Entity Linking and Knowledge Graph Construction › Neural and End-to-End Approaches

The stage decomposition helps diagnose errors, but it does not require separate learned representations. Components may share an encoder or be trained jointly. Joint systems can use linking evidence when deciding whether a span is a mention, while a modular design permits separate replacement or evaluation of its components.

Kolitsas et al. (2018) jointly learn mention detection and disambiguation scores for candidate span-entity pairs. ELQ (Li et al., 2020) uses a bi-encoder and predicts mention starts, ends, and token membership from question representations. Its mention representations are compared with entity vectors. The paper's one-pass description concerns the input question's BERT encoding; entity encoding and index search are separate parts of the computation. Sharing that question representation permits detection and linking to use the same contextual evidence. Evaluate total latency rather than inferring it from the number of question-encoder passes alone.

Joint mention detection does not by itself require a fixed entity inventory. The update procedure depends on the entity representation. A model with entity-specific learned parameters may need training changes, whereas a description-based model may encode new descriptions and update its index. Distinguish model retraining from index rebuilding when comparing maintenance costs.

Zero-shot entity linking evaluates entities without labeled training mentions in the specified split. Description-based matching gives the model information about a new entity without requiring a learned class weight for its ID. State what was held out: entities, domains, languages, and mention strings are different generalization tests. This use of "zero-shot" also differs from prompting an LLM without task demonstrations.

ReFinED (Ayoola et al., 2022) combines mention detection, fine-grained entity typing, and disambiguation in one document forward pass, using entity types and descriptions. Its reported efficiency and accuracy apply to the paper's benchmarks and configurations. This is an example of sharing computation across predicted mentions, not a guarantee that every target mention in a new document will be detected correctly.

ZESHEL (Logeswaran et al., 2019) evaluates linking to unseen entities in held-out domains using entity descriptions, without in-domain labeled training data or alias metadata in its task formulation. It tests a relevant kind of transfer, but does not establish performance on an arbitrary private or continually changing KB. A deployment evaluation should reproduce the intended languages, entity descriptions, mention inventory, and unknown-entity rate.

Botha et al. (2020) use a dual encoder for mentions in more than 100 languages, linking to a language-agnostic KB. This is distinct from mGENRE, the autoregressive multilingual system of De Cao et al. (2022). mGENRE predicts entity names and considers names in multiple languages, rather than fixing every target name to one canonical language. Both approaches separate the entity's identity from the language of a particular mention or name. The outline for Multilingual Language Models and Cross-Lingual Transfer lists shared representations under Cross-Lingual Transfer and Alignment.

Multilingual linking adds possible gaps in aliases, descriptions, and training supervision across languages. A cross-lingual representation may match a mention to a description in another language, but that ability must be evaluated. A missing description alone does not establish that the entity is absent from the KB: another retrieval route may find it, or the system may abstain.

An LLM can also be placed after retrieval: supply the mention, its context, and candidate IDs with descriptions, then request one ID or an abstention action. This is a system design option, not an accuracy claim. Evaluate it against a smaller reranker and the prior-only baseline on the same candidate lists, with the same mention spans and KB snapshot.

Evaluate the following potential failure modes:

• Identifier hallucination. An unconstrained output can contain an invented QID or title. Restrict the response to an allowed candidate ID or the designated abstention action. Check any returned ID against the chosen KB snapshot.
• Popularity effects. Test whether the reranker improves rare-entity cases rather than relying on the familiar candidate name.
• Prompt sensitivity. Evaluate several fixed candidate permutations and record whether the decisions change. Testing one fixed order does not test invariance to other orders.
• Cost and latency. Measure total cost at the intended mention volume, including retrieval, batching, output validation, and any fallback calls.

Validate every returned ID against the allowed list and the chosen KB snapshot. This can reject an invented identifier; it cannot detect a semantically wrong choice that is nevertheless a valid candidate. Likewise, a fixed candidate order does not establish the absence of popularity or order bias. Keep identifier validity, referential correctness, and run-to-run variability as separate checks.