] [2] In this case, each individual Wikipedia page is regarded as a separate entity. Entity linking techniques that map named entities to Wikipedia entities are also called wikification. [3]

Considering again the example sentence "Paris is the capital of France", the expected output of an entity linking system will be Paris and France. These uniform resource locators (URLs) can be used as unique uniform resource identifiers (URIs) for the entities in the knowledge base. Using a different knowledge base will return different URIs, but for knowledge bases built starting from Wikipedia there exist one-to-one URI mappings. [4]

In most cases, knowledge bases are manually built, [5] but in applications where large text corpora are available, the knowledge base can be inferred automatically from the available text. [6]

Entity linking is a critical step to bridge web data with knowledge bases, which is beneficial for annotating the huge amount of raw and often noisy data on the Web and contributes to the vision of the Semantic Web. [7] In addition to entity linking, there are other critical steps including but not limited to event extraction, [8] and event linking [9] etc.

Applications

Entity linking is beneficial in fields that need to extract abstract representations from text, as it happens in text analysis, recommender systems, semantic search and chatbots. In all these fields, concepts relevant to the application are separated from text and other non-meaningful data. [10] [11]

For example, a common task performed by search engines is to find documents that are similar to one given as input, or to find additional information about the persons that are mentioned in it. Consider a sentence that contains the expression "the capital of France": without entity linking, the search engine that looks at the content of documents would not be able to directly retrieve documents containing the word "Paris", leading to so-called false negatives (FN). Even worse, the search engine might produce spurious matches (or false positives (FP)), such as retrieving documents referring to "France" as a country.

Many approaches orthogonal to entity linking exist to retrieve documents similar to an input document. For example, latent semantic analysis (LSA) or comparing document embeddings obtained with doc2vec. However, these techniques do not allow the same fine-grained control that is offered by entity linking, as they will return other documents instead of creating high-level representations of the original one. For example, obtaining schematic information about "Paris", as presented by Wikipedia infoboxes would be much less straightforward, or sometimes even unfeasible, depending on the query complexity. [12]

Moreover, entity linking has been used to improve the performance of information retrieval systems [1] and to improve search performance on digital libraries. [13] Entity linking is also a key input for semantic search. [14] [15]

Challenges

There are various difficulties in performing entity linking. Some of these are intrinsic to the task, [16] such as text ambiguity. Others are relevant in real-world use, such as scalability and execution time.

Entity linking related to other concepts. Definitions are often blurry and vary slightly between authors.

Paris is the capital of France.

would be processed by an NER system to obtain the following output:

[Paris]City is the capital of [France]Country.

NER is usually a preprocessing step of an entity linking system, as it can be useful to know in advance which words should be linked to entities of the knowledge base.

Paris is the capital of France. It is also the largest city in France.

In this example, a coreference resolution algorithm would identify that the pronoun It refers to Paris, and not to France or to another entity. A notable distinction compared to entity linking is that Coreference Resolution does not assign any unique identity to the words it matches, but it simply says whether they refer to the same entity or not. In that sense, predictions from a coreference resolution system could be useful to a subsequent entity linking component.

Approaches

Entity linking has been a hot topic in industry and academia for the last decade. Many challenges are unsolved, but many entity linking systems have been proposed, with widely different strengths and weaknesses. [24]

Broadly speaking, modern entity linking systems can be divided into two categories:

Often entity linking systems use both knowledge graphs and textual features extracted from, for example, the text corpora used to build the knowledge graphs themselves. [21] [22]

Typical entity linking steps. 1. NER - find named entities in the text (here, Paris and France); 2. link found named named entities to corresponding unique identifiers (here, Wikipedia pages). (2) is often done by: 2.1. defining a metric for comparing candidates in the system; 2.2. creating a small set of candidate identifiers for each named entity, and 2.3. Scoring candidates with the metric and choosing one that has the highest score. Entity Linking - Example of pipeline.png
Typical entity linking steps. 1. NER - find named entities in the text (here, Paris and France); 2. link found named named entities to corresponding unique identifiers (here, Wikipedia pages). (2) is often done by: 2.1. defining a metric for comparing candidates in the system; 2.2. creating a small set of candidate identifiers for each named entity, and 2.3. Scoring candidates with the metric and choosing one that has the highest score.

Text-based

The seminal work by Cucerzan in 2007 published one of the first entity linking systems. Specifically, it tackled the task of wikification, that is, linking textual mentions to Wikipedia pages. [25] This system categorizes pages into entity, disambiguation, or list pages. The set of entities present in each entity page is used to build the entity's context. The final step is a collective disambiguation by comparing binary vectors of hand-crafted features each entity's context. Cucerzan's system is still used as baseline for recent work. [27]

Rao et al. [16] proposed a two-step algorithm to link named entities to entities in a target knowledge base. First, candidate entities are chosen using string matching, acronyms, and known aliases. Then, the best link among the candidates is chosen with a ranking support vector machine (SVM) that uses linguistic features.

Recent systems, such as by Tsai et al., [23] use word embeddings obtained with a skip-gram model as language features, and can be applied to any language for which a large corpus to build word embeddings is available. Like most entity linking systems, it has two steps: an initial candidate selection, and ranking using linear SVM.

Various approaches have been tried to tackle the problem of entity ambiguity. The seminal approach of Milne and Witten uses supervised learning using the anchor texts of Wikipedia entities as training data. [28] Other approaches also collected training data based on unambiguous synonyms. [29]

Graph-based

Modern entity linking systems also use large knowledge graphs created from knowledge bases such as Wikipedia, besides textual features generated from input documents or text corpora. Moreover, multilingual entity linking based on natural language processing (NLP) is difficult, because it requires either large text corpora, which are absent for many languages, or hand-crafted grammar rules, which are widely different between languages. Graph-based entity linking uses features of the graph topology or multi-hop connections between entities, which are hidden to simple text analysis.

Han et al. propose the creation of a disambiguation graph (a subgraph of the knowledge base which contains candidate entities). [2] This graph is used for collective ranking to select the best candidate entity for each textual mention.

Another famous approach is AIDA, [30] which uses a series of complex graph algorithms and a greedy algorithm that identifies coherent mentions on a dense subgraph by also considering context similarities and vertex importance features to perform collective disambiguation. [26]

Alhelbawy et al. presented an entity linking system that uses PageRank to perform collective entity linking on a disambiguation graph, and to understand which entities are more strongly related to each other and so would represent a better linking. [20] Graph ranking (or vertex ranking) algorithms such as PageRank (PR) and Hyperlink-Induced Topic Search (HITS) aim to score node according their relative importance in the graph.

Mathematical

Mathematical expressions (symbols and formulae) can be linked to semantic entities (e.g., Wikipedia articles [31] or Wikidata items [32] ) labeled with their natural language meaning. This is essential for disambiguation, since symbols may have different meanings (e.g., "E" can be "energy" or "expectation value", etc.). [33] [32] The math entity linking process can be facilitated and accelerated through annotation recommendation, e.g., using the "AnnoMathTeX" system that is hosted by Wikimedia. [34] [35] [36]

To facilitate the reproducibility of Mathematical Entity Linking (MathEL) experiments, the benchmark MathMLben was created. [37] [38] It contains formulae from Wikipedia, the arXiV and the NIST Digital Library of Mathematical Functions (DLMF). Formulae entries in the benchmark are labeled and augmented by Wikidata markup. [32] Furthermore, for two large corporae from the arXiv [39] and zbMATH [40] repository distributions of mathematical notation were examined. Mathematical Objects of Interest (MOI) are identified as potential candidates for MathEL. [41]

Besides linking to Wikipedia, Schubotz [38] and Scharpf et al. [32] describe linking mathematical formula content to Wikidata, both in MathML and LaTeX markup. To extend classical citations by mathematical, they call for a Formula Concept Discovery (FCD) and Formula Concept Recognition (FCR) challenge to elaborate automated MathEL. Their FCD approach yields a recall of 68% for retrieving equivalent representations of frequent formulae, and 72% for extracting the formula name from the surrounding text on the NTCIR [42] arXiv dataset. [36]

See also

Related Research Articles

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<span class="mw-page-title-main">Knowledge graph</span> Type of knowledge base

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  40. ↑ "zbMath mathematical document library".
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